Volume 12 | Issue 11 |

 Abstract

In today's dynamic work environments, the management of complex projects involving diverse stakeholders--employees, managers, and clients--presents considerable challenges. Traditional project management tools often exhibit limitations in real-time adaptability, intelligent task prioritization, and seamless transparency. These deficiencies lead to inefficiencies, communication breakdowns, and project delays. Additionally, the inability to effectively automate workflows exacerbates issues such as bottlenecks, missed deadlines, and employee burnout. This paper addresses the need for a more sophisticated project management solution that incorporates artificial intelligence (AI) and machine learning (ML) technologies. By integrating AI/ML, the proposed system aims to enhance real-time decision-making, automate task assignments based on priority, and foster greater transparency and collaboration among all stakeholders.

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Project automation, AI/ML, task assignment, workflow creation, progress tracking, transparency, productivity, real-time communication.

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 Abstract

Technology has advanced rapidly in recent years. This has been a godsend for the people, making their lives easier. The advancement of technology has a significant impact on students. This has revolutionised the way students learn, providing them with access to vast amounts of information and resources. Remote study is now possible for anyone who has a digital device like a mobile or a laptop and an internet connection. While technology has many benefits for students, it can also be a major source of distraction. The constant influx of notifications and updates might make concentrating on activities and assignments difficult. When working on a laptop, multitasking is simple, such as flipping between tabs for different purposes or opening undesired applications during productive time. This takes up most of the student's considerable time, which could be better spent studying and learning. To address the challenge of distractions and disengagement in online learning, we propose a gamified educational web application that combines video lessons with interactive quizzes. Our platform is designed to enhance student focus, motivation, and retention by incorporating game-like elements such as rewards, progress tracking, and leaderboards. This solution aims to change how students engage with online educational content by creating a more interesting learning experience. By embedding quizzes within the video content and rewarding focused engagement, the application keeps learners actively involved and motivated throughout their educational journey. This innovative approach is aimed at improving focus, retention, and overall academic performance, offering a dynamic learning environment that not only helps students manage distractions but also enhances the quality of online education. The solution is poised to benefit students, educators, and institutions alike, potentially reshaping the future of e-learning. Our approach with this gamified educational platform is significant because it addresses a key issue that many existing online learning solutions fail to tackle: student engagement and focus. Traditional e-learning platforms provide access to content but often overlook how students interact with that content, especially when they are bombarded with distractions. By embedding quizzes directly within the video lessons, we ensure that students stay actively involved rather than passively consuming information. This solution has the potential to extend its impact beyond individual students, transforming how educational institutions deliver online content by making it more interactive, engaging, and tailored to student needs.

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technology advancement, online learning, remote study, distractions, multitasking, gamified, interactive quizzes, retention, active learning, e-learning, student-centred education

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 Abstract

Skin diseases pose a significant health concern worldwide, affecting millions of individuals. The accurate and timely diagnosis of these conditions is critical for effective treatment. This project presents a robust solution for skin disease classification using deep learning techniques, specifically the VGG16 architecture, implemented in MATLAB. The primary objective of this research is to develop a highly accurate and efficient model for the automated classification of skin diseases. The dataset used in this project is composed of five distinct classes of skin diseases, including Acne-cystic acne, biting fleas, diabetic blisters, spider bites, and vitiligo. Each class is carefully curated to represent a wide range of skin conditions, making the model versatile and capable of handling various dermatological challenges. The VGG16 architecture, a well-established convolutional neural network (CNN) model, is employed for its remarkable feature extraction capabilities. Transfer learning is applied to fine-tune the pre-trained VGG16 model on the skin disease dataset. The model is trained, validated, and tested using a rigorous cross-validation approach to ensure its reliability. One of the standout achievements of this project is the exceptional classification accuracy obtained. The model demonstrates an impressive accuracy of 98.08%, signifying its effectiveness in accurately identifying and classifying skin diseases. This high accuracy rate is crucial in reducing misdiagnoses and enhancing the overall quality of patient care. In addition to its high accuracy, the proposed system also offers real-time skin disease classification, making it a valuable tool for medical professionals and dermatologists. The user-friendly interface developed in MATLAB ensures ease of use and accessibility, allowing healthcare practitioners to make informed decisions swiftly and accurately. In summary, this project presents a comprehensive approach to skin disease classification using deep learning techniques, with a focus on the VGG16 architecture. The achieved accuracy of 98.08% demonstrates the model's capability to accurately classify various skin diseases, thus aiding in early diagnosis and effective treatment. This research contributes to the advancement Generalization to Real-World Settings: Models that perform well in controlled research settings may not always generalize effectively to real-world clinical environments. Differences in lighting, camera quality, and patient demographics can all affect the performance of skin disease classification models. Robust validation on diverse, real-world datasets is essential for successful deployment in clinical practice(ar5iv). Future Directions and Clinical Applications As deep learning models continue to evolve, several promising directions are emerging in skin disease classification research: Mobile Health Applications: With the increasing availability of smartphones equipped with high-resolution cameras, there is growing interest in developing mobile applications for skin disease detection. These apps can allow users to capture images of skin lesions and receive instant analysis, making early detection more accessible to the general public(SpringerOpen).Teledermatology: AI-powered teledermatology platforms are being developed to diagnose remote skin disease, particularly in underserved areas. By integrating deep learning models with telemedicine platforms, healthcare providers can offer more timely and efficient care to patients who may not have access to in-person dermatological services(SpringerLink)(ar5iv). Improved Model Interpretability: Future research will likely focus on enhancing the explainability of deep learning models to increase their acceptance among clinicians. Techniques like Grad-CAM (Gradient-weighted Class Activation Mapping) can help visualize which parts of the image influenced the model's decision, providing insights into the underlying reasoning behind each prediction(ar5iv) Key Words: Skin disease, Convolutional Neural Network, Random Forest, Feature Extraction, Deep Learning.

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abstract you will find

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Abstract: Arthur Miller (1915-2005), a celebrated dramatist of 20th-century American literature, is a distinguished and profound social critic of contemporary social evils. Through his moralistic play, All My Sons, Miller has conveyed the theme of social responsibility through the allegorized tragic protagonist, Joe Keller, the killer of 21 American pilots. Miller emphasizes that one should not forfeit or sell one's conscience for personal and family benefit. Miller has investigated the philosophy of a man, Joe Keller, a member of the representative American everyman, as a dishonest and self-centered manufacturer selling his ethics for worldly consideration, who puts his responsibilities above those of society. Joe considers that his rightful position in his society is to be a good husband and a good father. Yet, he commits an awful offense against the outer world. His perverted priorities blind his conscience and weaken his judgment. The fact cannot be denied that "Chris, a man can't be Jesus in this world", but he must preserve some ethical values that secure society.

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Self-realization, dishonesty, identity crisis, allegory, and tragedy.

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Baru Chandidas's Sri Krishna Kirtan: Tradition, Reconstruction, and Modern Relevance

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Baru Chandidas's Sri Krishna Kirtan: Tradition, Reconstruction, and Modern Relevance

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Illegal logging and tree smuggling have profound ecological, economic, and social repercussions, ranging from habitat destruction and loss of biodiversity to undermining local economies and exacerbating climate change. This project proposes an innovative system leveraging advanced technology to combat these challenges effectively. The system integrates Radio Frequency Identification (RFID), sensors including DHT11 (temperature and humidity), fire sensors, IR sensors, an RFID reader, and Wifi connectivity. The primary objective of the system is to track and protect valuable trees throughout their journey from origin to destination. RFID tags are embedded in trees for real-time identification and tracking. Conventional enforcement methods often fall short due to the vast and remote nature of forested areas where these activities occur. Data from these sensors are transmitted via Wifi to a centralized monitoring system, enabling continuous surveillance and immediate response to anomalies. By employing this integrated approach, stakeholders can enhance traceability, prevent illegal activities, and ensure the sustainable management of valuable tree species.

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Tree Smuggling , Prevention Valuable Tree Protection, RFID-Based Monitoring,Forestry Anti-Poaching Technology, Forest Conservation with Sensors, Smart Forestry Solutions, Environmental Protection Technology, Wireless Sensor Networks in Forests, Real-Time Tree Tracking,.

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Abstract In today's changing environment life has become a rat race. Hence, depression and stress related problems have become lifestyle diseases. Depression has significant relationship with loneliness and self efficacy. Objective- Examining the consequences of depression among adolescent students and its effect on loneliness and self efficacy, the present study has focused on The Effect Of Loneliness and Self-Efficacy Among Adolescent Students In Terms Of Their Levels Of Depression (At Risk, Vulnerable And Non-depressed) Method A group of 150 higher secondary students from 2 schools in murshidabad district of West Bengal were drawn equiproportionally from two stream of education ( Science stream and Humanities stream ). They were selected randomly. The sample of present study was categorized under 3 levels of depression i.e. non-depressed, vulnerable and at risk by administering WHO Depression symptom checklist and Beck Depression Inventory. Conclusion The level of loneliness and self efficacy among adolescent students does not differ significantly in terms of their level of depression (at risk, vulnerable and non-depressed).

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Key words- Depression, loneliness, Depression symptom checklist , Depression Inventory, Self-efficacy.

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Abstract: This study focuses on developing an advanced accident prevention system tailored for mountain roads, which are characterized by hazardous terrain, sharp curves, and unpredictable weather. The system uses infrared (IR) sensor technology to identify vehicles and obstacles in real time, especially from blind spots. IR sensors are strategically deployed along the road to detect potential hazards, and a microcontroller processes the data to activate a warning system comprising visual and auditory alerts. Tested under diverse weather conditions, the system achieved 95% accuracy in hazard detection and driver notification, showcasing its effectiveness in mitigating accidents. The cost-effective design, devoid of complex components like LCDs, ensures broad applicability and accessibility. This innovation offers a promising solution to enhance safety on mountain roads, potentially saving lives and reducing risks.

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Index Terms - Ultrasonic sensors, hazard detection, accident prevention, warning system, microcontroller, mountain road safety, driver awareness.

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Agriculture is the most stream on which ranchers depend. Numerous overviews have demonstrated that low rate of agriculturists is proliferate over a long time. The most reasons for the increment in soil crop fertility loss are climate conditions, obligations, need of points of interest around the soil. In a few farther regions agriculturists need data almost soil quality, soil supplements, soil composition and may select off-base edit to sow which comes about in less abdicate. So as to overcome the issues confronted by ranchers we are attempting to actualize a demonstrate utilizing with pseudo color matching system with trained features classification of Artificial Neural Networks (ANN) and Random Forest which predicts the soil quality taking input as a few critical parameters related to soil. This paper basically centers on anticipating the crop abdicate utilizing the ANN combined with Random Forest which is totally a program arrangement additionally prescribes appropriate fertilizers to pick up tall surrender of crops. Soil pictures are captured with the assistance of Smartphone and store all the pictures as soil dataset. Soil pictures are prepared through the diverse steps of advanced picture preparing counting soil picture upgrade, soil picture segmentation, and soil picture highlight extraction. Amid the highlight extraction, Tone, Immersion and Esteem of the soil picture are calculated with store Immersion and Tone additionally Immersion as an file for the include vector of the soil pictures. Expectation of soil pH is done with the assistance pseudo color matching system of Straight Relapse, Neural network, and Random Forest Relapse. The coefficient of the straight relapse is 0.859 for the Immersion include of the soil picture. The relapse coefficient of KNN is 0.89326 for K=5 with an RMSE esteem 0.1311. It is found that ANN continuously gives distant better; a much better; a higher; a stronger; an improved" an improved result as compare to another one.

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Ranchers, soil supplements, pseudo color matching, Artificial Neural Network, Random Forest, Critical Parameters, random Forest, store immersion, relapse.

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An electric vehicle (EV) will be designed using CATIA software, and its performance will be optimized through simulation using IPG Car-maker. The goal is to develop a high-performance electric vehicle especially for racetrack environments. Finding the ideal balance between structural integrity, power-train efficiency, and aerodynamics is the main goal of the design process. Following the original design, Car-maker simulations are used to test and confirm the performance of the vehicle under various racing circumstances virtually. These simulations aid in the fine-tuning of elements like handling, energy consumption, and vehicle dynamics to guarantee that the EV satisfies demanding performance requirements appropriate for professional racing.

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CATIA , IPG Car-Maker, MATLAB

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