Multidisciplinary Journal of Research in Engineering and Technology
Researcher Connect Innovation and Impact Private Limited
Drug Discovery for Thyroid Cancer Using CNN and GNINA
Thyroid cancer often caused by the BRAF V600E mutation, which activates the MAPK/ERK signaling pathway that promotes tumor growth. Targeting this mutation using kinase inhibitors such as Vemurafenib, Dabrafenib, and Sorafenib has shown effectiveness. Traditional docking tools like AutoDock Vina have limitations in accurately predicting binding affinities. To overcome this, the project uses GNINA, a deep learning-based molecular docking framework that applies Convolutional Neural Network (CNN) scoring functions to improve protein–ligand interaction predictions. Through redocking, cross-docking, and whole docking approaches, to identify the most effective treatments, we evaluated how well different inhibitors bind to the BRAF V600E protein. The study shows that by blending deep learning with molecular docking, researchers can fast-track the discovery of new, more effective therapies for thyroid cancer.
Neurosense: A Machine Learning Framework for EEG Motor Imagery Classification and Visualization
Electroencephalography (EEG)- grounded Brain- Computer Interfaces (BCIs) grease anon-invasive communication link between the mortal nervous sys- tem and external bias, presenting considerable pledge in neurorehabilitation and assistive technologies. nonethe- less, precise bracket of motor imagery( MI) signals con- tinues to be a redoubtable challenge owing to natural signalnon-stationarity, lowered signal- to- noise rates, and considerableinter-subject variability. This paper describes the design and full testing of NeuroSense, a platform for classifying EEG motor imagery from launch to finish. The system uses a structured signal processing channel that includes bandpass filtering, time- grounded segmentation, Common Spatial Pattern (CSP) point birth, and a cali- brated Radial Base Function (RBF) Support Vector Ma- chine (SVM) classifier. During the development phase, sev- eral modeling strategies were delved, including a Genera- tive Adversarial Network (GAN)- grounded system for data addition and point improvement. This approach gave us useful experimental information, but the final system de- sign puts a streamlined CSP- SVM channel at the top of the list because it’s harmonious, easy to understand, and has strong empirical performance. The performing frame shows strong and harmonious bracket performance across subjects on standard EEG motor imagery datasets. Also, a web- grounded interactive interface makes it possible to see EEG data in real time, dissect Power Spectral viscosity (PSD), interpret CSP patterns, and make prognostications for each trial with an estimation of confidence. This makes it easier for both clinical interpreters and experimenters to use. The suggested system has a mean bracket delicacy of 84.4 and a Cohen’s kappa of 0.688, which shows that it works well and constantly across subjects.
Comprehensive Review on Adaptive Natural Language Interfaces for Autonomous Relational Database Schema Synthesis and Context-Aware Query Interpretation Using Advanced NLP Techniques
In this purposed system we provide a solution to a complex problem of building a system which can auto-matically create a database, populate it with data and re-spond to user queries. The system automatically creates a database based on user queries, and does entity extrac-tion, intent identification and automatically transforms nat-ural language input to SQL queries that can be executed on the database. The system also supports conversation and provides schema aware processing, supports query val-idation, and also includes data visualization. In order to provide these functionalities, we have integrated NLP tech-niques with state-of-the-art database systems. In addition, we have worked on multiple features that increase system performance. The main focus of this work has been to make database interactions easier for the users by reducing the gap between natural language and SQL. We have created a platform that supports all of these, and allows its users to provide inputs in natural language, and get results without having to write a line of SQL to interact with the database. This provides a scalable and efficient solution to manage data in various scenarios.
An AI-Based Framework for Personalized Educational Content Generation and Adaptive Assessment
The swift development of digital learning has required the replacement of the immobile teaching repositories with dynamic adaptive learning spaces. Although artificial intelligence has already started infiltrating the educational industry, the current systems often fail to be able to offer holistic personalization that considers not only the intellectual capacity but also the unique psychological profiles of specific learners. The paper suggests a holistic design of a personalized learning and assessment platform powered by AI that integrates Large Language Models (LLMs) with the elements of automated behavioral profiling and tools that enable accessibility to all. The proposed platform will also be capable of changing pedagogical strategies and content presentation on the fly to maximize student engagement by using natural language processing to understand student personality traits and learning styles. Moreover, the platform incorporates safe, decentralized data management mechanisms to safeguard confidential psychological and academic information. By carefully examining the modular structure of the system, the theoretical basis, and the hypothetical assessment plan, the paper will provide a strong base of the next generation educational technologies that can be effective, secure, and ethically responsible at the same time.
AgriScan AI: Real-Time Crop Disease Detection for Potato and Tomato
Farming is crucial for humanity’s food supply, yet many plant diseases continue to threaten crop yield. This paper presents the AgriScan AI smart system for disease detection in tomato and potato plants by image analysis. The system employs deep learning models like Convolutional Neural Networks for grouping healthy and diseased tomato and potato leaf images based on leaf pictures. We use ResNet and MobileNet with transfer learning to get things working more smoothly. Our service has a simple web interface where users can upload images and get their results instantly. Agriculture is an active field and this solution fits well because it is fast and accurate for on the spot farming decisions. This will help farmers avoid losses and maximize their crop yield.
VeriChain: Blockchain-Based Document Verification and Authentication System Using AI
Blockchain-based document verification systems provide secure and tamper-proof validation but have lim-itations like sensitivity to visual changes and no intelli-gent forgery detection. This paper presents VeriChain, a strong document authentication framework that combines color-invariant hashing and CNN-based forgery detection. A standard preprocessing pipeline ensures consistent hash generation despite differences in color, format, or scanning; a Convolutional Neural Network identifies tampered areas and structural problems. By using SHA-512 hashing, RSA-based signatures, and AI-driven validation, the proposed system improves accuracy, security, and reliability. Ex-perimental results show better performance compared to traditional blockchain-based verification methods, making VeriChain suitable for real-world use.
Quantum-Powered Vision Network for Intelligent Brain Analysis
Brain tumor detection from magnetic resonance imaging (MRI) plays a vital role in early diagnosis and effective treatment planning. However, conventional manual analysis and classical deep learning models often face challenges related to high computational complexity, long training time, and limited scalability when handling high-dimensional medical image data. This project presents a Quantum-Powered Vision Network for Intelligent Brain Analysis, which integrates classical image pre-processing with quantum-assisted machine learning techniques to improve classification performance. The proposed system follows a hybrid quantum–classical workflow that includes MRI image preprocessing, quantum data encoding using unary amplitude encoding, and quantum-assisted learning layers for feature extraction and classification. The model is designed to classify brain MRI images and identify tumor presence and levels efficiently. A structured methodology is implemented, and the working of the system is demonstrated through step-by-step output results. Experimental observations show that the proposed approach achieves reliable classification behavior while maintaining com-putational efficiency compared to purely classical methods. The results highlight the feasibility and potential of quantum-assisted learning techniques in medical image analysis. This project demonstrates that hybrid quantum-classical models can serve as a promising direction for future intelligent healthcare and diagnostic systems.
CareerPilot: Your AI-Powered Career Guidance System
Selecting an appropriate career path remains a significant challenge for students and job seekers due to limited access to personalized guidance and insufficient awareness of industry requirements. Traditional career counseling methods rely on generalized advice or academic performance metrics, which often fail to accurately represent an individual’s skills, interests, and career potential.[5] This paper presents CareerPi-lot, an artificial intelligence-based career guidance and learning platform that analyzes comprehensive user profiles to recommend suitable career paths alongside structured learning roadmaps. The system evaluates multiple attributes including skills, educa-tion, interests, experience, and resume data to predict career suitability through machine learning techniques. Additionally, the platform integrates an AI-based reasoning component using Google Gemini 2.5 Flash API that identifies skill gaps and gen-erates personalized multi-phase learning roadmaps comprising recommended resources, projects, and assessments. The model employs a soft-voting ensemble combining LightGBM and Random Forest classifiers trained on 2,100 synthetic user profiles distributed across 30 career categories. Features are extracted using TF-IDF text embeddings (300 dimensional skill features, 50-dimensional industry features) combined with one-hot encoded categorical variables and numerical indicators, resulting in a 365-dimensional feature space. The platform employs React and TypeScript for the frontend interface and Django with Django REST Framework for backend services. Experimental evaluation on unseen test data demonstrates 90% overall accuracy, 99% top-3 accuracy, and 0.93 weighted F1-score, validating the effectiveness of the proposed approach in bridging the gap between academic learning and industry demands.
AI-Based Medicinal Plant Detection System
Medicinal plants are really important for our health both in ways of healing and in modern hospitals.. Finding these plants can be very hard and it takes a long time a lot of work and you need to know a lot about plants. To make this easier we made a computer system that uses intelligence to identify medicinal plants by looking at pictures of their leaves. This system uses computer methods like Convolutional Neural Networks to look at the shape, texture, color and patterns on the leaves and sort them correctly. We also use a method called YOLO to find leaves even in complicated places. To make this work we take pictures of leaves clean them up with computer tools like OpenCV and then teach the computer to recognize them. We use a way of teaching the computer called transfer learning, which helps it learn faster with fewer pictures. We made a website where people can upload pictures of leaves and get information about the plant and its healing properties. This system is really good, for doctors, farmers and people who study plants because it is very accurate and works fast. Even though it is very good we still have problems to solve like getting types of pictures and making it work in different environments. In general our system shows how computers can help make finding medicinal plants easier more accurate and faster which can help us have healthcare and protect the environment. Medicinal plants are very important. Our system can help people who work with medicinal plants every day.
This paper discusses the actual implementation of an AI-based e commerce referral system with a token-based reward program. The implementation is aimed at allowing the customers to become affiliates and receive tokens via the use of promo codes to refer other people. The hybrid model of AI employs the machine learning techniques for identifying fraudulent transactions, secure promo code management, and token distribution. The paper highlights the aspects related to transactional, security, and learning functionalities of this system implementation.
A Biomechanics Motion Analysis System for Boxing Technique Evaluation using Computer Vision
This paper presents a computer vision-powered boxing technique analysis system designed to assist athletes in improving their boxing form. The proposed system utilizes the MediaPipe pose estimation framework to extract upper-body skeletal landmarks and analyze boxing movements in real-time. By computing precise joint angles and body rotation metrics, the system compares user techniques against pre-computed professional reference poses for common boxing punches (jab, cross, hook, uppercut). We propose a rule-based system that calculates joint-specific and overall technique accuracies, providing actionable, detailed feedback on form and alignment. The system features a comprehensive frontend interface supporting real-time camera analysis and side-by-side video comparisons, incorporating robust performance optimizations for memory management and frame processing.
The increasing volume of geoscience-related data from research papers, environmental reports, and satellite observations has created challenges in efficient knowledge retrieval and interpretation. Traditional keyword-based search systems fail to provide context-aware and precise responses for complex queries. This paper presents a Retrieval-Augmented Generation (RAG)-Powered Geoscience Assistant that integrates vector-based retrieval with large language models (LLMs) to generate accurate and context-aware answers. The system processes user-uploaded geoscience documents, converts them into embeddings, and stores them in a vector database for semantic retrieval. A Streamlit-based interface enables interactive querying, while the backend utilizes embedding models and LLM APIs to deliver grounded responses. Experimental evaluation demonstrates improved accuracy, reduced hallucination, and enhanced user experience compared to traditional search systems. The proposed system provides an efficient and scalable solution for domain-specific knowledge retrieval in geoscience.
Cyber I: A Post-Quantum Secure AI Architecture for Cyber Threat Detection and Response
Cyber I is a kind of Security Operations Center that works on its own. It is made to find pretend to be and stop Advanced Persistent Threats. Cyber I uses a group of intelligence agents and standardized telemetry to do this. The system puts together threat detection in one place. It uses two ways of looking at things to find threats: one way is with help from people and the other way is without help from people. Cyber I also has a part that helps it automatically respond to threats. Cyber I has a tool that tests how well it can find threats. This tool is like an enemy that tries to get past Cyber I. Cyber I also has a place where it collects information about threats from all over the internet.. It has a way to manage problems that come up. Cyber I was built using technology like Fast API, Next.js and Redis. This means Cyber I can look at a lot of information quickly and fix problems on its own. Cyber I is a next-generation Security Operations Center that can detect Advanced Persistent Threats and stop them. Cyber I is very good, at finding and stopping Advanced Persistent Threats.
A Hybrid Multi-Agent Emergency Response Simulation Platform with Centralized Dispatch and Localized Agent Autonomy
Urban emergency response systems are complex, high-risk, and hard to evaluate directly in real-world settings due to operational limits, costs, and safety concerns. Just as circuit designers use virtual prototyping platforms like Tinkercad before deploying physical hardware, this work introduces a simulation-first approach for planning and evaluating emergency responses. We develop a hybrid multi-agent simulation platform that allows for "test-before-deploy" experimentation for ambulance dispatch and urban incident management policies. The proposed system is built on a realistic urban road network derived from OpenStreetMap and runs using SUMO (Simulation of Urban Mobility) with TraCI integration. It models civilian traffic, hospital infrastructure, random incident generation, and a fleet of ambulances acting as autonomous agents. Each ambulance keeps localized state, intent, and behavior policies, allowing for decentralized decision-making for patrol and response. A centralized dispatch layer coordinates incident assignments using a scoring system, which can be enhanced with a tabular reinforcement learning policy for selecting primary responders. The platform adds more operational realism through severity-based responder allocation, scene dwell times, hospital handover delays, hotspot-aware patrol rebalancing, and localized traffic-signal preemption to simulate green corridors. An expandable experimental layer allows for synthetic modeling of weather conditions, traffic congestion patterns over time, variations in district-level demand, and hospital operational limits. The system provides structured operational metrics, including response times, completion rates, and resource use, which are analyzed through a dashboard-driven process. This setup allows for systematic comparison of dispatch strategies, fleet configurations, and environmental conditions under controlled, reproducible scenarios. The main contribution of this work is a modular and rollback-safe simulation platform that connects heuristic multi-agent control with learning-based dispatch in a realistic urban environment. By allowing rapid prototyping and evaluation of emergency response strategies before real-world deployment, the platform acts as a digital testbed for research in smart-city mobility, multi-agent coordination, and optimizing emergency logistics.
AI-Enabled Smart Helmet for Rider Safety and Intelligent Bike Control Using ESP32
The rapid growth of two-wheeler usage has significantly increased road accidents, primarily due to unsafe riding practices such as riding without helmets and driving under the influence of alcohol. Traditional safety measures are mostly reactive and fail to prevent accidents before they occur. This research presents an AI-enabled smart helmet system integrated with IoT and embedded technologies to enhance rider safety and ensure responsible driving behavior. The proposed system combines multiple sensors, including alcohol detection and helmet verification mechanisms, with an intelligent control unit based on ESP32. The system ensures that the motorcycle ignition is enabled only when the rider is wearing a helmet and is not intoxicated. Additionally, the integration of artificial intelligence enables real-time road sign recognition and rider assistance, improving situational awareness. By incorporating wireless communication between the helmet and the vehicle, the system creates an automated safety enforcement mechanism. The solution emphasizes preventive safety, reduces dependency on manual monitoring, and contributes to minimizing accident rates. This research highlights how the integration of AI, IoT, and embedded systems can transform traditional helmets into intelligent safety devices for modern transportation.
AI-Assisted Inventory Forecasting in Small Enterprises
Inventory management poses a significant challenge to SMEs, as they frequently depend on manual solutions or rudi­mentary tools, leading to inaccurate forecasts of consumer de­mands, over-stocking, and under-stocking. This paper proposes an artificial intelligence-based inventory prediction model de­signed to ensure accurate demand forecasting and improved in­ventory control using machine learning techniques. The pro­posed inventory prediction system leverages historical sales records, inventory reports, and seasonality factors to detect con­sumer demand trends and generate predictions. The develop­ment of the proposed system is implemented using Python li­braries such as Pandas, NumPy, and Scikit-learn. Supervised learning algorithms including linear regression and decision trees are utilized to forecast future consumer demand. More­over, the proposed inventory prediction system is capable of performing multiple tasks apart from generating demand fore­casts, such as monitoring inventory levels, sending automated alerts, and visualizing inventory management data in an inter­active dashboard. According to the experimental evaluation re­sults, the proposed system can achieve a predictive accuracy of 80–90% based on the quality of training data sets. In addition, the proposed system can significantly reduce inventory man­agement issues such as over-stocking and under-stocking. The proposed solution is lightweight, inexpensive, and specifically tailored to small enterprises, allowing for advanced inventory optimization without the need for infrastructure.
AI Engineering in the Making of Next-Generation Conversational System
Artificial Intelligence (AI) has significantly transformed human–computer interaction by enabling machines to understand, interpret, and respond to human language. However, most existing conversational AI systems primarily focus on text and voice-based communication while lacking the ability to understand the emotional state of users. This paper proposes an enhanced conversational AI system, MindMesh-E, that integrates Natural Language Processing (NLP), sentiment analysis, and speech-based emotion recognition to provide more adaptive and human-like interactions. The system captures user voice input through a microphone, converts speech into text using Python-based speech recognition modules, and analyzes emotional context using machine learning models. Based on detected emotions such as happiness, stress, sadness, or neutrality, the assistant generates personalized responses using transformer-based language models. The generated responses are delivered through audio feedback along with a pseudo-holographic projection interface, creating an immersive screenless interaction experience. The proposed architecture focuses on affordable implementation using compact edge-computing devices such as Raspberry Pi, making it suitable for educational, research, and experimental applications. The results demonstrate that combining emotional intelligence with conversational AI can improve user engagement and contribute toward the development of future human–AI communication systems.
Design and Research of TreatMate: A Smart Public Dog Food Vending Machine
Urban regions face persistent challenges related to irregular stray-dog feeding, unmanaged plastic waste accumulation, and inconsistent access to controlled animal-food dispensing. This paper presents Treatmate, an IoT-enabled public dog food vending machine that integrates verified digital transactions with a recycling-oriented activation pathway to ensure controlled and incentivized food dispensing. The system is built around an ESP32 with an OV2640 camera module, while a Tiny CNN (96×96 input, quantized int8, TensorFlow Lite Micro) performs on-device bottle material classification to confirm whether the item inserted for recycling is plastic before rewarding the user. The dispenser uses a dual-actuation mechanism with a DC geared motor and servo motor, and real-time stock monitoring is provided through a load cell interfaced via an HX711 amplifier. A cloud-based backend developed using Node.js manages secure transaction validation, device synchronization, and centralized logging over HTTPS communication. In addition to automated dispensing, the system records recycling activity and feeding patterns within a centralized database. A dedicated analytics layer processes historical dispensing and bottle-insertion records to identify feeding trends and correlate food distribution with regional stray-dog demand. The proposed system demonstrates a scalable, sustainability-oriented smart infrastructure model integrating embedded vision, cloud computing, recycling incentives, and data riven urban welfare management.
An AI-Powered Conversational Agent for Smart College Assistance
Educational institutions have a lot of information that students and faculty need to access all the time. They usually use things like notice boards websites that do not change and desks where people have to ask for information in person.. These methods often mean that people have to wait a long time for answers and it is hard for people to communicate with each other. It also means that the people who work at the institution have a lot work to do. Recently Artificial Intelligence and Natural Language Pro- cessing and Machine Learning have gotten a lot better. This means that we can now make computer programs that can have conversations with people and help them get the information they need. This paper is about a computer program that is like a chatbot. It is designed to help students and faculty get information about the college. The College Information Management chatbot can answer questions about things like admissions and schedules and exams. Who the faculty members are and what facilities are on campus and what rules the college has. The chatbot uses Natural Language Processing and Machine Learning to understand what people are asking and give them the answers. The College Information Management chatbot is on the cloud, which means it can be used by a lot of people at the time without any problems. It can be used on the web and, on phones. We tried out the College Information Management chatbot. It worked really well. It could understand what people were asking and give them the answers most of the time. This means that people do not have to wait a time for answers and the people who work at the institution do not have as much work to do. The College Information Management chatbot can also be used by people who speak languages and it can understand voice commands. This makes it easier for all kinds of students to use. The College Information Management chatbot shows that Artificial Intelligence can be used to make college life better. It can help people get the information they need and make the college a smarter place. The College Information Management chatbot can also help the college communicate with people efficiently.
A Systematic Review of EEG Signal Processing and AI Driven Techniques in Brain–Computer Interface
In the field of neuroscience, Meditation has gained significant focus because of its positive effects on cognitive performance and mental health. With the help of Brain Computer Interface (BCI), communication between the brain and other devices can be made possible through the analysis of brain signals. As Electroencephalography (EEG) is non-invasive in nature and has high temporal resolution, it is widely used in BCI applications. Meditative EEG signals have attracted attention because meditation alters brain activity patterns, which can be analyzed for cognitive and mental health applications in recent years. The accuracy of EEG signal classification and interpretation has significantly improved with advanced machine learning techniques. This paper reviews and explores the frameworks used for analyzing meditative EEG signals using machine learning algorithms. It discusses EEG signal acquisition, signal pre-processing methods, feature extraction techniques, and classification approaches. Moreover, the paper points out present challenges and potential areas for future research in creating reliable BCI systems for analyzing meditation.
Privacy-Preserving On-Screen Activity Tracking and Classification in E-Learning using AI
The blistering development of online education has changed considerably the manner in which education, learning as well as scholarly analysis is given in academic institutions, instructional centers, and in professional education. Internet technologies have provided the flexibility in accessing learning materials regardless of geographical limits and thus learning institutions have been able to touch the greater and more varied populations of learners. Nevertheless, the task of making online assessment secure and trustworthy is a pressing challenge despite the fact that the process of content delivery has been developed at a remarkable pace. Invigilators monitor student behaviour directly in physical examination halls, confirm identity, limit unauthorised materials and compliance with examination regulations. It is much more challenging to provide the identical level of control within remote settings. Somewhat towards this end, various scientists and organizations have been undertaking the study of learning intelligent proctoring systems which monitor student behaviour automatically in online exams. These are the systems that usually use the help of such tools as webcams, microphones, screen-capture functions, and behavioural analytics to detect suspicious behaviour like impersonation, collaborating with third parties, unusual gazes movement, multiple-people presence, using a hidden mobile phone, and constantly switching applications. Recent developments in artificial intelligence, particularly computer vision and machine learning, have enhanced the ability of such systems to scan facial expressions, head pose, attention patterns and environmental cues in real time. Although these have some advantages, issues about privacy, lack of fairness and bias in algorithmic choices, over-surveillance and false charges have hindered universal adoption. Thus, the new generation of online monitoring tools needs to strike a balance between integrity in examinations and responsible data management. In this review, a detailed discussion will be made on privacy preserving activities tracked on-screen in e-learning assessment. It examines significant technologies, the current constraints, architectural design imperatives, ethical issues, and future research potential. An approach is also outlined that uses a privacy-conscious framework, where sensitive processing of data is carried out locally, but significant risk indicators and little encrypted evidence are sent to reviewers. These solutions are able to enhance trust, scalability and regulatory compliance of digital education e-mails.
This project introduced the cloud based honeypot system development and uses the data collected by the honeypot to create comprehensive machine learning models and understand patterns and fingerprints to eventually categorize this data into a potential threat or a malicious activity. By utilizing cloud services, the system can be scaled up and down as required, the architecture integrates advanced data capturing techniques and date pipelining for proper isolation of the data collected and also the machine learning models, the architecture is comprised of capturing services, machine learning service, dashboard and a data pipeline to help isolate the individual components and services, data captured through the capturing services is uploaded to the machine learning service for data classification and model making. The research addresses the critical issue of lack of data for machine learning models in security and attack patterns including cloud environments.
RAKSHAK: A Multimodal Android Safety Platform Integrating Discreet Emergency Triggers and Responsible AI-Driven Support
Women’s safety apps have traditionally employed conventional panic button designs, which are immediately rendered ineffective as soon as the user finds herself in a situation where she cannot openly touch her screen. This paper proposes RAKSHAK, a native Android platform based on two unconventional, low-probability emergency triggering techniques—a computer vision pipeline for detecting deliberate eye blink patterns and an accelerometer-based signal processing pipeline for detecting deliberate triple tap rhythms. Along with these low-probability emergency triggering techniques, RAKSHAK also offers proactive assistance in the form of live sharing, a facility-finding mechanism based on proximity, informative content, and a scope-bound AI-powered chat assistant based on the Gemini large language model. The efficacy of RAKSHAK was validated through a study involving twenty participants, with results indicating a range of 85.5% to 99% for the true positive rate of the emergency triggering techniques and a SUS score of 82.5. An ethical architecture for the scope-bound AI-powered chat assistant offered in RAKSHAK serves as a reusable template for the safe deployment of generative AI in safety-critical mobile applications.
In the current scenario, mental stress has become a major issue in society, impacting mental and physical well- being. Early detection of the level of stress is critical in order to intervene and avoid severe mental conditions. In this paper, a novel method of predicting stress levels using the responses obtained in the DASS-21 questionnaire is presented using the machine learning approach. In the proposed system, a Random Forest classifier is used to classify individuals based on different levels of stress severity. Data preprocessing techniques are also used to enhance the accuracy of the model, including label encoding, feature scaling, and handling missing values. In the experiment, it has been shown that the Random Forest model is capable of predicting the levels of stress and the most dominant features in the questionnaire.
A Physics-Informed Deep Learning Framework for Performance Optimization of AlGaN/GaN HEMTs in High-Frequency RF Applications
AlGaN/GaN High Electron Mobility Transistors (HEMTs) are essential for high-frequency RF applications. However, designing HEMTs is difficult due to the complexity of the physical relationship between internal charges, device geometry, and reliability. The available standard simulation tools (TCAD) are accurate; however, they are too slow for testing thousands of design variations. In this study, we developed a Physics-Informed Neural Network (PINN) architecture to speed up this process. Initially, a reliable dataset is developed using calibrated TCAD simulations. During the training period, Poisson’s equation residuals and the charge conversions were used. It generates consistent predictions of transconductance, breakdown voltage, cut-off frequency, and drain current. The proposed framework achieves a low prediction error and a significant reduction in the computational cost.
A Hybrid Machine Learning Approach for Career Prediction Using Academic Features and Psychometric Personality Models
Selecting an adequate career requires taking into account various elements including academics, individual interest areas, and personality characteristics. Conventional career recommender models usually consider only one of these aspects, which is either academics or interest profiling, yielding relatively poor accuracy rates. This paper explores a multi-modal machine learning paradigm that utilizes academic characteristics, vocational interests, and personality traits in recommending careers in order to improve recommendation efficiency and robustness. Three major types of datasets will be applied for training: a Career Recommender Dataset with academic features, a massive RIASEC dataset that provides information about interests, and the Big Five personality dataset. Academic features will be normalized with TF-IDF and NLP techniques, and a logistic regression model will be first generated based on this data. It will then be improved by the addition of psychometric features, and a stacked ensemble will be constructed where a Random Forest meta-classifier will make final recommendations. Experimental analysis will reveal that the integration of diverse sources yields substantial prediction performance advantages in contrast to the use of academic-only data, and it may even provide valuable insight regarding personality-career associations.
Intelligent Investment Analysis and Asset Allocation System
The fast development of financial tech- nology platforms. has added to financial markets involvement, particularly among. first-time and young investors. Nonetheless, there are numerous people who are lacking. financial literacy to do informed investment. decisions. Through this, investors tend to turn to informal sources. as peer advice like social media or peer advice in investment selection. strategies. In this paper, an Intelligent Investment Analysis is proposed. Portfolio Sys- tem which offers individualized portfolio. user financial profile- based recommendations. The system evaluates the factors like income, age, amount invested, determinable risk tolerance and investment horizon to establish appropriate asset. divestment between such financial instruments as stocks, mutual. debt instruments, funds and exchange traded funds. The suggested system implements diversification principles inferred. as given in the Modern Portfolio Theory [8] to minimize investment risk and enhance sustainability of finances. The system is evaluated through the various investor profiles in terms of safeness, mod- erate, and risky violent types. Results demonstrate consistent, explicable portfolio allocations whose goals are feasible casually. test using Monte Carlo simulation, validation of the system. transparency of recommendation and functional correctness. The system will enhance the financial literacy hence increase data- driven investment decision making by the retail investors within the system. Indian market.
Bone Mature: A Deep Learning Framework for Bone Age Detection
Bone age estimation is one of the most important processes in paediatric radiology which is used to determine the level of skeletal development and identify various developmental problems. Hand radiograph interpretation is manually used in the estimation of bone age, but this method is rather subjective and inconsistent. In this paper, the author presents the Bone Mature, a two-step deep learning system to estimate bone age without the need of manually annotated region of interest. During the first step, an annotation-free cascaded critical bone region localization model is trained using InceptionV3 with an extra convolutional block attention module (CBAM) and gradient-weighted class activation mapping (Grad-CAM). In particular, carpal and metacarpal-phalanx bones, which are anatomically important, are automatically detected in hand radiographs by the proposed cascaded critical bone region localization model. The second step involves the localized areas being inputted into ResNet50 and Xception models with mid-level feature fusion and gender-assisted estimation.
SkullFractureNet: A VGG16-Based Transfer Learning Pipeline with Two-Phase Fine-Tuning and Test-Time Augmentation for Automated Skull Fracture Detection in Head CT
This work seeks to tackle the problem of significant misdiagnosis rates in CT imaging for identifying skull fractures – as high as 14.8%, even amongst expert radiologists – thus highlighting the need for reliable automatic skull fracture detection approaches. In this study, a new SkullFractureNet approach is proposed where VGG16 is used as the convolutional neural network (CNN) backbone, pretrained on the ImageNet database, instead of the previous efficient net based approach (EfficientNetB0). The proposed approach utilizes specific architectural modifications aimed at addressing overfitting issues arising from the small number of data samples in a real-world setting: GlobalMaxPooling2D is used in place of the GlobalAveragePooling2D layer to better locate local fracture areas; the classification head size is reduced from 256 to 64; dropout rate is increased to 0.6 and 0.4, and L2 regularization is applied throughout the dense layers. In terms of the training process, the same two-phase process remains – the first phase trains the classifier head while freezing all of the VGG16 weights (learning rate = 5 × 10⁻⁵; 40 epochs); the second phase unfreezes only the final convolutional block (block5_conv3) at a learning rate of 5 × 10⁻⁶. Aggressive slice-level augmentation strategies (flip, rotate, zoom, brightness, contrast, noise and cutout) in combination with a test-time augmentation (TTA) approach where eight forward passes are averaged together are used.The experiments were performed on a balanced dataset of 42 samples (21 fractures vs. 21 normals) using a patient-based split approach. The results of testing on the held-out portion of the test set (6 samples) yield the following performance metrics (maximum probability aggregation): accuracy 0.667, specificity 1.000, precision 1.000, AUC-ROC 0.667, and AUC-PRC 0.756, and no false positives. It can be noted that the training process shows that the measures adopted to prevent overfitting were effective.
Automated Skull Fracture Detection in CT Imaging: A Systematic Review of Computational Methods, Clinical Validation, and Future Directions
Skull fracture diagnosis is regarded as a significant diagnostic problem because it features a misdiagnosis rate up to 14.8% even among experienced radiologists. In this regard, the paper presents a systematic synthesis of 20 studies on skull fracture detection using conventional image-processing techniques, deep learning algorithms, and clinical validation. The best results obtained using the conventional technique are the recognition rates equal to 99-100%. In terms of deep learning models, their effectiveness is higher compared to others; for instance, CNN hybrid model reaches 97.6% accuracy, and YOLOv8 reaches 49% with recall 91.5%. Post-mortem skull fracture CT diagnosis features sensitivity 0.89 (0.80-0.94) with a base sensitivity of 0.87, and there are several visualization techniques such as azimuthal equidistant mapping that provide better skull fracture detection in less than one second of computational time. Important limitations are represented by the lack of dataset (CQ500 includes only 491 cases, including 84 patients with fractures), low inter-observer agreement (ICC < 0.4), and anatomical problems. Traumatic brain injury prevalence among maxillofacial fractures patients equals 51.04% (26,774).
An Intelligent AI-Based Proctored Examination Framework with Automated Question Generation and Performance Analytics
The current systems of on-line examination which help with competitive examinations have major flaws such as inadequate backup support of databases for questions, lack of categorization of difficulty levels of questions, inefficient general performance measures, and ineffective or incomplete monitoring systems. These flaws undermine the validity of the tests, and hence, make it difficult for them to evaluate candidates’ readiness. This study proposes a model of intelligent and AI-based proctored examinations which includes question management in real-time, question categorization based on difficulty levels, and overall performance assessment.The device consists of AI-driven proctoring techniques to reveal candidate conduct throughout examinations and find out capability malpractices, thereby improving examination integrity. Additionally, superior analytics are hired to generate exact standard performance insights, permitting purpose evaluation of learner development. The proposed framework ambitions to decorate the fairness, reliability, and effectiveness of online competitive examination exams and can be extended to massive-scale examination systems past MPSC education.
Multi-Crop Multi-Disease Detection Framework Using Explainable Artificial Intelligence for Precision Agriculture: A Comprehensive Literature Review
Plant diseases pose significant menace to food security in the world, causing significant losses of money and reduced yields of key agricultural products like cotton, tomato, wheat and rice. The traditional methods of detection that rely on manual diagnosis are time consuming, labour-intensive and prone to human errors hence the necessity of automated and accurate diagnostic systems. The recent advances in artificial intelligence, especially deep learning and computer-vision methods, have changed the scene of the plant disease detection because it has made it possible to identify various diseases on different crop species in real time, automatically, and accurately. In this literature review, we discuss the state of the art in multi-crop disease detection models, focusing on deep-learning models, such as Convolutional Neural Networks (CNNs), Vision Transformers (ViT), and object-detection models, including YOLO, Faster-R-CNN, and Mask-R-CNN, and interpretable AI (XAI) methods, such as Grad-CAM, SHAP, and LIME. Through a systematic review of thirty recent articles released in 2020-2025, the review outlines major technological developments, performance standards, as well as viable deployment plans of precision agriculture. It also determines serious gaps in research such as lack of integrated multi-crop models, little verification of these in field applications, inability to scale computations to support edge deployment, and lack of model interpretability. The review thus adds to the field through compiling the existing knowledge, providing a comparative methodology analysis, and future research steps to create explainable, lightweight, and farmer-friendly AI systems to achieve sustainable agriculture.
HemorrhageNet: Automated Detection and Fine-Grained Subtype Identification of Intracranial Hemorrhage in Non-Contrast CT Imaging Using Calibrated Dual-Head EfficientNetB4
Finding out if someone has a brain bleed, which is also called a hemorrhage is very important. We use a kind of x-ray called a cranial computed tomography or CT scan to check for this. If we do not find out about the brain bleed it can cause very bad damage to the brain or even death. In some places there are no doctors who are experts in looking at these x-ray pictures and they can get very tired from working too much. This means that sometimes people do not get the care they need . We can use computers to help look at these pictures and find out if someone has a brain bleed. This paper is about a computer program called Hemorrha- geNet. It is a kind of program that uses pictures to decide if someone has a brain bleed and what kind of bleed it is. There are five kinds of brain bleeds: Intraventricular, Intraparenchymal, Subarachnoid, Epidural and Subdural. The program uses a kind of picture called a CT slice and it was trained on 1,200 of these pictures. The program is very good at finding out if someone has a brain bleed. It is right 93.9 percent of the time. It never misses a bleed. The program is also good at figuring out what kind of bleed it is. It is about 80 percent of the time for each of the five kinds of bleeds. We think that HemorrhageNet can be very helpful in hospitals. It can help doctors find out if someone has a brain bleed and what kind of bleed it is. This means that doctors can make decisions about how to take care of the person.The program uses a kind of computer model called a convolu- tional model. It was trained on 1,200 CT slices.It is very good at finding out if someone has a brain bleed and what kind of bleed it is. It can help doctors make decisions, about how to take care of people with brain bleeds.
Speaking in Public and Presentation Delivery is Great Challenge for Professionals and Students. While there are some tools which provides a context or summary for the presentation or visual understanding but the user is not confident on his presentation and not get feedback except manual which is traditional way and not accessible when needed. This Paper Represents the Presentify an AI Based Full stack application which provides the script using RAG framework which creates chunking mechanism and store in vector database chroma, chat with presentation using API or Local LLM, and practice-based evaluation which will prepare you end to end to present your ppt confidently and accurately.