International Journal of Recent Advances in Engineering and Technology
International Journal of Recent Advances in Engineering and Technology P-ISSN: 2347-2812 Frequency: Biannual Publisher Website: https://mriindia.com/ Editor in Chief: Dr. Parikshit Mahalle Article Processing Charges: 50 USD Indexing In: Google Scholar, SCILIT, BASE, Semantic Scholar, Scribd, ResearchGate
Tomato Plant Leaf Disease Classification using Deep Learning: A Review
Tomato plants frequently be afflicted by diseases that may damage plants and decrease farmers’ earnings. finding these illnesse s early could be very vital for coping with them that affect tomato plants, like Yellow Leaf Curl Virus, Leaf mildew , late Blight, Early Blight, Septorial Spot, Bacterial Spot, goal Spot, Mosaic Virus, healthful, spotted Spider Mite, Powdery mould .We use deep getting to know strategies, especially a combination of EfficientNet-B0 with VGG-16, EfficientNet-B0 with CNN and VGG-16 with CNN, to investigate images of tomato leaves and decide if they may be healthful or diseased. The model learns to spot the specific signs of each disease, ensuring accurate detection. The system also suggests the best pesticides for treatment. By providing both disease identification and pesticide recommendations, this system helps farmers make better decisions to protect their crops, improve plant health and increase yield. This helps farmers grow healthier crops and increase food production in a sustainable way.
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ML-Powered Career Guidance: A Web Application for Personalized Career Decision-Making
Career decision-making is a crucial aspect of a student's academic journey, yet many struggle to identify the right career path due to a lack of personalized guidance. This research introduces a career guidance web application that leverages machine learning to provide tailored career recommendations. The platform gathers user data, including educational background, skills, and interests, to analyse and suggest suitable career options along with relevant skill recommendations. To enhance accuracy, the application employs two primary machine learning models: Random Forest, which predicts career paths with 87% accuracy, and TF- IDF, which identifies essential skills for professional growth. The frontend is developed using Next.js and Material UI to ensure a seamless user experience, while the backend, built with FastAPI and Python, handles data processing efficiently. Extensive testing confirms that the web application delivers reliable career recommendations and practical skill development insights, addressing the gap in traditional career counselling services. By integrating data-driven decision- making, this tool aims to empower students with personalized career guidance, enabling them to make informed academic and professional choices with confidence.
Review paper on Secure and Efficient Framework for Big Data Storage and Access Control in Cloud Environments Using Optimized Cryptographic Algorithms
A transition of industries from analogue to digital in last few decades has given rise to the requirement for reliable and strong big data handling systems. These systems must be able to handle complex and large databases at lowest possible cost but with high speed and reliability. Finance and retail industry, healthcare industry are the examples using big data and they rely heavily on big data analytics for decision making. Their fraud detection and compliance activities heavily depend in big data analytics. Cloud platform can satisfy these requirements. Cloud is cost effective, scalable as well as real time. Big data and Cloud technologies combined, forms Big Data Cloud, that provides necessary support for structured and unstructured data. But these systems, being shared among multiple users are prone to security and reliability attacks. This work proposes a secure storage framework and access control mechanism utilizing optimized cryptographic computations. Specifically, an Optimal Elliptic Curve Cryptography (OECC) approach is proposed, enhanced by the Modified Seagull Optimization Algorithm (MSOA) for key generation. The goal is to ensure secure data storage in the cloud while maintaining efficient data access and addressing critical privacy and computational efficiency needs.
Face Trace - Forensic Face Sketch Construction and Recognition Application
As we know hand-made face sketches are widely used in forensics, although they come with some limitations and are a time consuming process in criminal identification. In this project, we implemented some of the already existing approaches in combination to provide overall efficiency in the process of criminal identification. An application which is built to provide easy and computerised construction of forensic face sketches using the drag and drop features along with integration with the cloud infrastructure & deep learning for accurate recognition of the criminals from the law & enforcement database.
A Review on the Implementation of Artificial Intelligence for Real-Time Product Pricing and Demand Forecasting Optimization
In the contemporary, fast-paced global market environment, organizations need to respond swiftly and effectively to changing consumer expectations, rival pricing schemes, and fluctuating economic conditions. A paradigm shift from traditional, heuristic-based approaches to data-driven, adaptive, and scalable solutions is represented by the incorporation of Artificial Intelligence (AI) into pricing and demand forecasting processes. Artificial intelligence (AI) techniques, including machine learning (ML), deep learning (DL), and reinforcement learning (RL), have shown remarkably effective at identifying latent demand patterns, capturing non-linear relationships, and facilitating real-time dynamic pricing decisions. With a focus on their algorithmic underpinnings, implementation designs, real-world applications, and quantifiable effects on business performance, this paper summarizes the most recent state-of-the-art AI techniques used in product pricing and demand forecasting optimization.The paper also describes potential research routes and discusses current issues such data heterogeneity, model interpretability, and computational complexity. Clarifying the strategic role of AI in promoting revenue optimization, raising customer happiness, and boosting overall operational efficiency across a range of industrial areas is the key goal.
Forward Looking Perspective: AI shaping the Future of Software Testing
In recent years, the adoption of Artificial Intelligence (AI) in software development has experienced substantial growth and importance. Traditionally software testing has been a labor - intensive and error prone process and often forced by the limitations of human testers and conventional tools. However as technologies grow rapidly the applications of AI technologies including deep learning, machine learning, natural language processing is set to reshape the future of software testing. This paper explores the forward looking perspective of AI in software testing which examines challenges in traditional testing techniques and finds different key areas of software testing such as test automation ,test case generation , bug prediction, fault localization, security testing ,performance testing, code review with their novel technologies in AI. Through this exploration aim is to provide insights into future AI powered software testing which face to address challenges of integrating AI technologies with software testing.
Mood Sync: Personalized Music and Driver Safety through Facial Emotion Recognition
With the latest advancements in Deep Learning models and frameworks, we can tackle more complex problems than ever before. In this paper, we focus on two key areas: detecting drowsiness and recognizing emotions. Our goal is to create a system that can understand a driver's emotional and physical state, and respond appropriately. By alerting the driver when signs of fatigue are detected and suggesting music based on their current emotions, we aim to enhance their driving experience. For drowsiness detection, we use the dlib library and a facial landmark shape predictor to monitor the driver's eye conditions in real time. If the eyelids stay closed for a short period, an alert is triggered to wake the driver. Additionally, we incorporate AWS Rekognition to improve facial emotion detection, AWS Polly to generate audio alerts, and AWS S3 buckets to efficiently store and manage data. This integrated approach not only ensures driver safety but also personalizes their journey with music that suits their mood.
Revolutionizing Web Browsing with AI-Powered Personalized Ad Blockers
The growth of obtrusive and unrelated online advertisements has profoundly affected user experience and privacy. Traditional ad blockers, based on static rule-based approaches, find it difficult to cope with changing advertising tactics. This research suggests an AI-based personalized ad blocker aimed at transforming web browsing by providing dynamic, smart, and user-centric ad filtering. Based on machine learning models and behavioral analytics, the solution is tailored to match individual user behavior while ensuring high accuracy in recognizing and removing intrusive ads. The system combines computer vision and natural language processing (NLP) methods for end-to-end ad content analysis, enabling real-time detection of visual and textual components. Privacy is prioritized through on-device processing and federated learning approaches with minimal data exposure. Performance evaluations demonstrate significant improvements in detection accuracy, personalization, and browsing speed relative to conventional methods. This research highlights the promise of AI-driven solutions to improve user experience, maintain privacy, and revolutionize digital content consumption.
Machine Learning Based Prediction and Recommendation System for Anxiety and Depression: A Comprehensive Survey and Analysis
People often express their emotional and mental state through social media platform. This review synthesizes the key findings from recent studies, reporting diverse approaches to the use of machine learning, natural language processing, and multimodal analysis of physiological and behavioral data. By leveraging diverse data types, including text, images, and videos, provide valuable insights into users' mental well-being. Predicating anxiety and depression based on social media content allows for early identification of mental health issues before they become severe. Earlier research was based on findings that social media can serve as a rich source of information for understanding users' emotional conditions. Additionally, discusses the need for automated systems that can detect depression across different age groups, utilizing sentiment analysis and facial expressions. The review paper provides a deep view of the current techniques and provide it's limitative along with the research challenges in the field of mental health issues diagnosis by using ML algorithms.
As deepfake technology rapidly advances, it’s becoming increasingly difficult to distinguish between authentic and altered digital media. This study explores the application of deep learning techniques, specifically convolutional neural networks (CNNs) and Transformer-based models, to effectively detect deepfakes. The study provides a detailed experimental evaluation of the performance of these models in recognizing manipulated facial images and videos. In recent years, the rapid development of China's telecom industry has greatly improved the speed of telecom network construction. In addition, the telecom network (CNN) has also been used to learn the dynamics of Transformer, which has also been widely used. to make something happen.
Transforming Architectural Planning: - Exploring the Potential of Text-to-Image Models
Text-to-image models have the potential to transform architectural planning by making it possible to quickly and easily generate and visualize new design options, improve communication with clients and other stakeholders, and automate some of the tasks involved in architectural planning. This article describes various methods and techniques for developing text-to-image model for architectural plan. This review compares different methods and algorithms with the help of a pie chart.
Bridging Business Intelligence and AI for Advanced Educational Data Analytics
The increasing complexity of educational data and the imperative for data-driven decision-making within academic institutions underscore the necessity for advanced analytical tools. This study introduces the Comprehensive Educational Analysis Suite (EDUVISION), a robust platform designed to efficiently integrate, process, and analyze institutional data. Utilizing a multi-tenant architecture and AI-driven insights, EDUVISION facilitates seamless data visualization, predictive analytics, and role-based access, ensuring secure and informed decision-making. The platform harnesses machine learning, business intelligence tools, and real-time analytics to automate reporting, identify trends, and enhance institutional performance. Validation of EDUVISION was achieved through real-world implementation in a higher education environment, illustrating its effectiveness in improving strategic planning and administrative efficiency. A comparative analysis with existing frameworks underscores EDUVISION's scalability, accuracy, and usability advantages. This research contributes to the evolving field of educational data analytics, presenting an innovative solution to bridge the gap between raw data and actionable insights in academic institutions.
CycloCast – Predicting Cloud Formations in Cyclonic Conditions Using Indian Satellite Imagery
CycloCast is a deep learning-based forecasting model which predicts the future development of cyclonic weather cloud patterns from the satellite imagery taken from ISRO INSAT Series of satellites. Using a ConvLSTM network, it predicts future developments of cloud patterns at every 30 minutes based on a single input image sequence. The potential of predicting cloud coverage patterns makes CycloCast a valuable tool for meteorologists and disaster management authorities. In this paper we detail the data collection methods, data processing approaches such as adaptive thresholding and cyclone-based cropping, and the training approaches to achieve stable forecasting performance. CycloCast achieves a very high level of performance in forecasting cloud coverage patterns with a Cloud Coverage % of 85.6 and a Structural Similarity Index (SSIM) of 0.7765.
Falcon Face: Real time Face Reconstruction and Recognition
In modern forensic science, identifying suspects without photographic evidence remains a significant challenge. This paper introduces an innovative automated system that converts witness-provided textual or verbal descriptions into facial sketches and matches these with a pre-existing facial image database. Through the integration of machine learning and computer vision techniques, the system generates realistic sketches and utilizes facial recognition algorithms to find potential identity matches. This approach is aimed at increasing both the speed and accuracy of criminal investigations, especially in scenarios where descriptions vary in detail or clarity.
Multimodal Depression Detection Using Textual and Visual Cues: A Machine Learning Approach with the DAIC-WOZ Dataset
Depression is a prevalent mental health disorder affecting millions worldwide, yet remains underdiagnosed due to various barriers in clinical seFngs. This research proposes a novel multimodal machine learning framework that leverages both textual and visual behavioral indicators to detect depression. Utilizing the Distress Analysis Interview Corpus Wizard-of-Oz (DAIC-WOZ) dataset, our approach combines natural language processing techniques to analyze linguistic patterns with computer vision methods to capture non-verbal cues. The multimodal model achieved significantly higher performance (F1-score: 0.89) compared to unimodal approaches (text-only: 0.76, visual-only: 0.72), demonstrating the effectiveness of integrating multiple data modalities. Key depression indicators identified include specific linguistic patterns (increased negative emotion words, first-person singular pronouns) and visual markers (reduced facial expressivity, decreased eye contact). This research contributes to the emerging field of automated depression screening tools that could supplement clinical diagnostics, particularly in telehealth seFngs where in-person assessment is limited. Ethical considerations regarding privacy, bias, and appropriate implementation contexts are discussed.
Cutting-Edge Real-Time System for the Detection of AI-Generated and Manipulated Video Content
This study provides a social media network inspired by Instagram that incorporates an integrated AI-based system for video tampering detection. For deepfake signatures, the system analyses submitted video information using MesoNet, a compact convolutional neural network. To guarantee the authenticity of the content, detected altered videos are immediately marked with a visible watermark before being shared. To create a safe, scalable multipage application, the project makes use of Node.js, MongoDB, Python, and MoviePy. Results reveal 85–90.
Disaster Damage Assessment and Response Framework
In recent years, the frequency and impact of natural disasters have escalated, underscoring the need for efficient damage assessment and response frameworks. This paper proposes a novel approach that combines advanced image processing techniques, machine learning algorithms, and real-time data aggregation to create a comprehensive disaster damage assessment and response system. Using input from camera-based imagery and multi-source data streams, our framework rapidly identifies damage levels, enabling swift decision-making. This system aims to optimize resource allocation, support first responders, and improve situational awareness during crisis events. The proposed framework is evaluated on various disaster scenarios to assess its effectiveness in enhancing response efficiency and accuracy.
AI-Driven Task-Specific Hardware Optimization: Profiling Llama 3.2’s CPU Performance in Multi- Prompt Inference Workflows
This research presents an in-depth hardware compatibility evaluation of Meta's Llama 3.2 model on ten varied tasks—from long-context summarization and code generation to multi-turn dialogue and structured output generation—to discern performance trends and computational bottlenecks. Outputs show that hardware requirements differ widely by task type: long-context processing (1,915 input tokens) maximizes memory bandwidth (34% utilization), whereas code generation (659 tokens) and creative storytelling (2,321 tokens) maximize Central Processing Unit (CPU) parallelism (355% utilization on 4 cores). Multi-turn dialog shows growing latency (45 → 58 seconds) and token output increase (115→846 tokens) due to the compounding overhead of context retention, highlighting inefficiency in recurrent attention mechanisms. Repetitive work (1,000 "A"s) shows excellent token throughput (373 tokens/47 seconds), in contrast with mathematical calculations (square root of 123,456,789 × π), which overwhelm CPUs out of proportion (382% utilization) with trivial outputs, indicating Large Language Models (LLM) weakness in numeric accuracy. Ordered outputs (JSON) and translation work further reveal formatting-centric CPU overhead (355–382%), with tax policy questions (1,110 input tokens) uncovering context-parsing latency (53 seconds to process 148 tokens), highlighting semantic extraction inefficiencies from high-density texts. These results develop LLM work in three fronts: (1) Hardware Optimization, promoting task-specific settings (code/math with multi-core CPUs, plenty of RAM for context-intensive processes); (2) Model Architecture, calling for enhanced context handling and arithmetic blocks; and (3) Deployment Strategy, emphasizing resource allocation in keeping with use case operationalization (e.g., memory over legal/text analysis versus clock speed over creative applications). By correlating task types with hardware profiles, this research offers a framework for LLM scalability optimization, inference cost reduction, and informing future research into energy-efficient designs. The research highlights the need for interdisciplinary collaboration between Artificial Intelligence (AI) researchers and systems engineers to close the gap between algorithmic innovation and hardware capabilities, enabling sustainable large-scale LLM adoption.
Visiobot: Conversational Image Recognition Chatbot
As artificial intelligence advances, new possibilities for conversational agents have been made possible by the combination of computer vision and natural language processing. A thorough analysis of the VisioBot, a conversational chatbot made for image recognition tasks, is provided in this survey. In order to comprehend how VisioBot processes and interprets images in real-time conversations, we look at the different approaches, frameworks, and architectures used in the system. The survey examines the main obstacles to integrating language and vision models, such as multimodal data processing, captioning images, and visual input user interaction. Furthermore, we evaluate the present state of image recognition chatbots and pinpoint research gaps in areas like scalability, accuracy, and contextual awareness. This survey intends to give researchers and developers working in the domains of artificial intelligence and human-computer interaction a thorough grasp of VisioBot's potential uses and future development by examining recent developments in this area.
Personalized Learning through AI: Integrating NLP and Cloud Solutions for an Adaptive Education System
In today's fast-paced teacher scene, customary examining and learning procedures fight to meet the demands of progressed instruction. Understudies and enthusiastic followers face challenges in comprehending, summarizing, and meddling with the unending whole of information in books. The proposed solution introduces a dynamic AI-driven platform that revolutionizes the learning and study experience. This paper proposes a SmartQA (Smart Question Answering) method to improve learning results and openness for clients with changing learning capacities by giving personalized instruction, intuitive learning apparatuses, and real-time summarization. Coordination machine learning techniques such as NaturalLanguage Processing (NLP) and Google Cloud organizations, as well as the suggested method SmartQA, ensure flexibility, strength, and advanced AI capabilities. In addition, the process contributes to teacher development by speaking to the potential of AI to grow customary learning strategies. Other than that, our viability course of action underscores our commitment to inclusivity, data-driven encounters, and developing a sense of community among clients. Through the suggested SmartQA method, this article focuses on creating a long-lasting journey for data and insights, broadcasting a present-day chapter inside the history of instruction.
Skin Cancer Detection-Technological Innovation in Personalized Risk Assessment and Early Warning System and Empowering Healthcare Providers
Skin cancer is one of the most deadly cancers and a major cause of global mortality. However, early detection can greatly lower death rates. Traditionally, skin cancer is identified through visual examination, which can sometimes be inaccurate and miss subtle early indications. Early and precise diagnosis plays a key role in enhancing patient outcomes and preventing the progression of skin cancer. Recently, deep learning techniques have shown promise in assisting dermatologists with accurate diagnoses at earlier stages. Despite these advancements in dermatology, challenges in achieving quick and accurate diagnoses of skin cancer remain. The integration of 3D imaging with Texture-Based Processing (3D TBP) represents a cutting-edge approach to skin cancer detection. By analyzing texture and patterns within 3D images, advanced algorithms can identify slight texture variations that may indicate cancerous changes—variations that are often missed in traditional 2D analysis.
rCounting the Future: Deep Learning-Based Electric Vehicle Estimation from Video Surveillance
Research and policymaking activity on sustainable transportation thrust the importance of electric vehicle (EV) existence and distribution within urban environments. A computer vision framework presented in this study enables video surveillance data to determine EV counts within particular geographical locations. The methodology uses YOLOv11 license plate recognition together with YOLOv5 object detection to explore electric vehicle plates during examination and vehicle identification processes. A precise vehicle and license plate cropping process maintains accuracy. Processed information is applied to the recognition of electric versus non-electric vehicles. The system proves efficiency by using video surveillance from public roads to identify vehicles, recognize their license plates, and count electric vehicles. An intricate two-stage system, “search” and “identify” phases, ensures proper vehicle and license plate cutting. Further processing of the data completes the identification of conventional and electric vehicles. The system shows its functionality by using public road video footage to identify cars and extract license plates before counting electric vehicles. The investigation demonstrates that deep learning enables both traffic monitoring through analysis and environmental assessment.
Machine Learning Model for Efficient Botnet Attack Detection and Classification
Botnets constitute a significant threat to cybersecu- rity, enabling large-scale malicious operations such as distributed denial-of-service attacks, data exfiltration, and unauthorized system access. This research presents a machine learning-based framework for the detection and classification of botnet attacks, utilizing Decision Tree, XGBoost, and Logistic Regression al- gorithms. The UNSW-NB15 dataset is employed, with distinct training and testing splits to ensure model generalization and to prevent overfitting. Feature selection techniques are applied to en- hance model performance and reduce computational complexity. Model evaluation is conducted using confusion matrices and Re- ceiver Operating Characteristic–Area Under Curve (ROC-AUC) metrics to provide a comprehensive assessment. Experimental results indicate that ensemble methods, particularly XGBoost, deliver superior performance in accurately detecting and cate- gorizing botnet traffic across various attack types. The findings highlight the effectiveness of machine learning approaches in improving the robustness and scalability of network intrusion detection systems.
Agrocraft: Farmers' and Consumers' Direct Market Access
An Agrocraft site refers to an electronic site where farmers can sell and buy agricultural products to consumers. The system presents an easy interface through which the farmers can put their produce online while enabling viewers to view items and services of the buyers and buy them online. This piece is about the conceptualization and benefit of having a web site dedicated to the selling of farm produce by the farmers directly to consumers. The platform acts as a middleman between farmers and consumers, bringing convenience, transparency, and sustainability to agricultural transactions.
Automated Pomegranate Disease Identification: A Deep Learning Pipeline with FANLM-FKM and HBOA
Automated, Early and accurate identification of pomegranate Fruit diseases is vital for sustainable crop management and yield optimization. This research proposes a novel deep learning-based pipeline that integrates image preprocessing, instance segmentation, feature extraction, and hybrid classification for robust pomegranate disease diagnosis. The preprocessing stage employs a two-step enhancement technique combining Fuzzy Adaptive Non-Local Means with Fuzzy K-Means (FANLM-FKM) denoising followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) for improving visual clarity while preserving structural details. Segmentation of diseased regions is performed using a fine-tuned Mask R-CNN model trained via the Detectron2 framework. Features are extracted from segmented images using the average pooling output of a pretrained ResNet-50 network. To reduce dimensionality and enhance discriminative power, the feature space is refined using a Hybrid Binary Optimization Algorithm (HBOA). The final classification is conducted using a custom-trained CNN optimized on the HBOA-selected features. Extensive evaluations reveal that the proposed CNN-HBOA classifier achieves high accuracy in distinguishing among four disease classes Anthracnose, Bacterial Blight, Cercospora, and Healthy FruitsF. The complete pipeline demonstrates strong generalization capabilities and holds promise for deployment in real-time agricultural diagnostic tools. This study outlines the methodology, implementation, and results, highlighting its potential agricultural impact.
CarbonDie – Reveal. Track. Reduce. The Unseen Carbon.
Climate change is no longer a distant threat—it's fueled daily by personal lifestyle choices such as commuting methods, electricity usage, and waste disposal. While these individual actions may seem trivial, collectively, they contribute substantially to the planet’s carbon emissions. Yet, most people have no clear method to assess or comprehend the environmental impact of their routines. To address this challenge, we developed Carbon Die – The Unseen Carbon, a next-gen tool that leverages Artificial Intelligence (AI) and Augmented Reality (AR) to provide users with real-time, visual insights into their carbon emissions. By analyzing daily behaviors, the system delivers a tailored estimate of one’s carbon footprint and offers achievable strategies for reduction—often enabling users to cut down 2 to 5 tons of CO₂ per year. Beyond individual benefit, the platform aligns with national goals like the Bharat Zero Emission Challenge 2030, encouraging adoption of eco-friendly and locally produced goods under the Make in Bharat movement. Initial testing shows that the platform is user-friendly and highly adaptable, making climate-conscious living more accessible and actionable for everyone.
Smart Relief: Aadhar-Integrated Disaster Management System
An Aadhar-Based Disaster Management System leverages India's unique identification system, Aadhar, to improve disaster response and recovery. Aadhar, a biometric identification system, assigns a unique 12-digit number to every Indian resident, facilitating a centralized and reliable database. Integrating Aadhar into disaster management allows authorities to access essential data swiftly, enhancing coordination during emergencies. An Aadhar-based system allows for better tracking of affected populations, making it easier to manage evacuations and ensure that essential services are provided to the most vulnerable. This integration can also support post-disaster recovery by linking affected individuals to financial aid, insurance claims, and government support programs using their unique identification.
The study aims to investigate the use of AI in the culinary arts, particularly the creation of new recipes from pre- existing ones. The article provides a comprehensive overview of the history of artificial intelligence in the culinary arts as well as current uses in the recipe creation space The authors encountered a variety of recipe and cuisine-specific suggestions. None, though, concentrated on Recipe Generation, which would be a novel area to investigate. The promise of artificial intelligence (AI) approaches to automate and improve culinary inventiveness has drawn a lot of attention to recipe generation in recent years. An overview of the most recent techniques and difficulties in AI-based recipe generating is given in this article. Natural language processing (NLP) models and recommender systems for recipe development are among the methods we cover Along with ethical issues in formulation development, key challenges such component combinations, cultural sensitivity, and incorporating user feedback are addressed. We also look at the possible effects of AI technology on the culinary industry as well as future research opportunities.
Mental health is a critical aspect of overall well- being, yet barriers such as stigma, limited access, and high costs often prevent individuals from seeking timely support. MindMend: A Mental Health Support System aims to bridge this gap by providing a comprehensive platform offering virtual therapy sessions, AI-driven emotional support, mood tracking, and a crisis detection module that identifies high-risk cases and escalates them to professional counselors or helplines for immediate intervention. Leveraging cutting-edge technology, the system facilitates personalized mental health care through user-friendly interfaces and intelligent algorithms. The virtual therapy sessions create a safe and anonymous space for users to explore their emotions and gain insights. The AI component analyzes user inputs, recognizes emotional cues, and provides tailored resources and empathetic responses, ensuring immediate support. The mood tracking feature also enables users to monitor their emotional patterns over time, empowering them to make informed decisions about their mental health journey. This paper reviews the functionalities, technical architecture, and societal impact of MindMend, highlighting its potential to address the growing mental health crisis. By integrating technology and empathy, MindMend aspires to create a world where mental health support is accessible to everyone, anytime, anywhere.
The Gym Management System incorporating a Recommendation System and Image Processing is an innovative solution aimed at transforming the management and user experience in fitness canters. This system employs AI driven recommendation algorithms to provide personalized workout plans, dietary suggestions, and fitness routines tailored to individual user preferences, goals, and performance data. The dynamic nature of the recommendation system ensures continuous adaptation to user progress, enhancing effectiveness and engagement. With the integration of image processing technologies, the system introduces features such as facial recognition for secure and efficient member authentication, pose estimation for monitoring exercise form and posture, and real-time feedback to improve workout accuracy and reduce injury risks. The platform automates essential administrative tasks such as membership management, attendance tracking, and billing, while offering trainers a centralized dashboard to monitor member progress and optimize resource allocation
Microservices CI/CD Pipeline on Cloud Computing (QR Code Generator)
This research paper presents the design and deployment of a DevOps-based Continuous Integration and Continuous Deployment (CI/CD) pipeline for a Python-based QR Code Generator web application. The application, developed using Flask and PyWebIO, is containerized using Docker and deployed using Docker Swarm for scalability and high availability. The CI/CD pipeline is orchestrated by Jenkins, with source code hosted on GitHub. Infrastructure provisioning is automated using Terraform, while Ansible manages configuration and deployment. The application is hosted on AWS EC2 instances, ensuring cloud scalability. For monitoring, Nagios is used to track system health and uptime, while Datadog provides real-time performance analytics and alerting. This project demonstrates a full DevOps lifecycle—from code commit to production deployment—leveraging open-source tools and cloud infrastructure. The implementation enhances deployment speed, operational visibility, and infrastructure reliability in a production-like environment, serving as a model for modern, automated web application delivery.
AI-Powered Cyber Security Protection Tool
This report delves into the landscape of contemporary cybersecurity threats, with a focus on the dynamic nature of DeepFakes, phishing schemes, social engineering ploys, and the persistent issue of malware. It underscores the challenges posed by DeepFakes, which utilize sophisticated AI to create convincing but false content, leading to risks like identity fraud and the spread of misinformation. The report also examines the role of detection technologies, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in identifying these threats, while noting the ongoing challenge of detecting high-fidelity DeepFakes. It mentions the utility of tools like Face Forensics++ in training detection models to improve their accuracy. Phishing, a method of acquiring sensitive information through deceit, is another area of concern highlighted in the report. It discusses the use of URL analysis and resources like Phish-Tank to detect and prevent phishing attacks. The report also addresses social engineering, which manipulates individuals into divulging confidential information, and emphasizes the need for Natural Language Processing (NLP) models to detect such manipulations in communication channels. Furthermore, the report explores the realm of malware, including dangerous forms like ransomware and spyware, and how they pose ongoing challenges to cybersecurity. It details the application of machine learning and deep learning in malware detection and the use of tools such as Virus Total and Cuckoo Sandbox to enhance detection capabilities through comprehensive scanning and behavioral analysis. The report concludes by recognizing the potential of current detection technologies while also pointing out the challenges that remain, such as the need for real-time threat detection and raising user awareness. It stresses the importance of developing integrated and adaptive cybersecurity solutions that can evolve in response to the ever- changing threat landscape.
Wireless Predictive Maintenance for BLDC Fans using STM32
This paper demonstrates the feasibility of combining STM32 with Edge AI - an integrated AI processing on the device, enabling real-time decisions without internet dependency - to achieve accurate and low-latency predictive maintenance, where quick detection and response are critical to prevent costly machine failures, especially in industrial settings. Predictive maintenance is a preventive approach that ensures the smooth functioning of a machine by avoiding breakdowns. This is done by detecting abnormalities in the normal day-to-day functioning of the machine. This enables minimizing downtime and maintenance costs as well as improves the production/operation efficiency of the machine. Edge AI is a unique tool that provides real- time anomaly detection with immediate responses instead of relying on cloud-based alternatives, which can introduce delays. By analyzing data from the GY-521 gyroscope-accelerometer module, the system identifies different fan behaviours and categorizes them into three different states: "Normal condition", "Maintenance required soon", and "Critical fault". The ESP32 hosts a web server that displays the fan’s condition through a user-friendly interface, allowing remote monitoring.
A Review on Flight Control and Actuation Systems
The majority of modern aircraft depend heavily on flight control and actuation systems for stability, maneuverability, and overall safety. These systems control the motion of the control surfaces through various forms of actuation technologies, ranging from hydraulic, electromechanical, to electro-hydrostatic actuators. This research explores the principles of operation, structures, and uses of such actuators and how they assist in flight control. Along with this, fault-tolerant systems, management of redundancy, and future directions such as AI-driven flight control and more electric aircraft (MEA) are examined. As techniques are improved technically, the aviation industry is gradually shifting towards lighter, more dependable, and more efficient actuation systems, improving the overall aircraft performance and safety. The article delves into these changing directions and how they can have repercussions for future flight control practices.
Feasibility of Hydrogen Blends as a Fuel for CI Engine
The transition to sustainable energy sources has intensified the exploration of alternative fuels for internal combustion engines (ICEs). Among these, hydrogen stands out due to its abundance, clean combustion properties, and potential to reduce greenhouse gas emissions. This paper discusses the feasibility of hydrogen as a fuel for ICEs, highlighting its physical and chemical properties, storage challenges, and the modifications required in engine design for optimized performance. Furthermore, it evaluates the environmental benefits and economic implications of adopting hydrogen-based technologies in the transportation and energy sectors. While hydrogen presents significant promise, addressing its production efficiency, distribution infrastructure, and safety concerns remains crucial for its widespread adoption.
PVDF-Reinforced Hollow Fiber Membranes for Enhanced Biogas Methane Enrichment
Biogas is a promising renewable energy source, and its methane content is critical for efficient energy utilization. Membrane-based separation technologies offer a sustainable and energy-efficient method for methane enrichment. This study explores the application of polyvinylidene fluoride (PVDF) hollow fiber membranes reinforced with advanced structural properties to improve the selectivity and permeability of methane from biogas. The performance of PVDF membranes was evaluated for their gas separation efficiency, mechanical stability, and long-term durability under varying operational conditions. Results demonstrated that PVDF reinforcement significantly enhances methane recovery while minimizing CO₂ and other impurities. The findings underscore the potential of PVDF hollow fiber membranes as a cost-effective and scalable solution for upgrading biogas into high-purity methane.
Remote Sensing and GIS-Based Flood Mitigation Model Using Maps
Floods pose significant risks to human life, infrastructure, and the environment. Advanced geospatial technologies such as Remote Sensing (RS) and Geographic Information Systems (GIS) provide effective tools for flood hazard assessment, mapping, and mitigation. This study integrates RS and GIS to develop a flood mitigation model that identifies flood-prone areas using satellite imagery, digital elevation models (DEMs), and hydrological data. The model aids in early warning systems, urban planning, and disaster management by assessing flood risk zones and recommending mitigation strategies. The results highlight the effectiveness of geospatial techniques in improving flood resilience and disaster preparedness.
Sustainable Urban Development: The Role of Smart and Energy-Efficient Buildings: A Review
The accelerating pace of urbanization has significantly increased global energy demand, placing immense pressure on environmental sustainability. In response, the integration of smart technologies into urban planning has emerged as a promising solution for optimizing energy consumption and promoting eco-friendly development. This paper provides a comprehensive review of innovative approaches such as artificial intelligence (AI), the Internet of Things (IoT), smart grids, and renewable energy systems in shaping energy-efficient cities and buildings. Drawing from real-world case studies—including Singapore’s smart city framework, Masdar City’s sustainable urban planning, and The Barcelona city building design—the paper demonstrates how these technologies are applied in practice. While their impact is transformative, adoption remains limited due to challenges like cyber security vulnerabilities, high implementation costs, and policy misalignments. To overcome these obstacles, cross-sector collaboration is essential. Future research directions include leveraging AI for predictive energy management, enabling decentralized energy markets through block chain, and building climate-resilient infrastructure.
Redesigning Rural Toilets for Sustainability and Soil Health: Beldarwadi Case Study
This comprehensive paper delves into the intricate challenges faced by Beldarwadi village in Maharashtra, India, where sanitation issues intersect with agricultural practices. Through a meticulous household survey and innovative technologies like Arduino-based monitoring systems, the study reveals a stark lack of awareness regarding hygiene maintenance, resulting in prevalent open defecation. The research proposes transformative solutions, emphasizing the untapped potential of human waste as a nutrient resource for soil conditioning and pesticide production. Infrastructural challenges, limited connectivity, and illiteracy further compound the village's struggles. Statistical analyses provide insights into the socio-economic fabric of Beldarwadi, laying the groundwork for tailored interventions. The monitoring of toilet usage exposes a disheartening reality, with 80% of the population still practicing open defecation, revealing deep-rooted behavioral challenges. The paper extends its focus to farmers' perspectives on fertilizer usage, highlighting a predominant reliance on chemical fertilizers and a cautious openness to alternatives derived from human feces. The conclusion emphasizes the need for targeted awareness campaigns, infrastructural development, and a nuanced approach to agriculture, envisioning a sustainable future for Beldarwadi.
Advanced Oxidation Process for Wastewater Treatment Using H2O2 and UV Method
Advanced oxidation processes (AOPs) are being identified as potential technologies for wastewater treatment and improvement of conventional biological treatment processes, especially for wastewater with highly toxic and poorly biodegradable organic compounds. In the present work, wastewater samples were obtained from Kala Odha in Ichalkaranji. Several parameters of the wastewater, such as pH, turbidity, total dissolved solids (TDS), total suspended solids (TSS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), total phosphorus, and total alkalinity, were determined by standard methods. The best dose of hydrogen peroxide (H₂O₂) and the best treatment time were established by using a photochemical reactor in batch mode. The research revealed that the highest COD removal efficiency of 74.41% was obtained with a concentration of 300 mg/L of H₂O₂ after a reaction time of 90 minutes, from an initial COD of 1720 mg/L.To further optimize the treatment process, COD tests were also performed at different time intervals. The results showed that a reaction time of 75 minutes produced the maximum COD removal efficiency of 76.27% at the optimum dose of 300 mg/L of H₂O₂ with an initial COD of 2360 mg/L. Based on the findings, it is clear that the combined application of H₂O₂ and UV methods is a promising solution for efficient removal of COD from wastewater.
Hydraulic Modeling and Design of Water Distribution System – A Case Study
Water is an essential resource for human survival, economic development, and environmental sustainability. As urbanization and population growth continue to rise, ensuring an efficient and reliable water distribution system becomes a critical challenge. A well-designed WDS plays a crucial role in delivering adequate and consistent water supply while minimizing losses, optimizing pressure, and ensuring long-term sustainability. This research focuses on the design and optimization of a water distribution network using modelling and simulation techniques to enhance system performance and reliability. Using WaterGEMS software, the research focuses mainly on calibrating the current WDS in this area and analysing different operating conditions.
Sustainable Wastewater Treatment with Ultrasound Approach
The change in environmental conditions are because of global warming, misuse of natural resources which further stresses on conservation of conventional sources and lead to optimize the usage of natural resources. The industrial effluent waste generated in the distillery analyser column is termed as Spent Wash is also listed amongst highest polluting industry. And therefore stringent norms against environmental protection also ask for recycling of industrial effluent. Traditionally, spentwash was treated with aerobic, anaerobic, incineration etc. processes. Recently AOPs gain a popularity in waste water treatment, such as photocatalysis, fenton process, ultrafiltration etc. in this article ultrasonication treatment was studied for the treatment of spentwash by varying parameters such as pH & concentration. It is found that this technique is efficient for the wastewater treatment.
A Comparative Review of LEED and GRIHA Green Building Rating Systems
As the global demand for sustainable construction practices increases, green building certification systems like LEED (Leadership in Energy and Environmental Design) and GRIHA (Green Rating for Integrated Habitat Assessment) play a critical role in shaping the built environment. While both systems aim to reduce environmental impact, their methodologies, focus areas, and contextual relevance differ significantly. This paper presents a comparative review of LEED and GRIHA, analyzing their rating structures, implementation mechanisms, environmental performance, and adaptability. Through literature synthesis and case study evaluation, the study highlights the strengths and limitations of both systems and proposes pathways for integration and improvement. The findings suggest that while LEED offers scalability and global recognition, GRIHA excels in regional applicability and climate responsiveness. Together, they provide valuable frameworks for sustainable development, particularly in emerging economies like India.
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Microwave Sensor for Water Quality Testing
Water quality monitoring is vital to environmental sustainability, public health and industrial applications. Testing water quality requires laboratory analysis, which is both expensive and time-consuming. A cost-effective, real-time microwave sensing system for water quality assessment is presented in this study. We make use of various microwave resonators to measure water pollutants such as nitrates, phosphates and heavy metal substances, as well as pH levels. The suggested sensor array is capable of functioning at frequencies up to 1GHz to 10 GHz. The use of a GHz bandwidth allows for improved detection accuracy through broadband dielectric spectroscopy. Experimental evidence indicates that microwave sensing can detect and discriminate against a wide range of water contaminants with high sensitivity. This effort helps to develop compact, reusable and adaptable solutions for monitoring water quality.
Intelligent Diet and Exercise Recommendation System Using AI
Maintaining a healthy lifestyle is essential for preventing cardiovascular diseases, obesity, and metabolic disorders. Traditional diet plans often fail due to their generic nature and lack of real-time adaptability. This research presents an Intelligent Diet and Exercise Recommendation System that utilizes Optical Character Recognition (OCR), Natural Language Processing (NLP), and Machine Learning (ML) to provide personalized dietary recommendations based on cholesterol profiles extracted from medical reports. The approach includes automated cholesterol extraction, diet classification using ML models, and exercise recommendations via decision trees and K-Nearest Neighbors (KNN). A Spring Boot-based web application offers an interactive dashboard for real-time health insights. By leveraging predictive analytics and AI-driven insights, this project bridges the gap between static diet plans and personalized, dynamic health recommendations.
A small wearable assistive technology for patients with speech or physical disabilities is presented in this paper. Through embedded sensors, the system records wrist motions and converts them into real-time, personalized messages that are shown on a mobile application. The device, which was designed with the Seeed Studio XIAO nRF52840 microcontroller and MPU9250, combines wireless communication, Firebase- based storage, and gesture recognition. The goal of the solution is to facilitate proactive health monitoring and enhance communication between patients and caregivers. Its accuracy, effectiveness, and usability in hospital settings are confirmed by real-world testing.
Bridging the Gap: Integrating Science, Technology, and Humanities in Cross-Disciplinary Education
In an era of rapid technological advancement and complex global challenges, cross-disciplinary education has emerged as a crucial approach to fostering innovation, critical thinking, and problem-solving. This paper explores the integration of science, technology, and humanities in education, highlighting the benefits of interdisciplinary learning in preparing students for diverse career paths. It examines the theoretical framework behind cross-disciplinary education, the need for integration, and the challenges faced in its implementation. Additionally, strategies for effective curriculum design and successful case studies from global institutions are discussed. The paper concludes with future directions for interdisciplinary education, emphasizing its role in shaping well-rounded professionals equipped to tackle real-world problems.
Artificial Intelligence (AI) to Enhance Students' English Communication Skills
This paper examines the role of AI in improving English communication skills, highlighting its benefits, challenges, and prospects. The study is grounded in linguistic and educational theories, illustrating how AI facilitates pronunciation improvement, vocabulary expansion, grammar correction, and contextual language use. Through an analysis of case studies of AI-driven language learning applications, including Duolingo, Grammarly and ChatGPT, the researcher demonstrates their effectiveness in enhancing student engagement and learning outcomes. Despite the transformative potential of AI in language education, ethical considerations, data privacy, and accessibility concerns are also stated. The paper concludes with recommendations for optimizing AI integration in language learning to ensure equitable and effective educational experiences for diverse learners.
From Local to Global: The Transformation of Coding Through Online IDEs
The evolution of online Integrated Development Environments (IDEs) has been driven by increasing demands for remote and collaborative software development. These platforms provide seamless coding experiences by eliminating local setup requirements, enabling real-time collaboration, and supporting various programming languages. This paper examines the architecture, essential features, and technologies behind modern online IDEs, with emphasis on scalability, efficiency, and user experience. We explore emerging trends including AI-assisted coding, cloud-based execution engines, and personalized user profiles. The paper addresses challenges such as latency performance, data privacy protection, and handling resource-intensive operations. We conclude by examining the future potential of online coding platforms as essential tools for developers, educators, and learners, highlighting their role in democratizing access to coding resources and accelerating innovation.
Smart Tourism with AR Navigation: Bridging Cultural Exploration and Technology
VeezAR is a mobile-based AR-AI solution for smart travel planning, integrating AR for real-time navigation. Developed for Android using Java and Firebase, it leverages AR-Core, and Unity, for immersive navigation and an AI personalized recommendation. Key features include hybrid 2D-AR mapping, AI travel planner, and interactive gesture controls. AI chatbots ensure scalability, while Firestore caching supports real-time updates. Preliminary results indicate accurate recommendations and an engaging experience. despite challenges like device compatibility and internet dependency.
Transforming the Rental Market with Smart and Sustainable Solutions
Rent Bazar is an intuitive Flutter-based application that facilitates the renting of goods and services by connecting individuals who need items with those willing to rent out their possessions. The app provides a seamless platform where users can browse, search, and rent various products, including furniture, electronics, vehicles, real estate, and more. Additionally, Rent Bazar allows users to list their items for rent, maximizing the utility of their unused belongings. With secure payment options, a user-friendly interface, and location-based search features, Rent Bazar simplifies the process of renting, making it convenient, cost-effective, and environmentally sustainable. By promoting the reuse of goods, Rent Bazar fosters a community-driven sharing economy, reducing waste and encouraging efficient resource utilization.