VOLUME 11, ISSUE 8, AUGUST 2024

Abstract: Artificial Intelligence (AI) has significantly transformed various industries, including the pharmaceutical sector. As of 2023, the US AI market is valued at $123 billion, with a projection of $594 billion by 2032. Industries such as technology, automotive, finance, and healthcare have seen substantial AI adoption. In healthcare, AI optimizes routine tasks, accelerates drug discovery, and enhances clinical trials and manufacturing processes. However, the increased reliance on AI exposes pharmaceutical companies to cybersecurity threats, including data breaches, intellectual property theft, ransomware, and insider threats. Robust cybersecurity measures, such as strong access controls, securing AI systems, incident response plans, regular security audits, data encryption, employee training, and industry collaboration, are critical. Future trends indicate growing AI investment in healthcare, necessitating continuous advancements in cybersecurity to protect sensitive data and ensure regulatory compliance.

Keywords: Data encryption, AI optimization, Incident response, Cybersecurity, Intellectual property

Abstract: The prevalence of fake reviews on e-commerce platforms poses a significant challenge, undermining consumer trust and distorting the marketplace. This study addresses the detection of fake reviews using machine learning techniques, focusing on both semi-supervised and supervised learning approaches. We implement the Expectation-Maximization (EM) algorithm alongside the Naive Bayes classifier to distinguish genuine reviews from fraudulent ones. Our system analyses review content and various features, including word frequency count, sentiment polarity, and review length, to enhance detection accuracy. By utilizing a combination of Visual Studio for the frontend and SQL Server for the backend, we develop a robust platform capable of real-time detection and reporting of fake reviews. The proposed solution aims to provide a more reliable and authentic review system, ultimately improving the consumer experience and integrity of e-commerce platforms.

Abstract: The Kayaka Bandhu Android application aims to streamline the implementation of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) by digitizing and automating various processes involved in job demand registration and payment disbursement. MGNREGA, a pivotal social welfare scheme in rural India, guarantees 100 days of employment to adult members of rural households willing to undertake unskilled manual work. However, the implementation of this scheme has faced challenges such as corruption, delays in payment, and manual paperwork. To address these challenges, Job Ease provides a user-friendly platform for rural laborers and Rozgar Sewaks (employment assistants) to register job demands, manage work plans, and facilitate online payments. By leveraging mobile technology, the application aims to improve transparency, accountability, and efficiency in the execution of MGNREGA.

Keywords: MGNREGA, rural employment, women's empowerment, wage employment, decentralized democracy, Gram Panchayats, Gram Sabha.

Abstract: In today's digital age, ensuring robust security for online banking applications is paramount. Traditional authentication methods, while useful, often fall short in the face of sophisticated cyber threats. This project introduces an innovative image CAPTCHA authentication system designed to enhance security through an additional layer of protection. During the registration phase, users create their accounts by providing an email and password. They then select a series of images and click on specific locations within each image to set personalized key points. These key points are securely stored alongside the user's credentials. For login, users must not only enter their email and password but also replicate the image selection and key point clicks. The system extracts these key points during login and compares them with the stored values to verify the user's identity. This dual-authentication process significantly bolsters security by making unauthorized access considerably more difficult. Implementing this system involves using OpenCV for capturing and processing user clicks, alongside a secure backend to handle data storage and comparison. Key aspects such as data encryption, secure communication via HTTPS, and session management through authentication tokens are integral to maintaining the system's integrity. This approach leverages the familiarity and ease of use of graphical passwords, offering a user-friendly yet highly secure authentication method, thereby enhancing the overall security framework of online banking applications.

Keywords: Authentication, data encryption, secure communication, security.

Abstract: Visual impairment is one of the issues that several millions of people suffer from. They go through a lot of difficulties even to complete the basic chores. Even in their own home or office the struggle to navigate from one place to another without being dependent on anybody. As per the data from WHO(world health organisation) there are around 250+ million people with visual disablement out of which nearly 35+ million are totally blind which constitute a huge part of the population. The "Blind Assist System Using ML and Image Processing" is a cutting-edge technological solution aimed at empowering visually impaired individuals to navigate their surroundings with greater autonomy and safety. This innovative system relies on the integration of Machine Learning (ML) and Image Processing techniques to enhance the sensory capabilities of individuals who are blind or visually impaired. By capturing and analyzing real-time visual data from the environment, the system employs ML algorithms to identify and categorize objects and obstacles in the user's path. It then translates this information into actionable guidance, providing auditory or tactile feedback to the user through wearable devices like smart glasses .This abstracts the visual world into comprehensible data, thus enabling visually impaired individuals to make informed decisions and move confidently in their surroundings while avoiding potential hazards. The "Blind Assist System Using ML and Image Processing" represents a significant leap in assistive technology, promising greater independence and safety for those with visual impairments.

Abstract: Agoraphobia has become a fairly frequent disorder in today's environment. Agoraphobia is a spectrum of mental diseases characterized by strong dread and anxious feelings. The majority of individuals are completely oblivious of the problem. It's critical to detect it early on so that doctors can provide better treatment and avoid it from becoming a major issue. Recently, machine learning algorithms have been employed to analyze patient records in order to spot anomalies by modeling human thought or forming logical inferences. In this study, we strive to detect agoraphobia in the early stages. The basic theories and uses of machine learning algorithms in identifying anxiety kinds are also reviewed in this project work. In our project work we build an application with model that can predict panic disorders at early stages using ML algorithm and finally our system will recommend the suitable solution plan for the patients. Our proposed work uses some supervised machine learning algorithms like Random Forest or KNN Algorithm or Decision Tree algorithm for prediction. Proposed system is a real time medical system useful for hospitals and doctors and built using Microsoft tools such as Visual Studio tool and SQL Server tool. We use these tools as they support more libraries, packages required to build real time applications

Abstract: Women safety is an essential issue due to the rising crimes against women these days. To help resolve this issue we propose a GPS based women safety system that has dual security features. This device can not just be used by women when in distress but also by children when their travel modes are sans elders. For elderly people with issues like Alzheimer's this device can turn out to be very useful for them as well as the families. This device sends the current location of the woman/child/elderly to the family members and concerned authorities in case of any harassment faced or if in any sort of trouble. The device also has a panic button which is an in-built 400kV electric shock generator, which upon pressing will knock the assaulter down due to a sudden shock but without any fatality. The device is made using an AVR microcontroller, a GPS module, a GSM module and a high voltage generator.

Keywords: Women safety, Rising crimes, GPS-based system, Dual security features.

Abstract: A medical certificate, often known as a doctor's certificate, is a document attesting to the findings of a patient's medical examination that is prepared by a doctor or another medically competent health care professional. It can be used as proof of a medical condition or as a sick note or fit note, which indicates that an employee is not well enough to work. Sometimes you need a medical certificate for taxation, legal procedures, insurance claims, or to receive specific health benefits from your work. Medical certifications are used to show that an activity is eligible, such parking for the disabled. Medical certifications can also be used to explain a person's condition, like blindness. To attest that a person is clear of communicable diseases, drug addiction, mental illness, or other health concerns, medical certificates are frequently utilized. When submitting an application for something, such an eye exam to obtain a driver's license, health requirements are frequently necessary. At times, medical requirements are self-reported by the applicant in a self-evaluation, without the assistance of a physician or access to their medical history. Certain jobs have specific health requirements or require a medical history. One type of fraud is the falsification of a medical certificate. Jurisdictions have different laws regarding the fabrication or forging of medical certificates; however, those who use fake credentials risk legal trouble as well as health risks. There have been arguments about whether or not an employee can be fired for providing a fake medical certificate.

Keywords: AWS S3 Integration, Cloud Deployment, PDF Generation, Encryption and Decryption.

Abstract: The Animal Intrusion Detection and Alert System addresses the critical need for effective monitoring of animal intrusions in designated areas, such as agricultural lands and wildlife reserves. Traditional methods often rely on basic motion detection systems, which can lead to false alarms and lack specificity in identifying the types of animals. Our proposed solution leverages advanced computer vision techniques, specifically the Haar cascade algorithm, to accurately detect animals using a Pi Camera and a Raspberry Pi microcontroller. The system processes the video feed in real-time, identifying animal intrusions and immediately notifying users through the Telegram Bot API. These notifications include animal name, providing users with timely information to take appropriate actions.

Keywords: Haar cascade algorithm, Telegram API, Detection, Recognition.

Abstract: India's stock market is extremely variable and indeterministic, which has a limitless number of aspects that regulate the directions and trends of the stock market; therefore, predicting the uptrend and downtrend is a complicated process. This paper aims to demonstrate the use of recurrent neural networks in finance to predict the closing price of a selected stock and analyze sentiments around it in real-time. By combining both these techniques, the proposed model can give buy or sell recommendations. The proposed system has been implemented as a web app using Django and React.

Abstract: This project presents a comprehensive approach to phishing detection by utilizing email scraping, feature extraction, and machine learning, alongside integrating external services such as Seahound and Netcraft. By analyzing mailbox files with mixed HTML and mail data, it addresses the challenge of identifying malicious content within emails. The pipeline includes data extraction and cleansing, followed by Natural Language Processing (NLP) to transform textual content into meaningful features. Seahound and Netcraft add an innovative layer: Seahound analyzes URL legitimacy and reputation, while Netcraft offers historical insights into domain trustworthiness, enriching the feature set for the machine learning model. The meticulously labeled dataset distinguishes legitimate emails from phishing attempts, enabling rigorous training and evaluation of machine learning models, notably the Random Forest classifier and Support Vector Machine (SVM). The SVM model demonstrates high precision, recall, and F1-score metrics. This project underscores the synergy of email scraping, NLP, feature extraction, and machine learning, highlighting the crucial role of external services in enhancing phishing detection accuracy, thus advancing online security and protecting users from email-based cyberattacks.

Keywords: Seahound, Netcraft, Natural Language Processing (NLP), Phishing Detection.

Abstract: The "Human Emotion Detection for Hotel Feedback System" project is designed to revolutionize the way customer feedback is collected and analyzed in the hospitality industry. This system integrates advanced technologies to provide a seamless and efficient feedback collection process. The core components of the system include a web-based interface, image processing, and machine learning algorithms to detect and analyze customer emotions. The system comprises three main user roles: Admin, Staff, and Customer. Admins can log in through a frontend developed using AngularJS and C#, allowing them to add feedback questions categorized by type and view the collected feedback. Staff members also use the same frontend technologies to log in and review customer feedback. Customers provide feedback through a camera interface that captures their image, and the system processes these images to detect emotional responses. The backend system is built using Python, leveraging machine learning technologies. Specifically, a Convolutional Neural Network (CNN) algorithm is employed for image detection and emotion recognition. The captured images are analyzed to determine the customer's emotional state, and this information, along with the feedback, is stored in a database.

Keywords: Image Processing, Machine Learning, Convolutional Neural Network (CNN), Customer Feedback.

Abstract: Deep learning has become increasingly vital in data science, especially when handling large datasets. This paper focuses on analyzing intrusion detection attacks, which are critical for maintaining information security. The core technology lies in accurately identifying various network attacks. We explore the development of an intrusion detection system based on deep learning and propose a method using recurrent neural networks (RNN-IDS) for this purpose. Our project involves analyzing the KDD dataset, which comprises 44 features. Utilizing these features, we apply an RNN classification algorithm to train the data and assess accuracy. We also compare our results with those obtained from decision trees, support vector machines, and other machine learning techniques used by previous researchers on the benchmark dataset. This comparative analysis aims to highlight the effectiveness of RNNs in intrusion detection.

Keywords: Deep Learning, Prediction, DoS, R2L, U2R, Prob attack.

Abstract: The project's primary objective is to bolster the security of files stored in public cloud environments by implementing additional security measures. The focus is on developing an application that introduces an extra layer of protection to cloud storage, safeguarding files from potential attacks. Concerns arise from the delegation of data to third-party administrative control in cloud computing, which exposes data to security threats from other users and nodes within the cloud. The proposed solution revolves around the division of a file into fragments, which are then strategically distributed across various cloud storage spaces. This fragmentation and distribution strategy augments security, creating a formidable challenge for attackers attempting to access meaningful information, even if they compromise a specific cloud node. The methodology emphasizes the replication of fragmented data across multiple cloud nodes, ensuring a heightened level of security for stored files. Significantly, the method adopts a progressive recovery mechanism, surpassing conventional crypto techniques in terms of rate-distortion. This innovative approach significantly enhances the efficiency and effectiveness of recovering hidden information from encrypted images. In summary, the project addresses security concerns in public cloud storage through file fragmentation, distribution, and the application of an advanced crypto technique for reversible data hiding in encrypted images.

Keywords: File Fragmentation, Cloud Security, Cloud Storage.

Abstract: The transition to a sustainable future has led to the growing popularity of electric vehicles (EVs). However, EV owners encounter significant challenges in locating and accessing charging stations. Current systems for finding charging points are often outdated, unreliable, and lack comprehensive information on station locations and availability. This results in long waiting times and uncertainty, contributing to range anxiety and diminishing the convenience of EV ownership. To address these issues, we propose the development of an advanced EV charging point finder and booking app. This app aims to provide real-time information on charging station locations, availability, and the ability to book charging slots, thereby improving the overall user experience for EV owners.

Keywords: Electric Vehicles, Charging Stations, Real-Time Information, Range Anxiety.

Abstract: The goal of the project is to make Android apps more accessible to people with visual impairments. It uses machine learning and optical character recognition (OCR) to translate images into spoken language so that those with visual impairments can easily access weather updates and other vital information. The app's design also aims to provide clear instructions and help individuals overcome their daily challenges. The application provides real-time object detection, image-to-text conversion, text-to-speech synthesis, weather reporting, and position detection using the Java programming language and Android Studio. The camera takes pictures, converts visual data into aural input, and applies sophisticated machine learning models for object detection. Through the use of optical character recognition (OCR) technology, written content can be easily accessed by visually challenged individuals by converting photos into text. The application provides real-time weather data in audio format and also incorporates location detection and weather reporting. Clear directions and simple navigation are provided via the user interface, which was created with accessibility in mind.

Keywords: Optical Character Recognition, Speech Synthesis, Assistive Technology, Machine Learning.

Abstract: Infrastructure and human resource development are crucial to economic and social progress, as they encompass essential physical structures like roads, electricity, water systems, and telecommunications, which are vital for both economic activities and everyday life. However, many regions face persistent civic issues due to inadequate infrastructure, and the existing methods for reporting these issues-such as written letters, telephone calls, and manual entries at local offices-have proven to be ineffective and outdated. The current reporting mechanisms are cumbersome and not user-friendly, leading to inefficiencies and prolonged resolution times. To address these problems, a modern, streamlined reporting system is needed to ensure that citizens can easily report and track civic issues, thereby improving overall quality of life and civic satisfaction.

Keywords: Infrastructure Development, Civic Issues, Citizen Engagement

Abstract: The police department still work under manual system & paper work. To digitalize the police department proposed system developed. In existing system police station carry out on paper works & it will maintain in police station files/ledgers i.e., crime investigation details, evidence details are maintained in paper works, higher level influence chances of manipulation/modify data. So, crime investigation details need to secure. Along with maintain other country/foreigners study purpose enroll to college. Such a way that foreign students involved in crime cases like drug, smuggling, missing or any kind of crimes. Also, may be for foreign students any local persons give problem or any other things. It's helpful if we maintain/manage foreign student's details also to track passport details, local address there are currently stay in city. Now day's terrorist activity occurs more & more. Around 500 foreigners overstaying in Karnataka are facing criminal cases, while visas of 754 foreigners have expired. The State police registered 501 cases against foreign nationals, as of May 2023, for drug peddling, burglary, and other criminal offences.

Keywords: AWS S3 Integration, Cloud Deployment, PDF Generation, Encryption and Decryption.

Abstract: This project presents a web-based system that facilitates and streamlines the admission process. The system helps prospective parents find and learn about children available for adoption through a simple, user- friendly website. Key features include easy communication with foster homes, detailed child profiles, and helpful resources for adoptive families. The system also ensures that all information is kept safe. Future improvements will include a mobile app, better customization tools, virtual tours, and more support for different languages. The goal is to help more children find loving homes and give adoptive families the support they need during the adoption process.

Abstract: Brain tumors are among the most lethal and challenging diseases to diagnose and treat. Early detection and accurate classification are crucial for effective treatment planning and improving patient outcomes. This project focuses on developing a system for the detection and classification of brain tumors using Convolutional Neural Networks (CNNs). Utilizing MRI images, our model aims to differentiate between various types of brain tumors, offering a non-invasive and efficient diagnostic tool. The system is trained on a labeled dataset, and the CNN architecture is optimized for high accuracy in classification.

Keywords: Brain Tumor, MRI, CNN, Machine Learning, Image Classification, Medical Imaging.

Abstract: Plastic Identification Underwater addresses the pressing issue of plastic pollution in our oceans by developing a novel underwater plastic object detection system using the YOLOv8 architecture. The model, trained on a diverse dataset from Roboflow, achieved an accuracy rate of 87% in detecting submerged plastic objects. A Flask web application was created to enable users to perform object detection on live video streams and specific paths, while integration with a mobile application allows real-time detection using mobile cameras underwater. This comprehensive solution aims to enhance marine conservation efforts by facilitating the identification and removal of underwater plastic debris.

Keywords: Underwater plastic detection, YOLOv8, Marine conservation, Real-time detection

Abstract: The Emergency Wheels project is designed to provide a reliable and efficient solution for individuals experiencing vehicle breakdowns, regardless of their location. Through an Android application, registered users can seamlessly connect with approved mechanics via a secure and monitored system. The app exclusively enlists authorized mechanics, ensuring the trustworthiness of the services offered. Users can report breakdown incidents and efficiently connect with nearby mechanics through the app's intuitive interface. To prevent unauthorized charges, the system closely monitors mechanics, while users provide feedback on the service received to maintain quality standards. With a focus on accessibility and user-friendliness, the Emergency Wheels project aims to offer a dependable solution for on-the-go vehicle breakdown assistance across various locations.

Keywords: Infrastructure Development, Rental Service, Breakdown Services, User Interface.

Abstract: In this comprehensive project, we embark on the journey of constructing a robust real-time messaging platform inspired by WhatsApp, fortified with cutting-edge security features and enriched with video and voice calling capabilities. Leveraging Next.js for front-end development and Tailwind CSS for sleek UI design, we delve into the realm of Socket.io for seamless real-time communication. The backend infrastructure is powered by Node.js and Express, with Prisma facilitating database management, while PostgresSQL ensures data integrity. Additionally, Firebase augments the platform with authentication services. The pièce de résistance of this project is the integration of the ZEGOCLOUD video and voice call SDK, which empowers developers to effortlessly incorporate secure communication functionalities into their applications. Through meticulous implementation and exploration of various libraries and technologies, this endeavor culminates in the creation of a Full Stack WhatsApp Clone, poised to redefine the landscape of real-time messaging with its robust security and versatile communication capabilities.

Abstract: In today's digital age, music plays a significant role in influencing and reflecting emotions. An emotion-based music recommendation system aims to enhance the user's listening experience by suggesting songs that resonate with their current emotional state. This project leverages advanced machine learning algorithms and natural language processing techniques to detect and classify emotions from user input, such as text, speech, or facial expressions. By analyzing emotional cues, the system can curate personalized playlists that align with the user's mood, whether they seek to amplify their current feelings or shift to a different emotional state. The recommendation engine is trained on a diverse dataset of music tracks labeled with emotional attributes, allowing it to accurately match songs to emotions.

Abstract: A person having a low status in a society with social, political, or other hierarchies is called a subaltern. It may also refer to someone who has experienced oppression or marginalization. The word subaltern is made up of the Latin words "sub" (meaning "below") and "alternus" (meaning "all others"), which naturally conveys the idea of being obedient to everyone else. An individual or group of people who lack political or economic authority, such as a poor person residing in a dictatorship, are also described by the word. A variety of themes are reflected in subaltern literature, including the oppression of the lower and working classes, marginalization, gender discrimination, oppression, contempt for women, impoverished classes, and racial and class prejudice. Gender inequality is one of these subjects that is heavily emphasized in subaltern literature. Even though women are revered as Kali, Durga, and Shakthi, child marriages, the sati system, and education denial are still prevalent. Even if we talk about women's empowerment and equality in the twenty-first century, gender prejudice still exists today.

Keywords: Gender, Discrimination, Subaltern, Women, Inequality.

Abstract: The Mid-Day Meal Scheme in India is a key government initiative designed to enhance the nutritional health of school-aged children by offering free lunches in schools. Despite its commendable goals, the scheme encounters several obstacles, such as administrative inefficiencies, logistical challenges, and issues related to quality and hygiene. This project proposes the development of an Android application to overcome these hurdles, focusing on streamlining the management and execution of the Mid-Day Meal Scheme. The app aims to automate administrative processes, increase transparency, and strengthen monitoring, ultimately improving the delivery of nutritious meals to students.

Abstract: The increasing accessibility of quantum computing resources encourages research into the potential applications of this technology in a variety of scientific fields, including artificial intelligence, manufacturing, and finance. While a large number of research scientists do their work largely using cloud computing infrastructures, access to real (sometimes distant) quantum hardware resources necessitates the deployment and appropriate setup of several software components. We provide a hybrid cloud-based reference architecture in this study that makes it easier to launch new experiments utilizing a variety of quantum computing resources. The technique makes it easier to access many remote quantum compute resources and to run distributed quantum computing simulations in conventional cloud settings. Because the reference design is so adaptable to many cloud platforms, there are many opportunities for applications.

Keywords: Cloud computing, quantum computing, reference architecture, simulation, science gateway.

Abstract: Facial Emotion Recognition (FER) is a significant technology in fields such as human-computer interaction, healthcare, and security. This paper investigates the use of Convolutional Neural Networks (CNNs) for improving the accuracy and reliability of FER systems. CNNs, known for their ability to automatically extract hierarchical features from raw data, offer substantial improvements over traditional machine learning techniques. The proposed system is trained on a large dataset of facial images and demonstrates a notable improvement in accuracy, achieving a classification rate of 93.5% across multiple emotion categories. The study also includes a comprehensive literature survey, examining key advancements in FER and the role of CNNs in this domain.

Keywords: Facial Emotion Recognition, Convolutional Neural Networks, Deep Learning, Emotion Detection, Human-Computer Interaction

Abstract: As data is being generated each and every time in the world, the importance of data mining and visualization will always be on increase. Mining helps to extract significant insight from large volume of data. After that we need to present that data in such a way so that it can be understood by everyone and for that visualization is used. Most common way to visualize data is chart and table. Visualization is playing important role in decision making process for industry. Visualization makes better utilization of human eyes to assist his brain so that datasets can be analyzed and visual presentation can be prepared. Visualization and Data Mining works as complement for each other. In this paper, we present which type of chart is suited for which type of data.

Keywords: Data integrity, Information Visualization, Scientific Visualization, Decision Making, Graph, Chart, Xmdv tool.

Abstract: Because of the importance of lowering the infant baby death rate, the demand for innovative advanced controls for incubators is rapidly expanding. A variety of parameters must be monitored in an incubator. This research described an advanced control system for monitoring some critical parameters that affect an infant's life. This technology, which monitors and controls multiple parameters simultaneously with enhanced control and smooth operation, serves to improve the system's accuracy. Two temperature sensors are employed in the proposed system to control the incubator temperature and to monitor the skin temperature. A humidity sensor was utilized to determine whether or not the infant had peed. Measuring BPM with a pulse sensor (Beats Per Minute). The cooling fan and heating bulb are utilized to keep the incubator temperature at an optimal level. These two are controlled by an Arduino microcontroller based on the temperature of the incubator. Two push buttons are utilized in this system to check feeding and full body check- up completion. A user-friendly application page was created to ensure that the user could easily monitor the service. The systems are Arduino- based, and the IOT module can be used to control the incubator.

Keywords: Humidity Sensor, DHT11, Pulse Sensor, EM Reader, RFID Cards, LM35

Abstract: Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data is a core component of data science, driving applications in areas such as computational biology, law, and finance. However, such a highly positive impact is coupled with significant challenges. For tractable pricing of complex financial products, segregation of dubious activities in telecommunications networks, or determining the sanctioning of potentially dangerous criminal acts, the decisions suggested by these systems should be fully understood so that they can be trusted. The quest for interpretable, accountable, explainable, and responsible AI may thus be viewed as a fundamental scientific problem in the understanding of complex systems. A closely connected question would be how to hold such immense responsibilities accountable. Issues of social acceptance, ethical regulations, liability, and law enforcement concerning the behavior of these systems are being addressed by legislators and policymakers, highlighting a need for academic research in this area as well.

Keywords: Big Data, Actionable Insights, Artificial Intelligence (AI), Data Interpretation, Machine Learning, Data Analytics, Predictive Analytics, Data Processing, Data Visualization, AI Algorithms, Data Science, Business Intelligence, Data Mining, Real-time Analytics, Deep Learning, Statistical Analysis, Data Integration, Data-driven Decision Making, Automated Insights, Cognitive Computing.

Abstract: The goal of the Power Monitoring project is to increase energy efficiency and improve home safety. This clever setup tracks how much power is used at home using Arduino, voltage and current sensors. By delivering charges to each customer and the grid simultaneously, the device streamlines the billing procedure. The system also has features to identify fire, overvoltage, and overcurrent risks. When any of these dangers arise, the gadget turns off the electricity and uses GSM SMS to notify the householder and the fire brigade. By preventing electrical risks in household surroundings, this initiative helps to manage energy more efficiently.

Keywords: Energy Efficiency, Power Monitoring, Electrical Hazards, GSM Notification

Abstract: Coal mining, which is essential for global energy production, poses significant safety challenges for miners working in hazardous environments. This project explores the development and integration of advanced safety helmets designed specifically for coal miners, using Atmega 328p and NodeMCU controllers. These innovative helmets offer comprehensive safety features, including hazardous gas detection, fall detection, health monitoring and real-time connectivity. The system includes sensors to detect gases such as carbon monoxide, monitor vital signs such as heart rate and blood oxygen levels, and track the miner's location using GPS. The helmets provide instant alerts and data transmission via a mobile app, enabling quick responses to emergency situations.

Keywords: Senors, Nodemcu, Gps, Arduino, Server.

Abstract: The proliferation of cyber threats has necessitated the evolution of web-based firewall systems to fortify digital infrastructures against increasingly sophisticated and malicious attacks. Traditional firewall mechanisms, while effective to some extent, have exhibited significant limitations in adapting to the dynamic and rapidly changing nature of modern cyber threats. These conventional systems primarily rely on predefined rules and static configurations, which are insufficient in the face of advanced threats such as zero-day exploits, advanced persistent threats (APTs), and other evolving attack vectors. To address these challenges and enhance cybersecurity measures, this research explores the integration of Artificial Intelligence (AI) within web-based firewall systems. This study investigates the role of AI algorithms, particularly focusing on machine learning (ML) and deep neural networks (DNN), in enhancing the efficacy of web-based firewalls. By leveraging the computational power of AI, these systems can autonomously analyze massive datasets, discern intricate patterns, and make real-time decisions to mitigate evolving cyber risks. AI-based firewalls can dynamically adapt to new threats by continuously learning from the data they process, thereby significantly improving their threat detection and response capabilities. This approach marks a substantial shift from traditional firewalls, which are inherently reactive and limited by their rule-based architecture. AI empowers web-based firewalls to perform several critical functions more effectively. Proactive threat detection is one of the primary enhancements, where AI algorithms can identify potential threats before they can exploit vulnerabilities. Anomaly identification is another critical function, enabling the system to detect unusual patterns that may indicate malicious activity. Additionally, AI enables adaptive response mechanisms, allowing the firewall to automatically adjust its defenses based on the nature and severity of detected threats. This adaptability is crucial in maintaining robust security postures in an environment where cyber threats are continuously evolving.

Keywords: AI-Enhanced Firewalls, Dynamic Threat Detection, Zero-Day Exploit Mitigation, Intelligent Cyber- Defense Systems.

Abstract: Psychological factors or psychological parameters can play a major role in determining injuries in sports and physical activities. Although physical factors are involved in the sports injuries, the accident-prone profile appears to be dominated by psychosocial factors. The primary objective of the study is to determine the psychological and Occurrences of football Injuries ,The sampling method of the present research was the purposive method of sample design for football players. Data was collected through questionnaire form respondents from 1000 football players. The investigator is contacting the footballers individually and in some cases also at the venue of inter-university, state tournaments. A total of 578 injuries were detected among 1000 football players in one year during this period. A correlation test was taken to find out the relationship between Psychological Factors and occurrences of injuries of football players were correlated negatively with sum of the Psychological factors. To test for the effect of Psychological Factors on occurrences of football injuries, multiple regression analyses was carried out. Psychological Factors and the interaction term were regressed on overall occurrences of football injuries

Keywords: Factors, Occurrences, Injury, Football, Game

Abstract: Since the Indian economy is expanding and its financial markets are becoming more integrated into the global economy, it is critical to examine the established relationship of the securities market of India, which is represented by the Nifty index, and major global securities indices. This research investigates the dynamic correlations and causal relationships between the Nifty and a selection of global indices, including the Dow Jones Index (USA), Nikkei250 (Japan), FTSE 100 (UK) index, Hang Seng (Hong Kong) index, Shanghai Composite Index (China) index, and CAC 40 (France), using daily closing prices from January 1, 2010, to December 31, 2023. We take the closing prices of this index and estimate the daily average return of all indexes. To achieve these objectives, we used various statistical techniques such as correlation analysis, descriptive statistics analysis, and regression analysis. The findings reveal that the Dow Jones and Nifty were the top-performing global indices, while the FTSE and Hang Seng were the lower-performing indices. The Nikkei and CAC40 exhibited higher volatility, whereas the FTSE and Dow Jones displayed lower volatility. The findings will help international portfolio investors manage risk more effectively through diversification and hedging strategies. Understanding the transmission mechanisms for financial shocks allows regulators to coordinate monetary/fiscal policies with major economies to stabilize investor confidence domestically.

Keywords: Nifty, Global Indices, Stock Markets, Correlation, Regression Analysis

Abstract: The objective of the study was Impacts of Yogic Practices Program on Forced expiratory volume of Female students . The 45 female students selected for the present study were divided into three equal groups called, Experimental group I (Meditation Group), experimental II (Pranayama group) and Control group, consisting of 15 Female students in each group. They were the students of graduate Course and their age ranged from 18 to 25 years during the academic year 2016-17. The entire sample were directed to assemble in a multipurpose hall Padmpani College of Physical education to seek their willingness, to act as subjects. Pranayama and meditation programme was planned for 12 weeks, 5 days a week and 60 minutes a day. The investigator explained to them the purpose, nature, importance of the experiment and the procedure to be employed to collect their information. Further the role of the subjects during the experimentation and the testing procedure were also explained to them in detail. The result of the study reveals that there were significant difference were found in post-test Forced expiratory volume among Meditation, Pranayama and Control group . That means Pranayama was more effective to increase Forced expiratory volume as compare to their counterparts.

Keywords: Meditation, Pranayama and Control group, experimental group

Abstract: In this research paper, we have investigated the dependence of temperature and high intensity illumination on the photoconductive materials by using the properties of recombination rates, carrier generation rate and carrier concentration. This photoconductive materials shows the nonlinear effect in our studies. Nonlinear photoconductivity represents a vital area of study in material science, with significant implications for the development of advanced optoelectronic devices. This research explores the complex phenomena of nonlinear photoconductivity in various materials when exposed to high-intensity illumination. Unlike linear photoconductivity, where the conductivity increases proportionally with light intensity, nonlinear photoconductivity involves intricate mechanisms such as carrier trapping, defect state saturation, and multiphoton absorption, leading to non-proportional responses in the material's conductivity. This study focuses on the experimental characterization and theoretical modeling of these nonlinear effects in selected semiconductor and organic materials. The findings reveal how high-intensity illumination can induce significant deviations from linear behaviour, including sublinear and superlinear conductivity responses, and even negative photoconductivity under certain conditions. The results not only enhance our understanding of light-matter interactions in non-linear regimes but also provide valuable insights for optimizing materials for practical applications in photodetectors, solar cells, and other optoelectronic devices that operate under varying light conditions.

Keywords: Photoconductivity, optoelectronic devices, high-intensity illumination, multiphoton absorption.

Abstract: The Era of the eco-friendly technologies is emerging rapidly; bicycles are the most dependent modes of transportation. The environmental factors and the increase in fuel price make it clear that it is far better to use a bicycle over a motor vehicle for traveling. This project 'Design and implementation of Electric Bicycle with battery monitoring system and speed control mechanism, is designed to provide two modes of travel, such as a power- on-demand mode for long distances and pedaling mode for short distances. This electric bicycle is provided with ease to switch between these two modes of operation. The electric vehicle depends on the battery for energy. Provision for monitoring the state of charge of the battery using lot techniques is provided in the project. The speed controlling mechanism enables the user to ride the electric bicycle in different ranges of speed IOT based speed monitoring system is provided. The monitored values of battery state of charge and the speed are displayed so that the rider gets the information about the status of the battery and the speed. Each circuit is designed separately and assembled to form an electric bicycle which can make the long distance cycling easier with many user-friendly features.

Keywords: Electric bicycle, Speed Controlling, Speed Monitoring, Battery Monitoring.

Abstract: An innovative digital health management system has been designed with the intent of improving healthcare services in rural sub-centres. It allows health workers to register individuals and automatically generate unique IDs, streamlining the tracking and updating of patient medical records across different levels of care. By utilizing sequential ID generation, the system employs an auto-incremental ID generation feature within the database to assign each patient a distinct identifier upon registration, thereby facilitating efficient data management and patient follow-up. This system specifically focuses on diabetes and hypertension; it gathers comprehensive health data and utilizes a multinomial logistic regression model to categorize patients' health conditions as healthy, diabetic, hypertensive, or both diabetic and hypertensive. Based on these classifications, health workers can create personalized lifestyle management plans tailored to each individual's health needs. For infants and children under 18, the system offers detailed immunization charts, while pregnant women receive vaccination schedules and nutrition guidelines to ensure comprehensive prenatal care. In more complex cases, health workers can refer patients to doctors at Primary Health Centres (PHCs), and if advanced diagnostics or specialized treatments are required, PHCs can further refer patients to hospitals. The unique ID facilitates seamless information sharing and updates among sub-centres, PHCs, and hospitals, enhancing continuity of care and improving health outcomes in rural areas. This integrated approach aims to bridge the healthcare gap in underserved regions by leveraging data and predictive analytics to provide targeted and effective healthcare services.

Keywords: Digital Health Management System, Rural Healthcare, Unified Patient ID System, Predictive Analytics, Multinomial Logistic Regression

Abstract: The Dhobi Khana of Fort Kochi, Kerala, embodies a centuries-old tradition of community-based laundry practices upheld by the Tamil-speaking Vannan community. Originating during the Dutch colonial period, this collective laundry space operates through manual methods such as stone slab washing, rice starching, and traditional ironing using charcoal. These techniques, while unique and culturally significant, have led to numerous socio-economic and health challenges for its workers. This study evaluates the Dhobi Khana's historical and operational framework, highlighting critical issues such as physical strain, seasonal dependency, and resistance to technological adoption. It also investigates the community's reliance on tourism, which constitutes 90% of their clientele, and the reluctance of younger generations to engage in traditional laundry work. Drawing from both primary data collected through interviews and surveys, as well as secondary data sources, the research underscores the urgent need for policy interventions, financial support, and infrastructural modernization. Strategic initiatives, including government-backed modernization programs and heritage-based tourism promotion, could preserve the Dhobi Khana's cultural legacy while ensuring its economic viability in a rapidly modernizing society.

Keywords: Dhobi Khana, traditional laundry, sustainability, modernization, socio-economic challenges, community heritage, tourism dependency, generational shifts, health issues, financial instability, traditional methods, cultural preservation.