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Artificial Intelligence-Driven Lumbar Stenosis Diagnosis: A Deep Learning Pipeline for MRI-Based Segmentation and Classification
Lumbar spinal stenosis is a prevalent musculoskeletal disorder that requires accurate diagnosis through magnetic resonance imaging (MRI). However, manual interpretation of MRI images is time-consuming and subject to inter-observer variability. This study proposes an automated deep learning-based pipeline for lumbar stenosis identification, integrating advanced methodologies for preprocessing, segmentation, feature extraction, and classification. The pipeline consists of Super-Resolution Convolutional Neural Network (SRCNN) for MRI image enhancement, SegNet for segmentation of the spinal canal, intervertebral discs (IVDs), and neural foramen, Convolutional Block Attention Module (CBAM) for feature refinement, and Swin Transformer for final classification. The proposed method was evaluated on a publicly available multicenter lumbar spine MRI dataset, comprising 218 patient studies with 447 MRI series. Model performance was assessed using accuracy, recall, precision, and F1-score, achieving 95.2% accuracy, 89.82% recall, 92.3% precision, and an F1-score of 96.12%. The results demonstrate that SRCNN enhances MRI quality for improved segmentation, CBAM strengthens feature extraction, and Swin Transformer effectively classifies stenosis cases. This study highlights the efficacy of AI-driven methodologies in lumbar spine MRI analysis, offering a potential computer-aided diagnosis (CAD) tool for clinical applications. Future work may focus on optimizing model efficiency and improving generalization across diverse imaging protocols. 2025 IEEE. -
CHARM: Context-based Hierarchical Association Rule Mining for Analyzing Purchase Patterns
Data mining is now an essential part of business intelligence, specially in the retail analytics, allowing companies to derive meaningful insights out of big volumes of transaction data. This paper uses Context-Based Hierarchical Association Rule Mining to study purchase behavior in Indian retail outlets through Apriori algorithm that helps to take effective decissions. The available literature primarily employs flat item association models and lacks contextual dimensions and profit-oriented outcomes of rules, which also creates an evident gap in the current research. The study combines various contextual aspects, including product category, sub-category, region, and state, to produce the multilevel association rules indicating the product relationship under different sales levels of the products following an hierarchy. The Lift and Conviction metrics are applied along with support and confidence to eliminate the coincidental patterns and make the rules in business reliable. Support-based filtering and a minimum threshold of confidence of 0.1 are used to determine separate patterns of co-purchase that are significant. In order to make business relevant, the level of profit is involved as a result which puts into emphasis rules which lead directly to financial performance. The findings show that context-enriched rules offer a better insight into customer buying behavior and retailers have the opportunity to identify profitable cross-selling opportunities that more traditional flat associated analysis might otherwise miss. The hierarchical structure allows improving interpretability through associating items with larger contextual properties, which will be useful in designing the promotion, product placement, and optimizing the regional strategy. Overall, this paper presents the combination of contextual and profit-driven parameters as a concept that can be used to provide a data-driven basis of strategic retail decision-making and sustainable competitive advantage. 2026 IEEE. -
Deep-fake Detection for Recognising Altered Audio using Deep Learning Approach
Ensuring the validity of audio recordings is becoming increasingly difficult due to deep-fake technology. Audio-analysis is used to identify deep-fake audio, which has been examined here. Machine-learning models can be made technological to compare between real and modified audio by examining minute artifacts and inconsistencies added to during the deep-fake production process. In this work, advanced signal-processing techniques like spectrum-analysis, voice-activity detection, and speaker-recognition; are used to extract relevant information from audio recordings. In order to exact deep-fake audio detection, these features are then utilized to guide and judge deep-learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The objective is to create reliable and efficient techniques for detecting altered audio, almost eliminating the possible dangers. The goal is to provide reliable and efficient techniques for detecting modified audio in order to mitigate the possible risks related to deep-fake technology in a number of fields, such as social-media, journalism, and security. 2025 IEEE. -
Leveraging Neural Networks for Personalized Student Engagement and Performance Prediction
Neural Networks can be used to predict students' performance and future placement opportunities. Nowadays, it is a really difficult task for students to predict their chances of getting a good campus placement, even if they have prepared well for it. There is an intense competition among peers, and many factors that influence a student's placement. To manage this data and predict their chances, they need a reliable system. In this paper, we discuss a model in which the system focuses on three main areas: predicting placement chances, analyzing skill gaps, and offering personalized recommendations for improvement. The system would predict many potential career paths by analyzing academic records, extracurricular activities, and job market trends, while also highlighting immediate opportunities and long-term growth prospects. The integration of agentic AI further enhances this system by enabling autonomous decision-making and adaptive learning. Which ensures personalized guidance for each student. By dynamically refining the predictions which are based on real-time feedback, agentic AI helps to empower students to proactively navigate their career paths with greater confidence and precision. This approach provides a genuine solution, in order to improve placement strategies, by ensuring that the students are well-equipped to meet the challenges of the modern workforce. 2025 IEEE. -
Low Latency based Smart Ambulance Model in Emergency Healthcare
Smart ambulance represents a significant evolution in emergency healthcare,using most advance and innovative new devices, machine and techniques to improve and create new opportunities by the help of engineering methods or any formula or scientific principles to offer solutions to the limitation of traditional emergency medical services. This work focuses on the combination of Artificial Intelligence, IoT, Telemedicine and real time monitoring system to ameliorate response time, enhance patient care and optimize resource allocation in smart ambulance. By examining the performance of smart ambulance compared to traditional ones ,the research emphasize their ability and focuses on strategies to navigate challenges such as traffic congestion, communication delays and inefficient decision making ,and suggest relevant ways to resolve the issue. The result showed a positive and steady increase in the performance of smart ambulance in ensuring faster, more efficient and higher quality of emergency medical services. This paper emphasizes breakthrough ability of smart ambulance system in improving in emergency healthcare outcome and build a bedrock for their wider usages in the medical field. 2025 IEEE. -
Optimizing Sustainable Agriculture Through Customized Crop Management Approach
Sustainable agriculture is essential for actively addressing the dual challenges of global food security and climate variability. This study deploys an intelligent, data-driven approach to tailored crop management, positioning it as a dynamic framework for optimizing cultivation by enhancing soil quality and climate resilience. Recognizing soil quality as a foundational element of sustainable agriculture, this paper highlights its critical role in nutrient cycling, water retention, and organic enrichment. Through strategic interventions such as precision crop rotation, conservation tillage, cover cropping, and organic amendments, this approach maximizes soil porosity, health, and fertility while mitigating environmental degradation. By operationalization tailored crop management as an adaptable and scalable system, this research advances the synergy between agricultural productivity and environmental sustainability. Leveraging AI-driven insights, predictive modeling, and modular frameworks, these strategies empower global efforts toward food security, ecological balance, and climate-adaptive farming solutions. 2025 IEEE. -
Deployment of Smart Surveillance System using Deep Learning to Recognize Cyber-criminals
This paper presents the development of the smart surveillance systems critical in identifying cybercriminals through the use of deep learning.The system utilizes deep learning algorithms for the identification of cybercriminals in physical and cyberspace.The systems apply neural networks to analyze images,video streams and cyber behavior for pattern recognition of suspicious activities and potential threats.Also the system analyze the online activities of users and flow of data within the network for signs of cybercriminals. Various technologies such as convolutional neural networks (CNN), and recurrent neural networks (RNN) are used to distinguish facial features, body language, and unusual online activities. This way, effective security measures are taken to prevent or reduce the impact of cybercriminals in various environments by combining intelligent monitoring systems with future threat prediction. The system is capable of evolving by identifying new criminal patterns to enhance its performance. This means the system is modified and updated as it receives more data, making it effective in multivariate settings such as any institution with financial activities, government networks, and high-security locations. 2025 IEEE. -
An Intelligent Cognitive Framework for Crime Prediction in Smart Cities using Video Mining
Booming development in cities with dense population have led to urban policing and public safety emerging as urgent concerns in city environments.current monitoring practices including CCTV'S and other IOT sensors generate a vast amount of data ,thus making them inadequate for the task. However a combination of video mining,computer vision,artificial intelligence and data mining techniques,do offer us a better framework for monitoring and real-time detection of crime in Smart city"s environment. This paper proposes an intelligent and Cognitive framework for prediction of crime. by combining various advanced modals such as YOLO (You took Only Look Once) for detecting objects, 3D Convolutional Neural Networks (CNN) for recognizing actions, deep SORT for tracking multiple objects, One-class SVM for detecting anomaly and LSTM for behavioral analysis. These modals can organized to function in a coherent system which can be organized to distinguish examine and trail illegal activities such of mugging, robbery, pick pocketing, violence, utilizing available live video feeds. By efficient date processing, and overcoming shortcomings such of limited labeled datasets and real-time feed detection, this framework can provide practical conclusion making tool for law enforcement in urban smart city environments which can enhance urban safety.Besides effective crime detection,this tool compiles with established ethical standards such as upholding privacy and legal compliance. 2025 IEEE. -
An Optimized Convolutional Neural Network Model for Real-Time Object Detection in Drones
The capacity of drones to perform item detection in actual-time is crucial for applications inclusive of surveillance, seek and rescue, and environmental tracking. This look at investigates how convolutional neural networks (CNNs) can beautify object detection in aerial imagery by enhancing both accuracy and speed. CNNs excel at extracting spatial info, permitting drones to apprehend objects even in relatively complicated environments. by adopting light-weight CNN architectures and optimization strategies, we acquire advanced performance with minimum computational requirements, ensuring green operation on embedded drone platforms. Our findings verify that CNN-based fashions considerably decorate detection accuracy and responsiveness, allowing the improvement of smarter and more self reliant drones. 2025 IEEE. -
An Optimized Convolutional Neural Network Model for Real-Time Object Detection in Drones
The capacity of drones to perform item detection in actual-time is crucial for applications inclusive of surveillance, seek and rescue, and environmental tracking. This look at investigates how convolutional neural networks (CNNs) can beautify object detection in aerial imagery by enhancing both accuracy and speed. CNNs excel at extracting spatial info, permitting drones to apprehend objects even in relatively complicated environments. by adopting light-weight CNN architectures and optimization strategies, we acquire advanced performance with minimum computational requirements, ensuring green operation on embedded drone platforms. Our findings verify that CNN-based fashions considerably decorate detection accuracy and responsiveness, allowing the improvement of smarter and more self reliant drones. 2025 IEEE. -
Design of a Multi Camera Enabled Scrutinizing Framework for Smart Cities
This research paper presents a multi-camera surveillance system tailored for the demands of smart cities. By integrating edge computing, the system decentralizes processing, reducing latency and alleviating network bandwidth strain. The architecture includes layers for data collection, edge processing, centralized storage, AI-driven analysis, synchronization, and user visualization. Cameras capture and preprocess data locally to identify anomalies and minimize unnecessary transmission. AI algorithms handle tasks like object tracking, behavior analysis, and event detection with precision. Synchronization ensures seamless temporal alignment across video streams for accurate event reconstruction. User-friendly dashboards provide actionable insights for urban planning and public safety. By leveraging edge computing, AI, and robust synchronization, this system addresses scalability, latency, and privacy concerns, offering enhanced safety, optimized traffic flow, and better urban planning. 2025 IEEE. -
Gaming for the Planet: Exploring Belief, Motivation, and Behaviour in Climate-Conscious Play
The emergence of digital games as an impactful medium for engaging audience from diverse background and clime cannot be overemphasised in today's world of sophisticated technologies and high-end devices. This study explored the potential capabilities of digital games in influencing beliefs, motivations, and behaviours that have connections with climate. The main purpose was to understand the psychological and structural game design elements that promotes measurable ecological impact. Case studies from the EU-funded GREAT Project, such as Play2Act, and broader literature on gamification for environmental engagement. Data from a global in-game poll (n > 180,000) was analysed to gain insights into how green messaging within popular games translates into real-world behavioural changes. Findings from the study shows that 65% of players reported increased awareness of climate issues. Also, results indicated that 40% of the players adopted at least one new eco-friendly behaviour after gameplay. Finally, it was found that specific game design elements like reward systems and narrative integration. The study concluded that mainstream digital games could serve as scalable tools for environmental engagement, which can bridge the gap between environmental psychology, game studies, and design research. We recommend that future research should focus on refining these design frameworks and integrate them into policy to maximise the ecological impact of digital games. integration. 2025 IEEE. -
Contextual Embedding Fusion for Quick-Commerce Reviews: Rating, 5-Class Classification, and Sentiment Analysis Across Classical ML, DL, and Transformer Models
Quick-commerce platforms depend on accurate reading of customer feedback to steer pricing and service quality. We study a dataset of delivery-agent reviews, and build two rating predictors and a text-only classifier suite. On the rating regression task (1-5), we benchmark seven models (Ridge, Lasso, ElasticNet, SVR-RBF, Gradient Boosting, Random Forest, XGBoost). The best test performance is achieved by RandomForestRegressor with MAE = 0.527, RMSE = 0.973, and R2 = 0.555; cross-validation places XGBoost and Gradient Boosting close behind. For 5-class rating classification, we compare seven TF-IDF baselines (LogReg, LinearSVC, two SGD variants, RidgeClassifier, ComplementNB, XGBClassifier) and find SGD (hinge) strongest (Accuracy = 0.817, Macro-F1 = 0.389), yet mid-rating classes (2-4) remain difficult. To address label ordinality, we fine-tune DistilBERT with ordinal heads: CORAL and CORN. These achieve Accuracy = 0.837/0.819 and Macro-F1 = 0.439/0.441, improving Macro-F1 by +5-6 points over TF-IDF baselines, mainly by rescuing classes 2-4. We further map regression outputs to 5 classes via optimized cutpoints, raising Macro-F1 from 0.329 (rounding) to 0.380. Paired, fold-wise comparisons using Wilcoxon signed-rank and bootstrap 95% CIs show consistent gains; with 5 folds the discretized p = 0.0625 indicates all folds favor the better model, suggesting statistical significance with more folds. Overall, ordinal transformers and learned thresholds provide measurable, reproducible improvements for short, noisy e-commerce reviews, delivering higher fidelity on mid-range ratings while preserving high accuracy. 2025 IEEE. -
MobileNetV3-Based Fine-Tuned Facial Emotion Recognition with Targeted Class Balancing
Facial emotion recognition (FER) is a pillar of affective computing and augmented human computer interaction, but has been stymied by the problem of class imbalance and lack of prevalence of subtle emotional differences. This paper presents a lightweight FER framework based on the MobileNetV3 architecture with a fine-tuned and weighted dataset that applies class balance and class weighting as strategies that optimized the three-class classification of three discrete emotions Angry, Happy, and Sad. The characteristics of the dataset were assembled comprising a total of 7,305 labelled facial images, based on the KDEF, Kaggle, and Face Expression Dataset hence inheriting the heterogeneity of subjects and imaging conditions. The pre-processing of all of the images carried out as the RGB input and after resizing (224 x 224 pixels) a massive data augmentation done to encourage generalization. Transfer learning in the training pipeline is done through progressive unfreezing and the weight of the loss on the minority classes (Angry and Sad) are boosted to improve the performance of detection. The achieved model resulted in an accuracy of 87% on the test set, and had equal accuracy in preciseness, recall, and F1-scores over all emotion types. Extended error analysis revealed that the majority of cases that were misclassified fell between the categories Angry and Sad because they were mistaken due to combining visual cues. Even then, the performance showed stability despite the variable lighting as well as in variable positional context. In Comparison, MobileNetV3 outperforms state-of-art-lightweight models with respect to accuracy and computation of similar computational complexity. 2025 IEEE. -
An Intelligent Framework for Evaluating Handwritten Responses: Integrating Bloom's Taxonomy with Adaptive Assessment
Traditional manual grading of descriptive-type answer scripts is inefficient and laborious, while existing technologies rely on strict keyword matching and cosine similarity, which fail to capture the semantic meaning and argumentative quality. This paper proposes a multi-layered intelligent framework for evaluating handwritten descriptive answer scripts by integrating Revised Bloom's Taxonomy with adaptive assessment methods. The system consists of a multi-dimensional evaluation strategy comprising four valuation metrics, namely, content relevance, coherence, depth, and argumentation quality. When compared with conventional methods of keyword matching or computing the cosine similarity, this proposed framework evaluates the semantic meaning and argumentative structure while adapting to varying response styles and contextual differences for personalised assessment. 2025 IEEE. -
Levelling Up for Nature: Pathways of Engagement with Green Messages in Games
Digital games have become potent platforms for environmental messaging, capable of influencing beliefs, attitudes, and behaviours. This paper explores how players engage with green content embedded within game environments, using mixed-method data from the GREAT Project with more than 181,000 anonymised gameplay sessions. This research analysed cognitive, affective and behavioural responses to in-game environmental messaging and identified key pathways that lead to sustained pro-environmental actions using PCA and K-Means clustering analysis, by which varying types of user were identified from casual to deeply engaged players. The engagement questions were grouped into three categories: narrative frame, behavioural pledges, and social reflection. Our findings showed that 68% of the players participated in at least one green-themed pledge, which indicates a strong sensitivity to environmental content, informing a multilayered engagement model that supports both player experience and environmental literacy known as a 5-pathway green engagement model (5-PGEM), supporting the hypothesis that green messages, when embedded in games, can significantly shape eco-friendly actions. 2025 IEEE. -
Price Minds: AI-Driven Insights, Recommendations and Dynamic Pricing
This research aims to enhance e-commerce systems by leveraging customer behavior analysis, dynamic pricing, and personalized recommendations. With the increasing demand for tailored shopping experiences and competitive pricing, businesses require adaptive solutions. The study integrates synthetic and real-time customer data to identify purchasing patterns and segment customers effectively. Dynamic pricing strategies are applied to optimize revenue while maintaining customer satisfaction. A unified framework combines clustering techniques, real-time data streams, and decision-making models to deliver actionable insights for business operations. The proposed system dynamically adjusts pricing and recommends products based on individual customer preferences and behavior. The approach addresses the growing need for intelligent systems that adapt to market trends and consumer demands. Results demonstrate improved operational efficiency, better customer engagement, and enhanced profitability. This work highlights the importance of real-time analytics and intelligent pricing mechanisms in advancing e-commerce and creating competitive advantages in rapidly evolving markets. 2025 IEEE. -
Self-Organizing Microservice Composition for IoT-Enabled Neonatal Monitoring
The fast-paced growth of the Internet of Things (IoT) has revolutionized several industries, including the delivery of healthcare applications, due to its ability to support realtime and automated applications. However, several key challenges are associated with the dynamic and complex nature of IoT environments, such as scalability, increased system complexity, and network instability. This paper presents a self-organizing microservice Composition Model created especially for healthcare systems using IoT to address these issues. The Model employs decentralized, local interactions inspired by bio-inspired mechanisms. These mechanisms enable the system to autonomously form complex service composites with minimal human intervention through emergent behaviour. The model leverages an ant agent to optimize bio-inspired mechanisms and a deep learning agent for autonomous service choreography, enhancing the system's flexibility, adaptability, and overall performance. A continuous neonatal monitoring case study illustrates how the model works in composing services in a self-organizing manner. This research contributes to the advancement of smart healthcare infrastructures by proposing an innovative framework that adapts to the evolving needs of IoT-driven healthcare applications. 2025 IEEE. -
Self-Organizing Microservice Composition for IoT-Enabled Neonatal Monitoring
The fast-paced growth of the Internet of Things (IoT) has revolutionized several industries, including the delivery of healthcare applications, due to its ability to support realtime and automated applications. However, several key challenges are associated with the dynamic and complex nature of IoT environments, such as scalability, increased system complexity, and network instability. This paper presents a self-organizing microservice Composition Model created especially for healthcare systems using IoT to address these issues. The Model employs decentralized, local interactions inspired by bio-inspired mechanisms. These mechanisms enable the system to autonomously form complex service composites with minimal human intervention through emergent behaviour. The model leverages an ant agent to optimize bio-inspired mechanisms and a deep learning agent for autonomous service choreography, enhancing the system's flexibility, adaptability, and overall performance. A continuous neonatal monitoring case study illustrates how the model works in composing services in a self-organizing manner. This research contributes to the advancement of smart healthcare infrastructures by proposing an innovative framework that adapts to the evolving needs of IoT-driven healthcare applications. 2025 IEEE. -
MCCLDP: Multi Class Cotton Leaf Diseases Prediction and Classification using Deep Learning Model
Cotton plant disease detection is critical for sustainable agriculture and reducing crop losses. This paper proposes a novel Multi-Stream Attention-Guided Hybrid CNN (MAH-CNN) for accurate classification of cotton leaf diseases. The model leverages pre-trained ResNet152v2 and DenseNet-121 backbones for hierarchical feature extraction, complemented by a shallow CNN for localized texture analysis. A spatial attention mechanism enhances focus on disease-relevant regions, mitigating background noise. Features from the global and local streams are fused and passed through a lightweight classification head. The model achieves superior performance in terms of accuracy 97.32%, F1 score 98%, and specificity 100% on benchmark datasets which are available in open access, outperforming existing state-of-the-art methods. The integration of Grad-CAM provides interpretability, fostering trust in automated disease detection systems. 2025 IEEE.