VOLUME 10, ISSUE 8, AUGUST 2023

Abstract: The study "Identifying the Best Soil and Climatic Conditions for Maximum Yield of Stevia Seedlings at Kaduna Polytechnic" investigates the impact of soil type and climate on the growth of Stevia rebaudiana, a perennial herb with sweet leaves. The research aimed to determine the optimal conditions for Stevia seedling yield. The experiment involved growing Stevia plants in four different soil types: loamy, sandy, silty, and clay, under a controlled environment. Measurements of plant height, leaf count, root length, and root count were taken and analyzed using analysis of variance (ANOVA). The results showed that Stevia plants thrived best in silty soil, with no growth observed in clay soil. Optimal growth was found in loamy soil with a composition of 45% sand, 40% silt, and 15% clay, and a pH of 6-8. The study also found that Stevia growth was most robust during warm weather (February to April) with over 10 hours of sunlight, especially in well-drained sandy loam soil composed of 70% sand, 20% silt, and 10% clay. These findings provide significant insights for Stevia cultivation practices, potentially leading to improved yield and quality. The study offers valuable information for Stevia cultivation in the Kaduna Polytechnic region and other regions with similar conditions.

Keywords: Stevia Rebaudiana, Soil Conditions, Climatic Conditions, Kaduna Polytechnic

Abstract: Foreign Direct Investment is a driving factor for developing economies. There are several other factors also that significantly impact Foreign Direct Investment. This study aims to critically analyze the previous studies on factors acting as potential determinants of Foreign Direct Investment. This study proceeds with drawing a broad overview of Foreign Direct Investment determinants on the basis of relevant theories of FDI. Further, the specific determinants are identified and critically reviewed on the basis of available literature. The dynamics of these determinants have changed considerably in the context of pandemic. The study also attempts to draw conclusions regarding the effectiveness of these determinants in the current scenario. This study identifies Market Size, Openness to Trade, Labour Cost, Infrastructure and Political Risk as the most common potential determinants constructively or negatively associated with FDI.

Keywords: Foreign Direct Investment, potential determinants, pandemic, developing economies

Abstract: Phishing is a type of cybercrime in which an attacker pretends to be a real person or institution by presenting themselves as such through email or other forms of communication. In this kind of cyber assault, the perpetrator sends harmful links or attachments via phishing emails or some other websites that can accomplish a number of tasks, including stealing the victim's login credentials or bank account information. These emails cause people to lose money and have their personal identities stolen.

Keywords: Phishing techniques, Phishing Websites, Legitimate websites.

Abstract: Starlink, a revolutionary satellite internet constellation project developed by SpaceX, has significant attention for its potential to provide high-speed, low-latency internet connectivity to users around the world, even in remote and underserved regions. This research paper dig into the technological aspects of Starlink, examining its working,speed and coverage.Through a comprehensive analysis of the challenges and security. This paper explores the potential impacts of Starlink on global internet accessibility.

Keywords: Starlink, Satellite, Network

Abstract: Sand mining can have significant adverse effects on both surface and underground water resources. It can lead to surface water depletion, groundwater depletion, water quality degradation, habitat destruction, increased flood risk, erosion, and other detrimental impacts. To address these issues, various mitigation measures can be employed. Implementing and enforcing regulations, conducting thorough environmental impact assessments, adopting responsible mining practices, promoting sustainable extraction methods, controlling extraction rates, protecting critical habitats, monitoring water quality, restoring mined areas, involving stakeholders, and encouraging research and innovation are some of the key strategies for mitigating the adverse effects of sand mining on water resources. By combining these measures, it is possible to minimize the environmental impacts of sand mining and ensure the long-term sustainability of water ecosystems.

Keywords: Sand mining, Surface water, Underground water, Erosion, Environmental Impact Assessment

Abstract: As the world evolves towards becoming more environmentally conscious, more people are opting to buy electric automobiles. Users of electric cars often have a difficult time finding charging stations. The systems now in use for identifying charging stations are often outdated, unreliable, and provide only limited information on location and availability. Anxiety over the car's range and difficulties in owning an electric vehicle might be the result of lengthy charging times and ambiguity. These problems can be solved with an app that locates and books charging stations for electric vehicles.

Keywords: Electric vehicle, Green Drive, Android,

Abstract: Artificial intelligence (AI) is evolving in a way that makes the distinctions between certain fields of application fuzzier and increases its capacity to be applied in a variety of contexts. A significant step in this direction has been made with the public's availability of ChatGPT, a large languages model (LLM)-powered generative AI chatbot. Professionals expect this technology will impact education and the job of teachers as a result. Nevertheless, how instructors might really utilize the technology & the nature of its connection with teachers remain understudied, given some presumptions regarding its influence on education. In this study, ChatGPT's interaction with instructors was investigated with an emphasis on understanding the complimentary functions that each play in education. Over the course of two weeks, ChatGPT was requested to be used by eleven language instructors. They subsequently shared interaction logs created during their usage of the device and took part in individual interviews to discuss their experiences. Four ChatGPT roles-interlocutor, content provider, teaching assistant, and evaluator-and three teacher roles-orchestrating various resources with quality pedagogical decisions, empowering students as active investigators, and fostering AI ethical awareness-were identified through qualitative analysis of the data. The potential application of LLM-powered chatbots for educational purposes is also discussed, along with its implications.

Keywords: ChatGPT, Large Language Model, Chatbot, Artificial Intelligence, AIEd, Human-Computer Interaction, Large Language Models-Powered Chatbot

Abstract: The use of waste paper sludge ash in concrete formulations was investigated as an alternative to landfill disposal. In this paper represents the optimum use of the waste paper sludge ash with the concrete mixture cement has been replaced by waste paper sludge ash in the range of 0%, 5%, 10%, 15% and 20% by the weight for M-25 and M-40 grade of mix with water binder ratio various from 0.5 and 0.40. The concrete mixtures were produced and compared in terms of fresh and hardened properties with the conventional concrete. The concrete specimen was tested in test as compression test, split tensile test and flexural strength test of concrete at 7, 28, and 56 days. The water absorption, dry density test of concrete at 28 days' age of concrete and compared with the conventional concrete. It can be concluded that the optimum percentage of waste paper sludge ash as a result, the compressive, splitting tensile and flexural strength increased up to 10% by weight and particle size less than 90µm to prevent decrease in workability. Further waste paper sludge has very high calorific value and could be used as a fuel before using its ash as partial cement replacement.

Keywords: Waste Paper Sludge Ash, Compressive Strength, Split Tensile Strength, Flexural Strength and Durability Test.

Abstract: The art of knowledge of basalt fiber, it is relatively new material. Basalt fiber is a high performance non-metallic fiber made from basalt rock melted at high temperature. Basalt fiber reinforced concrete is more characteristics such as good fire resistance, light weight, brown in color and strength. In this study trial test for concrete with basalt fiber and without basalt fiber are conducted to show the difference between in compressive strength and split tensile strength by using concrete cubes of size 150mmx150mmx150mm and concrete cylinders of size 150mm diameter and 300mm height. .In this study the basalt fiber having length 12mm are used. Adding basalt fiber in concrete with different percentages of coarse aggregate (0%, 1%, 2%, and 3%) and partially replacement of cement with silica fume with different percentages (0%, 2%, 4%, and 6%).

Keywords: Concrete, Portland cement, Basalt fiber, Silica fume, Mechanical properties.

+91-7667918914 iarjset@gmail.com 0 Items International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal ISSN Online 2393-8021 ISSN Print 2394-1588 Since 2014 Home About About IARJSET Aims and Scope Editorial Board Editorial Policies Publication Ethics Publication Policies Indexing and Abstracting Citation Index License Information Authors How can I publish my paper? Instructions to Authors Benefits to Authors Why Publish in IARJSET Call for Papers Check my Paper status Publication Fee Details Publication Fee Mode FAQs Author Testimonials Reviewers Topics Peer Review Current Issue & Archives Indexing FAQ’s Contact Select Page The role of gamification and incentives in improving crowd worker engagement and performance: A systematic review Sagar Mal Nitharwal Abstract Objective: This systematic review aims to examine the role of gamification and incentives in improving crowd worker engagement and performance in crowdsourcing tasks. This provides a clear focus for the review and sets out the research question the review aims to answer. Methods: This review synthesizes the existing literature on the topic, drawing on studies from various disciplines such as computer science, psychology, and management. This methodological approach allows the review to draw on a broad range of research and to provide a comprehensive overview of the topic. By adopting a systematic approach to the review, the authors can ensure that their findings are robust and reliable. Findings: The review explores the various gamification and incentive strategies that are crowdsourcing tasks and their impact on worker engagement and performance. This subtopic provides a clear overview of the review's main findings and provides evidence for using gamification and incentives in crowdsourcing. Discussing the factors influencing these strategies' effectiveness highlights the issue's complexity and the need for a nuanced approach to gamification and incentives in crowdsourcing. Novelty: This review provides a comprehensive overview of the literature on the topic and identifies gaps in the literature. By identifying gaps in the literature, the review highlights areas where further research is needed. The review also highlights potential avenues for future research on the topic, which could help to advance our understanding of the role of gamification and incentives in improving crowd worker engagement and performance in crowdsourcing tasks.

Keywords: Crowdsourcing, Gamification, Incentives, Worker engagement, Performance Downloads: | DOI: 10.17148/IARJSET.2023.10810 How to Cite: [1] Sagar Mal Nitharwal, "The role of gamification and incentives in improving crowd worker engagement and performance: A systematic review," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2023.10810 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form   Submit to iarjset@gmail.com or editor@iarjset.com   Submit My Paper Author CenterHow can I publish my paper? Publication Fee Why Publish in IARJSET Benefits to Authors Guidelines to Authors FAQs (Frequently Asked Questions) Author Testimonials IARJSET ManagementAims and Scope Call for Papers Editorial Board DOI and Crossref Publication Ethics Editorial Policies Publication Policies Subscription / Librarian Conference Special Issue Info ArchivesCurrent Issue & Archives Conference Special Issue Copyright © 2026 IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License. Open chat

Abstract: In this paper, the fiber Bragg grating (FBG) is optimized for sensor applications. The size of the FBG sensor is very important for structural health monitoring applications. Therefore, the core diameter and grating length of FBG are selected for the optimization. The FBG response includes three important characteristics like reflectivity, full width at half maximum, and side lobe power. For sensor applications, reflectivity should be high and FWHM and side lobe power should be low. A GratingMOD tool from Synopsis is used for the simulation study of FBG. This tool uses coupled mode theory and a transfer matrix method for the characterization of FBG. The core diameter and grating length are optimized to 5 μm and 2.5 mm respectively. The optimized FBG offers reflectivity, 0.9945 a.u., full width at half maximum, 0.848 nm, and side lobe power, 0.3393 a.u.

Keywords: Fiber Bragg grating; Core diameter; Grating length; Reflectivity; Full width at half maximum; Side lobe power

Abstract: Dessert is the last course of a meal and this study aimed to develop Elos desserts and determine their acceptability. The dessert products formulated were Butchi, Pastillas, and Bibingka and were evaluated as to sensory qualities in terms of appearance, aroma, taste, and texture. The level of their acceptability was also tested using the same qualities. Significant differences in the treatments were determined for both the sensory qualities and acceptability. The study was a developmental-experimental research that used the Completely Randomized Design (CRD) with two (2) treatments in three replications. The sensory qualities of the desserts were evaluated by 10 semi-trained panellists who were food technology teachers at Capiz State University while the final product was evaluated by 100 consumers using the Nine Point Hedonic Scale. The statistical tools used were the mean, and the Analysis of Variance One Way (ANOVA) which was set at 0.01 alpha level. The findings of the study revealed that both treatments (Treatment A with 25 grams of Elos and Treatment B 50 grams of Elos) of Elos Butchi were extremely appealing in appearance, extremely pleasant in aroma, extremely delicious in taste and extremely crispy and chewy in texture with Treatment A showing the same mean in Treatment B. For the Elos Pastillas, both treatments were extremely appealing, extremely pleasant, extremely delicious and extremely soft and creamy. The Elos Bibingka were extremely appealing, extremely pleasant, extremely delicious and extremely soft and spongy in both treatments. The three (3) Elos desserts were all liked extremely in their acceptability with product B (Pastillas) getting the highest result in all quality attributes. There were no significant differences in the sensory qualities of the Elos in making desserts considering the two treatments in terms of appearance, aroma, taste, and texture. There was no significant difference in the consumers' acceptability of Elos desserts considering the sensory qualities. Elos Buchi and Elos Pastillas can stay up to 3days at room temperature and seven days at chilling temperature. Elos Bibingka can stay for two (2) days at room temperature and five (5) days at chilling temperature. The elos pastillas was safe for human consumption based on the results of microbial analysis of the product and based on the BFAD standard for microorganism test for products belonging to sugar confectionaries.

Keywords: Elos (Lesser Yam), Dessert, Development and Acceptability

Abstract: The present work in the detection of a disorder known as Alzheimer's with the assist of Image processing. This look at concentrates on detection of Alzheimer's disorder early and offering suitable treatment to the patients. Alzheimer's disease (AD) is a modern neurological sickness characterized via cognitive decline and memory impairment. Early detection of AD is crucial for effective remedy and intervention. In latest years, image processing strategies have emerged as a promising tool for the detection and diagnosis of AD. This research paintings focuses on the software of image processing algorithms to pick out ability biomarkers and styles indicative of AD from mind imaging data. The study utilizes a dataset inclusive of magnetic resonance imaging (MRI) scans received from AD sufferers and wholesome controls. Various photograph processing techniques, such as feature extraction, image segmentation, and classification algorithms, are hired to research mind pictures and differentiate between AD and non-AD instances. The extracted records are used to train system getting to know models for correct classification. The results reveal the effectiveness of image processing in detecting AD with excessive accuracy. The proposed method has the capability to help clinicians in early diagnosis and monitoring of AD, main to timely interventions and improved affected person care. This research contributes to the ongoing efforts in developing non-invasive, dependable, and reachable techniques for AD detection the use of image processing techniques.

Keywords: Alzheimer's ailment, Image processing, Biomarkers, MRI scans, Early detection.

Abstract: Chilli is one of the most important commercial spice crops of India . There are various uses of chilli, it is used as spice, condiment, culinary supplement, medicine, vegetable and ornamental plant. Chilli is an essential spice because of its pungency, taste, appealing colour and flavor. Bird's eye chilli are small, thin, pointy peppers that are red when mature. India produces about 75 of the 109 varieties which are listed by the International Organization for Standardization (ISO). The most produced and exported spices from India are pepper, cardamom, chilli, ginger, turmeric, coriander, cumin, celery, fennel, fenugreek, garlic, nutmeg & mace, curry powder, spice oils and oleoresins. Out of these spices, chilli, cumin, turmeric, ginger and coriander makeup about 76% of the total production. In the present study, the researcher interviewed the farmers from Champhai, Reiek and West Phaileng in Mizoram growing bird's eye chilli. Bird's eye chilli is a promising spice of Mizoram and has the potential to be widely exported globally. However, marketing practices are still conventional and hence need support from government agencies to enhance markets beyond local and regional level.

Keywords: bird's eye chilli, export, production, marketing practices,Mizoram.

Abstract: Cloud computing has emerged as a transformative technology paradigm that is reshaping the way businesses and individuals' access, manage, and utilize computing resources. This paper presents a comprehensive exploration of cloud computing's multifaceted landscape, delving into its underlying concepts, deployment models, service models, benefits, challenges, and future directions. Through an extensive review of existing literature, case studies, and empirical data, this research highlights the profound impact of cloud computing on various industries, such as IT, healthcare, finance, and education. Furthermore, it examines the challenges posed by security, privacy, data governance, and vendor lock-in, emphasizing the need for robust solutions and best practices. The paper also addresses the evolving role of cloud providers and their continuous efforts to offer innovative services, including serverless computing, edge computing, and AI-powered analytics. It delves into the intricacies of cloud adoption strategies for organizations, considering factors such as scalability, cost-effectiveness, and regulatory compliance. By analysing real-world implementations and success stories, this research underscores the tangible benefits of cloud computing, such as enhanced agility, resource optimization, and global accessibility.

Keywords: Cloud Computing, Deployment Models, Service Models, Security, Privacy, Innovation, Cloud Adoption, Future Directions.

Abstract: This research study explores the complex realm of consumer perception and attitudes towards online shopping, a trend that is transforming the retail industry worldwide. As E-commerce platforms continue to thrive, it is crucial for businesses to grasp how consumers perceive and interact with online shopping. Despite numerous visits to online shops, many potential customers hesitate to make a purchase. To convert these prospects into actual customers, marketers must examine the factors that influence their decision to shop online and their overall attitude towards online shopping. This research stands out by specifically investigating consumers' perceptions and expectations when it comes to online shopping

Keywords: Consumer perception, Attitudes, Online shopping.

Abstract: Education is becoming more teacher centric with emphases on 'Value Addition' by teachers. Teachers stand out as the key variable in realizing the complex standards rolled out in education systems. Out of many multiple sources, quality of teachers are of a vital importance in student's outcomes and achievements.

Keywords: Teachers, quality, student performances, correlation, school.

Abstract: Structural frameworks constructed on hill slopes exhibit distinct structural characteristics compared to those on flat terrain. Due to their inherent asymmetry, these buildings experience significant shear forces and torsional moments. Additionally, the distribution of these forces is uneven due to variations in column lengths. This research involves the modelling and analysis of two different configurations of hillside buildings using the ETABS software. A parametric investigation was conducted, varying the incline of the hill slope (20° and 25°). The analytical models were subjected to seismic forces employing the Response Spectrum Method. The resulting dynamic parameters were examined, including fundamental natural periods, maximum storey displacements, storey drifts, and storey shear forces. A comparative analysis was performed between the two considered hillside building configurations- Stepback and Stepback-Setback. Ultimately, based on the findings, recommendations are made regarding the suitability of various hillside building configurations. The buildings in this study are equipped with shear wall, bracing and a combination of both and their overall performance is thoroughly assessed.

Keywords: sloped terrain, step-back configuration, storey shear, time period, top storey displacement.

Abstract: The rabid growth in electrical power consumption and decrease in generating plants and transmission capacities, present the importance of using Distributed Generation (DG) sources. DG is known as small scale generation unit which is installed in the distribution system and connected at substations or feeders. DG is electrical generation and storage performed by a variety of small grid, and refers to various technologies which generate electrical power at or near the consumers. It may be a single structure like a home or a part of micro-grid. DG can support delivery of reliable power to additional customers. This paper discusses the effect of DG in distribution net-works reliability. Port-Sudan distribution net-work (DNW) is selected as case study. The results show that addition of two generating unit at Klayneep with rating of 15MW for each improves the voltage profile and supply continuity of Port- Sudan DNW. Simulation is done using ETAP software.

Keywords: distributed generation, Klayneep, distribution net-work, reliability

Abstract: Nuclear energy is generated by the process of molecular fission, molecular fusion and molecular decay. Uncontrolled formation of such energy leads to nuclear disasters. These disasters significantly affect the population and its effects are observed for years. Radioactivity leads to cancer, genetic disorders and death in the affected area for decades. A single nuclear accident can cause loss of life, long term illness and destruction of property in a large scale. Most of the commercial nuclear power plants release gaseous and liquid radiological effluents into the environment as a by product of the elements. The planet at present where are living have witnessed three major nuclear accidents have changed the environmental conditions of both biotic and abiotic. The first incidence occurred at Three Mile Island in 1970. The second accident took place at Chernobyl located in Ukraine in the year 1986.The finally, the Fukushima accident occurred in Japan due to tsunami in the year 2011 resulting in hydrogen gas explosions and partial meltdowns. The total amount of radioactivity released through this method depends on the nuclear power plants. The nuclear energy is the direct cause of global warming and climate change in many ways. The heat released by the nuclear reactors. Once the energy is released from uranium , the fuels of the nuclear reactor then it radiates out in to the outer space as long wave radiation and the rest goes into the air, waterways , glaciers which intern increases the atmospheric temperature, thus it leads to the reasons for the Global warming. There are 400 nuclear plants all over the world to generate electric power all of them are generating considerable amount of nuclear wastes which increases the earth's Antarctica glaciers. Nuclear power is unreliable for fighting global warming. Scandals, natural disasters and accidents can shut down numerous plants simultaneously. When one of these problems occurs, without sustainable alternative energy sources, fossil fuel plants must kick in which spikes greenhouse gas emissions. For centralized, large systems like nuclear generation, utilities must install a "reserve margin" of extra capacity ready for instant use. For example, in Japan every new nuclear power plant requires additional fossil-fuel-fired capacity. Nuclear power plants and fossil fuel plants come in tandem. If the number of nuclear power plants could be doubled, which is impossible, their total contribution to world energy use would only increase to 12%. Thus, it is untrue to say that nuclear energy is greenhouse friendly.

Keywords: Radioactive, greenhouse friendly, Uranium, CFC, Fukushima, Inhalation exposure, Ground-level external exposure and Atmospheric external exposure., etc.

Abstract: Due to urbanisation and space occupancy issues, soft storey architecture is a common characteristic in high rise buildings or multi-story buildings. Due to their soft storeys, these measures cause the lateral load resisting system's stiffness to decrease, making a progressive collapse in a powerful earthquake inevitable for such structures. Damage and collapse are frequently seen in soft story buildings during earthquakes because this storey level contains concrete columns that were unable to offer appropriate shear resistance. The current study is primarily concerned with examining how a soft storey affects a structure's behaviour. The current work uses dynamic analysis to examine several positions for a soft storey within a multi-story building. In a multi-story building with lateral loads from an earthquake, the building's performance is assessed at various levels by taking into account the bare frame, bracings, shear walls, and composite column. The response spectrum analysis techniques described in the code practice are used to evaluate the lateral load analysis caused by seismic action. ETABS software is used to carry out these structural analyses. To evaluate the performance of the soft storey in the multi-story building, ETABS software is used to track variables such storey displacement, storey drift, storey shear, and time period. The study is carried out by considering a G+15 RC multistoried building. Ultimately, based on the findings, recommendations are made regarding the suitability of various configuration.

Keywords: ETABS, storey shear, time period, storey displacement, soft storey.

Abstract: This paper compares an optimized shear wall layout obtained from an existing research paper and a conventional model. The objective of the study is to evaluate the structural efficiency of both designs while considering constraints related to drift and displacement. Shear walls are important components that provide lateral stability to buildings, particularly in regions susceptible to seismic activities. The optimized shear wall layout, sourced from a relevant peer-reviewed paper, serves as the basis for comparison. The conventional model adheres to established design practices and code requirements. The study evaluates the response of the structures under various load combinations, including seismic and wind forces. Through rigorous comparative analyses, the study reveals the relative advantages and limitations of the optimized shear wall layout compared to the conventional design in terms of structural performance and efficiency. The findings highlight the significance of considering drift and displacement constraints to ensure the safety and resilience of buildings in seismic-prone regions. The implications of this study offer valuable insights for the architectural and engineering community, showcasing the potential for enhanced structural performance through optimization techniques. The paper contributes to a deeper understanding of the benefits of incorporating optimized shear wall layouts in structural design, ultimately promoting more sustainable and safer construction practices in the future.

Keywords: RC Shear wall, Optimization, story drift, displacement

Abstract: In the construction of contemporary buildings, it's essential to incorporate numerous pipes and ducts to facilitate vital services like water supply, sewage, air-conditioning, electricity, telecommunications, and computer networking. Typically, these conduits are positioned beneath the beam's underside. The dimensions of these pipes or ducts can vary, ranging from a few centimetres to up to half a meter. The introduction of such openings inevitably results in reduced structural stiffness, increased cracking, excessive deflection, and diminished beam strength. To address these challenges, the use of Carbon Fibre Reinforced Polymer (CFRP) sheets for retrofitting concrete structures has emerged as an economically viable and technologically advanced alternative to traditional methods. This approach offers several advantages, including impressive strength-to-weight ratio, resistance to corrosion, excellent fatigue resistance, straightforward and efficient installation, and minimal disruption to the existing structural layout. The focus of this research paper is to examine the behaviour of a Reinforced Concrete (RCC) beam featuring a Rounded Rectangular opening located within the shear zone. The study aims to evaluate the effectiveness of three distinct CFRP reinforcement techniques. The analysis is performed using ANSYS software. The study involves five beams: one acting as a control beam without an opening in the shear zone, with opening in shear zone the other three strengthened using different CFRP techniques-applying CFRP inside the opening, around the opening, and both inside and around the opening. The results include deflection values corresponding to varying load levels, allowing for comparison among different loading scenarios. Furthermore, the study analyses the crack patterns associated with the various CFRP techniques employed. Ultimately, among the approaches investigated, it becomes evident that the technique involving CFRP reinforcement both around and inside the opening proves to be the most effective. This method substantially enhances the beam's load-carrying capacity, surpassing that of the control beam by approximately threefold.

Keywords: Reinforced Concrete Beam, strengthening of beam, finite element analysis, application of ANSYS software, graphical representation of load versus deflection relationships, CFRP, Rounded rectangular Opening,

+91-7667918914 iarjset@gmail.com 0 Items International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal ISSN Online 2393-8021 ISSN Print 2394-1588 Since 2014 Home About About IARJSET Aims and Scope Editorial Board Editorial Policies Publication Ethics Publication Policies Indexing and Abstracting Citation Index License Information Authors How can I publish my paper? Instructions to Authors Benefits to Authors Why Publish in IARJSET Call for Papers Check my Paper status Publication Fee Details Publication Fee Mode FAQs Author Testimonials Reviewers Topics Peer Review Current Issue & Archives Indexing FAQ’s Contact Select Page Assessment of Load Carrying Capacity of RC Girder Bridge Nikhilgouda S Kulkarni, Meghashree.M offering unique insights into various bridge structures and their behaviours under diverse conditions. These papers explore topics ranging from T-beam girder bridge evaluations to capacity assessments of aging T-beam bridges, dynamic responses of high-speed vehicles on bridges, and finite element analyses of deteriorated T-girder bridges under cyclic loading. Lastly, the paper on finite element failure analysis highlights the importance of accurately modelling support conditions in CSi Bridge software. Together, these studies significantly contribute to bridge engineering knowledge, benefiting designers, engineers, and researchers dedicated to enhancing the safety and integrity of diverse bridge types in various operational scenarios.

Keywords: FEM Analysis, Dynamic loading, Vehicular loading, CSi Bridge, T-girder Bridges. Downloads: | DOI: 10.17148/IARJSET.2023.10824 How to Cite: [1] Nikhilgouda S Kulkarni, Meghashree.M, "Assessment of Load Carrying Capacity of RC Girder Bridge," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2023.10824 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form   Submit to iarjset@gmail.com or editor@iarjset.com   Submit My Paper Author CenterHow can I publish my paper? Publication Fee Why Publish in IARJSET Benefits to Authors Guidelines to Authors FAQs (Frequently Asked Questions) Author Testimonials IARJSET ManagementAims and Scope Call for Papers Editorial Board DOI and Crossref Publication Ethics Editorial Policies Publication Policies Subscription / Librarian Conference Special Issue Info ArchivesCurrent Issue & Archives Conference Special Issue Copyright © 2026 IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License. Open chat

Abstract: A Zebra Crossing is used by pedestrians to cross a road safely, LEDs are used as traffic lights, LCD displays are used as a timer to the pedestrians as well as the vehicles meanwhile barriers are used to stop people for carelessly crossing the road. The project titled "Design and Implementation of Smart Zebra Crossing using Arduino" is a demonstration of all these devices incorporated into one model which is based on IoT technology. It uses the Arduino Uno R3 used as a microcontroller, 16x2 LCD display as a timer, an I2C module for easy connections, jumper wires for solderless connections, green and red LEDs for traffic light display, and servo motors along with barriers to enhance pedestrian safety and facilitate efficient traffic management.

Keywords: Zebra crossing, Barrier system, project report, IOT based project, engineering project.

Abstract: The evaluation process carried by faculties in Physics labs face numerous challenges. The traditional approach relies on manual evaluation of the experimental results and respective calculations carried by the student. This manual method, not only consumes considerable time and effort if done thoroughly but also introduces the potential for human errors, impacting the correctness of evaluations. Faculty members had the arduous task of manually reviewing each student's records, making the assessment process time-consuming and potentially prone to subjectivity. Because of this reason, most of the times faculties just check whether the final calculated result is within the expected range or not and simply glance over the intermediate steps. Also faculties wrongly attribute the deviations in the calculated value from standard values to random error instead to lack of experimental skill of the student. It is because they don't standardize the samples they use in the experiment. In some of the cases students simply doesn't calculate at all. They just copy the calculations from their friends or simply write a number in the expected answer range. Many times if faculties calculate for the data presented by the students, the answers don't match! Present article suggests an idea for creation of an advanced web portal to address these significant issues and modernize the evaluation method. Here the lab experiments are evaluated by utilizing the Flask, SQLite database for back-end and HTML, CSS, JavaScript for front-end. Students will input their observations in a more streamlined and error-free manner and faculties will be able to see the right calculations for the entered data so that they can use it to verify the results presented by the students. The correctness of the evaluation process will be guaranteed.

Keywords: Digital system ,evaluation,database, Flask

Abstract: This study investigates the strength performance of a geopolymer mortar made from red mud and combined with industrial waste. In the creation of geopolymer mortar, red mud, a result of the aluminium refining process, is mixed with various industrial waste products as a precursor. The investigation's main goal is to evaluate the compressive strength of various formulations and additives in order to get knowledge about how effective they are as strengthening agents. Strength development and material structure are related by microstructural investigation, which uses SEM and XRD methods. The comparative nature of the study reveals the best mixtures of red mud and industrial waste for raising the compressive strength of geopolymer mortar. These results highlight the potential for eco-friendly geopolymer materials to replace traditional cement-based systems.

Keywords: Geopolymer, Red mud, Industrial waste, Compressive strength

Abstract: It is a sad reality that many people across the world are unable to access food, while countless meals are wasted every day. India, in particular, has reached a high level of economic status where a significant amount of edible food is thrown away as waste. This wastage of food is not only bothersome but also evident in garbage bins, landfills, and streets. Canteens, restaurants, weddings, and family gatherings contribute to the problem by wasting a lot of food. Furthermore, food waste not only leads to hunger and pollution but also highlights several economic issues. The rapid changes in lifestyle and high standards of living have resulted in the wastage of food, clothing, and other resources.

Abstract: This collection of research endeavors encompasses a broad spectrum of studies focused on bridge analysis and design, providing distinctive insights into diverse bridge structures and their behaviors across varying conditions. The papers delve into a wide range of topics, from evaluations of T-beam girder bridges to assessments of aging T-beam bridges' capacities, investigations into dynamic responses of high-speed vehicles on bridges, and finite element analyses of deteriorated T-girder bridges subjected to cyclic loading. Additionally, the paper on finite element failure analysis underscores the vital significance of precisely modeling support conditions within the CSi Bridge software. Collectively, these studies make substantial contributions to the realm of bridge engineering knowledge, offering valuable insights for designers, engineers, and researchers committed to enhancing the safety and integrity of diverse bridge types across a multitude of operational scenarios.

Keywords: Bridge design, Bridge analysis, , CSi Bridge, T-girder Bridges.

Abstract: The requirement for health information is altering knowledge-seeking behaviour, which should be noted globally. Many of us struggle with finding health information online about illnesses, diagnoses, and various treatments. It will save a lot of time if a suggestion system is frequently used for physicians and medications. Because the users of a system like this are laypeople, the user has several difficulties understanding the basic medical terminology. The customer is perplexed since there is an excessive amount of medical information available in various formats.

Keywords: Random Forest Algorithm, Naive Bayes, Support Vector Machine, Logistic regression and etc.

Abstract: Creative thinking is the ability to come up with unique, original solutions. Also known as creative problem-solving, creative thinking is a valuable and marketable soft skill in a variety of careers. The purpose of this study was to assess critical and creative thinking among English and other undergraduate students. A total of 100 English and 100 Other than English students were selected from various affiliated colleges of Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. The interview schedule use to measure the Critical and creative thinking of the students. The findings of the study revealed that significant differences were found in critical and creative thinking between English and other students. It was found that English students had better critical and creative thinking than their counterparts.

Keywords: Critical, creative, Students, English

Abstract: PLA-Epoxy and PLA-Epoxy-Alumina reinforced composites are employed in various mechanically demanding applications. Polylactic acid (PLA) with epoxy resin form PLA-Epoxy composite. The matrix material epoxy resin gives the composite mechanical characteristics. However, PLA reinforces the composite's strength and rigidity. Production of a PLA-Epoxy-Alumina reinforced composite involves adding alumina particles to the matrix. Alumina particles reinforce the composite's mechanical strength and stiffness. A PLA-Epoxy-Alumina reinforced composite has better mechanical strength. Adding alumina particles to the composite increases its strength and stiffness, reducing stress-induced deformation and failure. Testing a composite material's tensile, compressive, and flexural strengths to determine its mechanical strength is routine. Alumina particles improve PLA-epoxy composite tensile, compressive, and flexural strength. However, many other factors affect composite material mechanical strength. The size, shape, and distribution of reinforcing particles, matrix material quality, and composite manufacturing technology all matter. As noted before, the above metrics are essential when comparing composite material mechanical strength.

Keywords: Composite materials, Mechanical strength, PLA-Epoxy composite, PLA-Epoxy-Alumina composite, Reinforced composites, Alumina particles, Matrix material.

Abstract: Malpractice has always been a serious challenge that results in problems for society. The increasing use of technology has led to an increase in counterfeit currency, which negatively impacts a country's economic growth. Therefore, it is crucial to have reliable and consistent note detection. The process of identifying paper currency involves several steps such as edge detection, feature extraction, image segmentation, grayscale conversion, and image comparison. This paper includes a literature survey that presents different methodologies for detection. The review concludes that applying efficient preprocessing and feature extraction techniques improves the algorithm and the detection system. Machine Learning techniques help in building tools that are necessary for research work, allowing us to develop computer learning design, implementation, and methods to differentiate between fake and genuine currency. The utilization of pattern recognition and image processing learning and analyzing methods helps to identify distinguishing features.

+91-7667918914 iarjset@gmail.com 0 Items International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal ISSN Online 2393-8021 ISSN Print 2394-1588 Since 2014 Home About About IARJSET Aims and Scope Editorial Board Editorial Policies Publication Ethics Publication Policies Indexing and Abstracting Citation Index License Information Authors How can I publish my paper? Instructions to Authors Benefits to Authors Why Publish in IARJSET Call for Papers Check my Paper status Publication Fee Details Publication Fee Mode FAQs Author Testimonials Reviewers Topics Peer Review Current Issue & Archives Indexing FAQ’s Contact Select Page AN ADAPTIVE SOCIAL SPAMMER DETECTION MODEL WITH SEMI-SUPERVISED BROAD LEARNING Apoorva.R, Hemanth Kumar B.N Abstract-This approach is highly effective in creating predictions, especially when compared to certain traditional forms of supervised learning. When using ASSD, identifying individuals within a group becomes simpler and requires less effort. To update the spammer detection model without requiring retraining of the model's users, incremental learning is used as a technique because social scammers often modify their behavior to deceive the spammer detection model. When benchmarked against other controlled and semi-supervised machine learning algorithms, the Social Honeypot Dataset is used to compare ASSD's performance. The study's findings suggest that the proposed model outperforms baseline approaches in terms of memory capacity and accuracy. Additionally, ASSD maintains its high accuracy in identifying spammers by continuously updating its model with newly collected data from social media. Downloads: | DOI: 10.17148/IARJSET.2023.10834 How to Cite: [1] Apoorva.R, Hemanth Kumar B.N, "AN ADAPTIVE SOCIAL SPAMMER DETECTION MODEL WITH SEMI-SUPERVISED BROAD LEARNING," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2023.10834 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form   Submit to iarjset@gmail.com or editor@iarjset.com   Submit My Paper Author CenterHow can I publish my paper? Publication Fee Why Publish in IARJSET Benefits to Authors Guidelines to Authors FAQs (Frequently Asked Questions) Author Testimonials IARJSET ManagementAims and Scope Call for Papers Editorial Board DOI and Crossref Publication Ethics Editorial Policies Publication Policies Subscription / Librarian Conference Special Issue Info ArchivesCurrent Issue & Archives Conference Special Issue Copyright © 2026 IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License. Open chat

Abstract: Our deep visual Odometry (VO) approach prioritizes memory and improved postures while taking into account global information. Unlike existing learning-based approaches which treat VO as a simple tracking issue, we reconstruct camera postures from picture fragments, which leads to a high accumulation of errors. To correct past mistakes, accurate worldwide data is essential. However, it might be difficult for end-to-end systems to reliably store such data. Therefore, we have developed an adaptive memory module that preserves information gradually and adaptively from a local to a global level in a neural memory analogue. This approach has been further enhanced using a refining module that takes advantage of global information stored in memory. To pick features for each view based on the co-visibility in the feature domain, we use past outputs as a guide and apply a spatial-temporal attention. Our system is more advanced than simple tracking since it has a memory module and a refinement module. Our experiments on the KITTI and TUM-RGBD datasets demonstrate that our technique not only delivers competitive results compared to conventional approaches in normal settings but also significantly outperforms state-of-the-art methods. Moreover, our model performs very well in instances where traditional algorithms struggle, such as texture-less areas and sudden movements.

Abstract: When working with limited training data, training a deep neural network without causing overfitting can be a challenge. To address this issue, a new data augmentation network called the Adversarial Data Augmentation Network (ADAN) has been proposed in this article. The ADAN is based on Generative Adversarial Networks (GANs) and consists of a GAN, an autoencoder, and an auxiliary classifier. These networks are trained adversarially to synthesize class-dependent feature vectors in both the latent space and the original feature space, which can then be used to augment the real training data for training classifiers. Instead of using the conventional cross-entropy loss for adversarial training, the Wasserstein divergence is used to produce high-quality synthetic samples.The proposed networks were applied to speech emotion recognition using EmoDB and IEMOCAP as the evaluation datasets. By making the synthetic latent vectors and the real latent vectors share a common representation, the gradient vanishing problem can be largely alleviated. Results show that the augmented data generated by the proposed networks are rich in emotional information

+91-7667918914 iarjset@gmail.com 0 Items International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal ISSN Online 2393-8021 ISSN Print 2394-1588 Since 2014 Home About About IARJSET Aims and Scope Editorial Board Editorial Policies Publication Ethics Publication Policies Indexing and Abstracting Citation Index License Information Authors How can I publish my paper? Instructions to Authors Benefits to Authors Why Publish in IARJSET Call for Papers Check my Paper status Publication Fee Details Publication Fee Mode FAQs Author Testimonials Reviewers Topics Peer Review Current Issue & Archives Indexing FAQ’s Contact Select Page BRAIN TUMOR CLASSIFICATION USING CONVOLUTION NEURAL NETWORK (DEEP LEARNING) Sahana.BM, Chaithra. UC Abstract- Astrocytoma is the most common and serious disease with a high grade and short life expectancy. Therefore, planning effective therapy is crucial to enhance patients' quality of life. Malignancies in different organs, such as the brain, lung, liver, chest, and libido, are usually diagnosed using image procedures like computed tomography (CT), magnetic resonance imaging (MRI), and computerized tomography. Among these techniques, MRI is considered superior in diagnosing brain tumors. However, the identification of tumors by humans in a specific time period is difficult due to the enormous amount of data generated by an MRI scan. Moreover, MRI has limitations as quantitative data is not commonly available for all images. Downloads: | DOI: 10.17148/IARJSET.2023.10837 How to Cite: [1] Sahana.BM, Chaithra. UC, "BRAIN TUMOR CLASSIFICATION USING CONVOLUTION NEURAL NETWORK (DEEP LEARNING)," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2023.10837 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form   Submit to iarjset@gmail.com or editor@iarjset.com   Submit My Paper Author CenterHow can I publish my paper? Publication Fee Why Publish in IARJSET Benefits to Authors Guidelines to Authors FAQs (Frequently Asked Questions) Author Testimonials IARJSET ManagementAims and Scope Call for Papers Editorial Board DOI and Crossref Publication Ethics Editorial Policies Publication Policies Subscription / Librarian Conference Special Issue Info ArchivesCurrent Issue & Archives Conference Special Issue Copyright © 2026 IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License. Open chat

Abstract: The issue of finance fraud is on the rise and poses a significant threat to the financial industry. While various techniques have been developed to tackle this problem, data analysis is proving to be a particularly effective approach. By analyzing vast amounts of complex data from finance databases, data analysis can help automate the process of detecting fraudulent activities. This approach has already been successfully employed in detecting credit card fraud during online transactions. However, credit card fraud detection is a challenging problem due to two main factors: the profiles of normal and fraudulent behaviors change frequently, and credit card fraud datasets are highly skewed. To address this challenge, this project proposes investigating and evaluating the performance of various algorithms on highly skewed credit card fraud data. The dataset used in this project contains 284,786 transactions from European cardholders. The algorithms will be applied to both raw and preprocessed data, and their performance will be evaluated based on accuracy, sensitivity, specificity, and precision.

+91-7667918914 iarjset@gmail.com 0 Items International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal ISSN Online 2393-8021 ISSN Print 2394-1588 Since 2014 Home About About IARJSET Aims and Scope Editorial Board Editorial Policies Publication Ethics Publication Policies Indexing and Abstracting Citation Index License Information Authors How can I publish my paper? Instructions to Authors Benefits to Authors Why Publish in IARJSET Call for Papers Check my Paper status Publication Fee Details Publication Fee Mode FAQs Author Testimonials Reviewers Topics Peer Review Current Issue & Archives Indexing FAQ’s Contact Select Page An AI- based System for Bird and Drone Detection using YOLOv4/v5 Object Detection Models Suhana Noorain, Prof. Hemanth Kumar B N Abstract- For this project, we recommend using the YOLOv4 and YOLOv5 models to create a system that can accurately detect and classify drones and birds. The use of drones is increasingly threatening to bird populations, and it is crucial to develop a solution that can identify them. To do this, we will train the YOLOv4 and YOLOv5 models on the training set, using transfer learning. After that, we will assess their performance on the validation set and test their accuracy on a separate test set. Finally, we will compare the performance of the two models and select the best one for bird and drone detection. Downloads: | DOI: 10.17148/IARJSET.2023.10839 How to Cite: [1] Suhana Noorain, Prof. Hemanth Kumar B N, "An AI- based System for Bird and Drone Detection using YOLOv4/v5 Object Detection Models," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2023.10839 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form   Submit to iarjset@gmail.com or editor@iarjset.com   Submit My Paper Author CenterHow can I publish my paper? Publication Fee Why Publish in IARJSET Benefits to Authors Guidelines to Authors FAQs (Frequently Asked Questions) Author Testimonials IARJSET ManagementAims and Scope Call for Papers Editorial Board DOI and Crossref Publication Ethics Editorial Policies Publication Policies Subscription / Librarian Conference Special Issue Info ArchivesCurrent Issue & Archives Conference Special Issue Copyright © 2026 IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License. Open chat

Abstract: The recent COVID-19 pandemic has highlighted the necessity of using facemasks as a preventive tool against infectious illnesses. There has been a lot of interest in developing automated systems for real-time facemask detection to monitor compliance with facemask use. This work presents a visual method for identifying masks worn by individuals in live-action footage. The proposed system utilizes computer vision methods to analyze live footage captured by a camera or online cam. Firstly, face identification algorithms are used to locate human faces in the video footage. Next, a deep learning-based classifier is used to determine whether the recognized face is covered by a mask.To train the facemask detection algorithm, a large dataset of annotated photos of people wearing and not wearing facemasks is used. Transfer learning strategies are employed to perform accurate and efficient facemask categorization by leveraging pre-trained convolution neural networks (CNNs). The trained model is subsequently integrated into the pipeline used to analyze videos in real-time, enabling instantaneous facemask detection.

Abstract: In order to predict the sales of their goods and services, organisations must analyse the daily sales data. By using this forecasting, manufacturers can raise product output to keep up with demand or make necessary adjustments to boost sales. With the help of the data science techniques Nave Bayesian classifier and KNN Classifier, this research introduces a fresh approach to sales forecasting. In order to demonstrate the effectiveness of the suggested mechanism, experiments are conducted utilising sales data from prior years gathered from numerous stores situated in various cities. The best algorithm will be determined by comparing the two .We used datasets from both feature phones and smart phones in the suggested method.

Keywords: Product, forecasting, demand, classifier, ML techniques

Abstract: A number of factors, such as human intervention, environmental change, an increase in Earth's average temperature, forest fires or deforestation, etc., are causing the bird population to fluctuate significantly nowadays. Currently, it is possible to keep an eye on the population of birds as well as their behavior with the aid of programmed bird species discovery using AI calculations. This work develops a programmed bird ID framework that eliminates the need for actual mediation because manually identifying diverse bird species takes a lot of time and effort. When compared to commonly used classifiers like SVM, Irregular Backwoods, and SMACPY, Convolutional Brain Organization is used to achieve this purpose. Utilizing the dataset that includes different bird vocalizations, the main goal is to identify the different bird species. A spectrogram will then be generated and sent off to a convolutional brain network as an information, followed by CNN change, testing, and order. The information dataset will first be pre- handled, which will involve outline, quietness expulsion, and reproduction. Birds are arranged according to their highlights, such as size, variety, species, and others, and the results are contrasted with previously prepared data. It is presently crucial to screen the outcomes of human movement on the climate before it brings about the climate experiencing hopeless damage. Checking creature rearing way of behaving, biodiversity, and populace elements is one method for monitoring these results. It is becoming more and more crucial to monitor how human activity affects the ecosystem in order to keep the environment from suffering irreparable harm. One method of keeping tabs on these consequences is to observe animal reproduction patterns, biodiversity, and population dynamics. Birds are among the most fascinating species to monitor since they are often the most vulnerable to environmental changes, such as deforestation or forest fires. Estimates indicate that 13%, or 1,370 species, of all bird species, face extinction. Despite having a large range, many bird species are difficult to identify. Ineffective and time-consuming manual tracking of the birds by experts was used up until recently. . To solve this issue and assist ecologists, we provide a deep learning approach. In order to accomplish this, we want to automatically identify bird species using aural inputs by using the most recent Artificial Neural Networks model (ANN model). Increasing the classification accuracy of a current classifier for bird species was the goal of this effort. According to this, the accuracy during training was 100% and during validation it was 97%. We may therefore conclude that ANN can successfully avoid the present implementation techniques and correctly identify bird species. A few examples of words used in machine learning are ANN, CNN, SVM, Random Forest, and Audio Signal Processing.

+91-7667918914 iarjset@gmail.com 0 Items International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal ISSN Online 2393-8021 ISSN Print 2394-1588 Since 2014 Home About About IARJSET Aims and Scope Editorial Board Editorial Policies Publication Ethics Publication Policies Indexing and Abstracting Citation Index License Information Authors How can I publish my paper? Instructions to Authors Benefits to Authors Why Publish in IARJSET Call for Papers Check my Paper status Publication Fee Details Publication Fee Mode FAQs Author Testimonials Reviewers Topics Peer Review Current Issue & Archives Indexing FAQ’s Contact Select Page DETECTION OF DEPRESSION AND ANXIETY IN CHILDREN USING MACHINE LEARNING Vaishnavi.R, Prof. Shankar.B S Abstract- Childhood mental disorders such as anxiety, depression and attention deficit disorder are commonly found amongst children. It is crucial to diagnose these problems at an early stage to ensure proper treatment and to prevent further complications. Machine learning techniques can be applied to analyze a patient's history, aiding in the diagnosis of the problem. In this research, three machine learning techniques have been identified and compared based on their performance in accurately diagnosing five common mental health disorders. The objective is to determine the most accurate technique. The dataset contains sixty attributes, but only twenty-five attributes were found to be important in diagnosing the disorders. By ignoring irrelevant attributes, the techniques were evaluated based on their performance on selected attributes. Downloads: | DOI: 10.17148/IARJSET.2023.10844 How to Cite: [1] Vaishnavi.R, Prof. Shankar.B S, "DETECTION OF DEPRESSION AND ANXIETY IN CHILDREN USING MACHINE LEARNING," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2023.10844 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form   Submit to iarjset@gmail.com or editor@iarjset.com   Submit My Paper Author CenterHow can I publish my paper? Publication Fee Why Publish in IARJSET Benefits to Authors Guidelines to Authors FAQs (Frequently Asked Questions) Author Testimonials IARJSET ManagementAims and Scope Call for Papers Editorial Board DOI and Crossref Publication Ethics Editorial Policies Publication Policies Subscription / Librarian Conference Special Issue Info ArchivesCurrent Issue & Archives Conference Special Issue Copyright © 2026 IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License. Open chat

Abstract: StyleSage is an innovative project that leverages advanced machine learning techniques to revolutionize hairstyle recommendations. By analysing user-provided face images, whether through image uploads or real-time camera input, the system accurately identifies the individual's facial shape from five categories: long, square, oval, heart, and round. This critical initial step sets the stage for tailored suggestions. Utilizing a diverse dataset of hairstyles, the model then selects the most fitting options based on the user's facial shape, resulting in a curated list of six hairstyle images. Beyond saving users time and experimentation, StyleSage enhances their confidence by offering personalized recommendations that harmonize with their unique features, seamlessly combining technology and beauty. Incorporating sophisticated image processing and machine learning algorithms, StyleSage bridges the gap between technology and personal aesthetics. Its ability to discern facial attributes enables precise facial shape classification, which, in turn, drives the selection of hairstyles that truly complement the individual. By fostering a synergy between data-driven predictions and the art of beauty, StyleSage empowers users to confidently explore hairstyles aligned with their distinct characteristics, marking a significant advancement beyond conventional recommender systems. Ultimately, StyleSage exemplifies the marriage of machine learning and personal expression, reinventing how we approach hairstyling choice

Keywords: Image Processing, Facial Attribute Analysis, Machine Learning Algorithms, Data-driven Predictions, Recommender Systems

Abstract: In today's world, numerous students often find themselves pursuing career paths influenced by external factors such as family, peers, or societal expectations rather than following their true passions. This frequently results in discontentment and a sense of unfulfilment within their chosen professions. To address this issue, we propose the creation of a personalized career recommender system built to guide students in identifying and selecting career path that get align with their genuine interests. In our daily lives, we encounter a constant barrage of recommendations and information from various sources, including individuals, newspapers, and the internet. The sheer volume of information available on the internet can pose a challenge for the students who are exploring diverse educational and career opportunities. Our recommendation system aims to simplify this process by narrowing down choices based on individual interests, thereby facilitating a more straightforward decision-making process for students as they chart their future paths.

Keywords: Career, Education, Recommendation, Machine Learning

Abstract: In the ever-evolving fields of computer vision and machine learning, real-time object detection presents a significant challenge. Our project dives into this domain, harnessing cutting-edge machine learning algorithms. By leveraging advanced methodologies like YOLO variants and efficient backbone architectures, our system aims to redefine real-time object detection. Traditional methods often compromise either accuracy or speed, limiting their effectiveness in dynamic environments. Our approach seeks the ideal balance, achieving both precise and swift object identification in complex scenarios. This integration of state-of-the-art techniques empowers our system to serve diverse sectors, from autonomous systems and security to interactive technologies. Through this endeavour, we envision a future where intelligent visual perception sets new standards for real-time object detection.

Keywords: Object detection, YOLOv7, Machine Learning, Faster R-CNN.

Abstract: Autism spectrum disorder is a neurodevelopmental disorder that affects socio-communicative and behavioral abilities. In the language aspect, there is a greater impairment at the pragmatic level and in non-verbal aspects. The objective of this study was to characterize the severity of autism spectrum disorder in girls, pre and post speech language therapy, and describe the process of speech language intervention using educational software program, picture exchange communication system allied to the principles of behavioral analysis applied to language. The Autism treatment evaluation checklist with the parents was applied. Then, a therapeutic programs session of 50 minutes was developed, one per week and then the questionnaire was reapplied.

Keywords: Autistic Disorder, Speech-Language, therapy, intervention, children, behavior.

Abstract: Non-invasive heart rate prediction has gained significant interest in various domains. Traditional methods utilizing external gadgets with the intention of of estimating heart rate through electrocardiogram (ECG)sensors often require direct skin contact, limiting comfort and utility. Recent developments in computer vision techniques have shown their capability to extract physiological data from facial videos through the detection of the forehead region The proposed real-time system employs OpenCV for facial recognition and tracking, ensuring with good lighting and minimal motion artifacts. Additionally, the study explores recognizing various physiological waveforms from raw data streams to enhance health monitoring capabilities. Shifting focus to image reports of ECGs, the research employs machine learning to digitize and analyses ECG paper records automatically. The transformation of ECG data into 1-D signals facilitates the extraction of P, QRS, and T waves, aiding in measuring heart electrical activity using diverse techniques. We employ dimension reduction techniques for feature extraction, and multiple classifiers such as ensemble, logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN) are used for diagnosis. The resulting model demonstrates diagnostic potential, accurately identifying ECG records to interpret various cardiac conditions, such as myocardial infarction, arrhythmias, and normal heart function

Abstract: Flight delays have significant implications for both travelers and the aviation industry. In this project, we address the challenge of predicting flight delays by harnessing the power of machine learning regression algorithms. By analyzing historical flight data and relevant influencing factors, we aim to provide accurate estimates of delay times for specific flights. The project begins with a comprehensive collection and pre-processing of relevant data, including departure and arrival times, weather conditions, airport congestion, and historical delay records. Feature engineering techniques are employed to extract valuable insights from the raw data, enhancing the predictive capabilities of the models. Several machine learning regression algorithms are explored, including Linear Regression, Support Vector Regression (SVR), and Decision Tree Regression. The selection of these algorithms is based on their suitability for capturing complex relationships and patterns within the data. Hyper parameter tuning and model evaluation are conducted rigorously to ensure optimal model performance. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are employed to quantify the accuracy of the predictions The contribution to our calculation is columns of highlight vector like flight date, take-off delay, separation between the two air terminals, planned appearance time and so forth We at that point use choice tree classifier to foresee if the flight appearance will be delayed or not. Besides, we compare decision tree classifier and calculated.

Abstract: In this study, a high-performance CMOS band-gap reference (BGR) is designed. The suggested circuit uses a current mode architecture that has been specially designed for low supply voltage situations. The main component of the circuit uses the Brokaw BGR architecture, which only uses first-order temperature compensation technology and a three-stage operational amplifier to achieve high PSRR and a low temperature coefficient. The circuit uses Chartered 0.18-m CMOS technology and operates at 1.8 volts, simulation results are provided. According to the simulation's findings, the temperature coefficient is 9 ppm/K for the -40 to 125°C temperature range, and the reference voltage fluctuates by no more than 0.067 mV when the power voltage shifts from 1.44 to 2.16V. The PSRR is 108.5dB at 10 KHz with the power consumption of only 0.355mW as well.

Keywords: PMOS, NMOS, Low power applications, Constant Voltage, RLC Circuits, PSRR, Stability Analysis, Noise Parameters.

Abstract: The development and implementation of a content-based book recommendation system using machine learning algorithms. To improve the quality and accuracy of the recommendations given, the system employs a hybrid method that combines content-based filtering and collaborative filtering techniques. Content-based filtering is employed to recommend items based on their similarity to previously liked items. By analyzing the characteristics and features of books, the system identifies similarities between them and recommends books that share similar attributes. Collaborative filtering is utilized to leverage user ratings and establish correlations between users and items. This allows the system to recommend books that are popular among users with similar preferences. The implementation of the recommendation system is built upon the Django framework, which provides a robust and scalable web development environment. The system incorporates various machine learning algorithms, including feature extraction, similarity measures, and recommendation models, implemented by using the Python programming language and Jupyter Notebook for exploratory data analysis. Overall, this project shows the successful development and implementation of a content-based book recommendation system.

Abstract: This paper compares of lateral load acting on steel structure and their impact on cantilever floors. Height increases vulnerability to buckling under wind loads and seismic loads, affecting high-rise structures' stability. Tall structures are more flexible and can withstand earthquake stresses better. Assessing building height's impact on seismic load resistance is crucial, as it varies with horizontal seismic stresses. Steel frames are popular due to their ease of construction, maintenance, and retrofitting. However, their structural stability increases with height. Steel bracings can increase strength in steel frames. This paper examines the impact of brace types on steel framed structures under Indian Standards.

Keywords: steel structure, lateral load, stability,retrofitting.