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A 2-level cascaded deep learning with the adaptive novel feature extraction approach for sales forecasting framework

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International Journal of Computers and Applications 2025年第4期47卷 373-387页

作者： Selvaraj, Sivabalan Minu, R.I. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur India

This study presents a revolutionary deep-learning architecture that focuses on feature extraction, feature selection, and sales forecasting. The technique begins with a pre-processing step using median imputation and min–max. To extract features, the study uses the Word2Vec (CBOW) model to construct contextual word embeddings and TF-IDF to assess term relevance in texts. For numerical data, the Pearson Correlation Coefficient (PCC) detects linear associations, whereas the Adaptive Bhattacharyya Correlation refines characteristics using statistical metrics. The Tent Chaotic-based Puma Optimizer Algorithm (TC-POA) is used to pick important characteristics for the model effectively. The forecasting challenge employs the 2-level Cascaded Deep-Learning (2-LCDL) model. In this model, an Alexnet with Squeeze-and-Excitation is used along with the Bi-LSTM to capture sequential data dependencies and GRU for efficient long-term memory management. The work wraps up with a fully linked layer and SoftMax activation for sales prediction. This framework demonstrates substantial advances in deep-learning approaches, providing better-predicted accuracy and computational efficiency. The proposed model is evaluated for both 70% and 80% of training data for the metrics MSE of 0.0058, 0.0054, MAPE of 1.1968, 0.9979 and also the RMSE, G-Mean, MAE, MMAE, MNB, IoA have superior performance when compared to the other existing methods. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sales

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Utilizing enhanced deep learning methods for residential burglary detection via analysis of human behavior patterns

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Multimedia Tools and Applications 2025年第10期84卷 7627-7646页

作者： Pavithra, S. Muruganantham, B. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Residential burglary is a severe crime that affects millions of residents each year. It is critical to analyze patterns of human behavior in surveillance video data and discover suspicious actions to avoid and deter this crime. However, the complexity and diversity of human behavior make this undertaking challenging. In this research, we provide a unique system that uses advanced deep learning algorithms to detect abnormal actions that signal burglar attempts at time intervals. We use the state-of-the-art object identification method YOLOv7 to detect an object and its trajectories in video data. We additionally extract the time intervals between the object's movements and utilize them as a categorization feature. Based on the COCO dataset and three videos of distinct scenarios: abnormal activity, burglary attempt, and false alarm, we evaluate our system with earlier versions of YOLO and additional detection methods. On the COCO dataset, our method outperforms all other models with a precision of 93.9%, an f1 score of 94.42%, a recall of 94.71, and an accuracy of 95%. We also show that our algorithm can effectively discriminate between suspicious and normal behaviors based on the motion region's length, width, energy, and orientation variance, achieving an accuracy of 92% on average over three videos. Our system can provide practical domestic protection by automatically detecting and preventing burglary attempts and violent scenarios. Our method can also contribute to the advancement of knowledge in the fields of human behavior analysis and object recognition. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crime

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Ensemble Multimodal Disease Risk Prediction: Integrating Chest X-Ray Images and Medical Data with ERSGB-RSW Method

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 697-711页

作者： Amirtharaj, Shiny Irene David Marudappa, Pushpalatha Sachin, Lade Khalid, Mohammad Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Campus Chennai India

The emergence of multimodal disease risk prediction signifies a pivotal shift towards healthcare by integrating information from various sources and enhancing the reliability of predicting susceptibility to specific diseases. Traditional methods face several challenges such as limited capacity to handle heterogeneous datasets, high computational power, and less efficiency. To solve the drawbacks a novel Ensemble Random Support vector Gradient Bayes Random Seal Whiskers method is proposed. In this study, the Random Forest is utilized to extract chest X-ray images from the dataset, the Support Vector Machine (SVM) is employed to categorize images by integrating various medical data modalities, the gradient boosting is implemented to capture complex relationships in chest X-ray images and Naive Bayes is used to classify image features. Also, a weighted average ensemble is employed to integrate the outcomes of the methods namely Random Forest, SVM, Gradient boosting, and Naive Bayes by assigning different weights based on its predictions in this work, the Harbor seal whiskers optimization algorithm with a random update is employed to optimize the tuned parameters based on the datasets. The experimental results revealed that the proposed method improved the efficiency in the process of multimodal disease risk prediction. © The Institution of Engineers (India) 2024.

关键词： Support vector machines

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EHRT-RWB: A Novel Ensemble Hybrid Recurrent Transformer for Multimodal Heart Disease Risk Prediction

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 475-486页

作者： Irene, D. Shiny Peter, J. Selvin Paul Sankarasubramanian, Nivetha Krishnakanth, S. Praveen Department of Computing Technologies School of Computing Colllege of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Campus Chennai India

The disease that contains the highest mortality and morbidity across the world is cardiac disease. Annually millions of people are affected and deaths take place due to cardiac diseases worldwide. There are various diagnostic measures for the prediction of heart diseases. However, these techniques consist of some errors, delays, and high-cost consumption. These limitations affect the patients in many ways such as inadequate medication during the right time, affecting their mental health, affecting their physical health, and sometimes death. Hence, there is a need for an effective automatic multimodal disease risk prediction mechanism related to heart diseases. Therefore, this paper proposes the Ensemble hybrid recurrent transformer-based random wolf bird (EHRT-RWB) algorithm. The details of the patient acquired as input by the heart disease dataset are preprocessed initially and classification is performed with the EHRT-RWB methodology. The RNN method extracts reliable features and helps to study the temporal sequence data. Time series classifications are maintained with the help of the transformer method and the ensemble method integrates advanced cutting-edge technologies for performance augmentation. Finally, the RWB algorithm selects and tunes the hyperparameters of the developed classification method. Experiments conducted using certain performance assessment measures and the comparison with existing strategies show the method achieves superior cardiac disease detection performance with the highest accuracy score of 98.7%. © The Institution of Engineers (India) 2024.

关键词： Birds

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Develop a novel, faster mask region-based convolutional neural network model with leave-one-subject-out to predict freezing of gait abnormalities of Parkinson’s disease

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Neural computing and Applications 2025年第7期37卷 5441-5457页

作者： Ezhilarasi, J. Senthil Kumar, T. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu603203 India

A common symptom of severe Parkinson’s disease (PD) is Freezing of Gait (FoG), a gait disorder that causes sudden difficulty in initiating or maintaining walking. FoG frequently leads to falls and has a detrimental impact on a patient’s regular life. Real-time detection algorithms identify FoG occurrences using wearable sensors. Anticipating FoG in advance allows for pre-emptive cueing, which may prevent the episodes or reduce their severity and duration. This research proposes a Faster Mask Region-based Convolutional Neural Network (FMRCNN) signal processing approach for FoG identification. The model captured gyroscope, magnetometer, and tri-axial accelerometer signals using an inertial measurement device on the left side of the abdomen. The experimental results demonstrate a reduction in the equal error rate to 1.9% in the Leave-One-Subject-Out (LOSO) Cross-Validation (CV) with Long Short Term Memory (LSTM) assessment. Additionally, the tenfold CV evaluation enhances specificity and sensitivity by 0.045 and 0.017, respectively, compared to previous best results. It takes only 0.52 ms to detect a 256-data section. The proposed work uses the LOSO-CV-LSTM to evaluate various machine learning (ML) and deep learning (DL) techniques for FoG detection. The proposed system not only detects FoG but also enhances the automation of PD detection and therapy at an earlier stage. The results demonstrate that the proposed system improves performance measures compared to existing systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Transforming Education with Photogrammetry:Creating Realistic 3D Objects for Augmented Reality Applications

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 185-208页

作者： Kaviyaraj Ravichandran Uma Mohan Department of Computational Intelligence School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India

Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different *** increased use of mobile devices has an even greater *** the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in ***,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational ***,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)*** 3D objects for augmented reality education applications can be challenging and time-consuming for the *** address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)*** for augmented and virtual reality can then utilize these created 3D *** evaluated the proposed pipeline based on the usability of the 3D object and performance ***,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry *** analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above *** study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.

关键词： Augmented reality education immersive learning 3D object creation photogrammetry and StructureFromMotion

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Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-22页

作者： Dennyson, W. Beniel Jothikumar, C. Department of Computing Technologies SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Intelligently connected automobiles have come a long way thanks to the deep integration of cutting-edge networked gadgets and advancements in automotive technology. The increasing connectivity of vehicles has introduced significant cybersecurity concerns, particularly in controller area network (CAN) bus systems, which are crucial for vehicle communication. For this, the research presents a novel hybrid model combining and integrating a long short-term memory (LSTM) neural network and bacterial foraging optimization (BFO) technique to address challenges like identifying fuzzy and denial of service (DoS) attacks on the CAN bus system. The model’s efficacy is demonstrated by evaluating various cyber-attacks from the Car-Hacking dataset. An adaptive feature selection method using BFO to identify optimal CAN bus characteristics for accurate attack detection. The LSTM-based temporal pattern recognition system detects anomalous message sequences and real-time countermeasures for mitigating DoS and fuzzy attacks. Reducing attack detection time to 0.0838 s, an enhancement over LSTM-AE, suggests improving detection speed. With a higher precision of 94.6% and F1-scores of 95.8%, LSTM-BFO outperforms other models based on accuracy, precision, and F1-score under the current experimental setup. © The Author(s) 2025.

关键词： Automotive network Bacterial foraging optimization Controller area network Deep learning Denial-of-service Fuzzy attack

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Ensemble Deep Learning Approaches in Health Care:A Review

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Computers, Materials & Continua 2025年第3期82卷 3741-3771页

作者： Aziz Alotaibi Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of ***,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/*** the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big *** deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning *** ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble *** deep learning has been successfully used in several areas,such as bioinformatics,finance,and health *** this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug *** cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also ***,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and ***,future directions and opportunities for enhancing healthcare model performance are discussed.

关键词： Deep learning ensemble learning deep ensemble learning deep learning approaches for health care health care

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A reinforcement learning based backup queue strategy for reliable and fault tolerant scheduling

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International Journal of Information technology (Singapore) 2025年 1-6页

作者： Archana, R. Kumar, K. Pradeep Mohan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Fog computing has the capability to perform tasks in the local distributed environment within the expected time period. The approaches used for managing the faults in a fog computing environment are not competent to resolve the issues of latency and energy consumption in the network. Allocating and executing all the tasks regardless of how they scale up and completing them within the allotted time frame is crucial for achieving a higher throughput and a lower drop rate. In order to achieve higher scalability, timely execution, and dependability, the given paper introduces a reinforcement learning (RL) backup queue strategy (SEEDBACK_RL) that ensures every task is executed irrespective of its size. The proposed strategy is enhanced to manage the scalability and reliability by identifying the best nodes depending on the type of task. With the use of a simulative environment, the algorithm's efficacy is assessed, and the results show a notable improvement in load balancing, reliability, and network efficiency. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Asynchronous advantage actor critic (A3C) Distributed reinforcement learning Fog computing Internet of things (IoT) Load balancing

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A novel approach on heart disease prediction using optimized hybrid deep learning approach

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Multimedia Tools and Applications 2025年 1-26页

作者： C, Adline Sherifa Kanisha, B. Department of Computing Technologies Faculty of Engg and Technology SRM Institute of Science and Technology Kattankulathur Chennai603 203 India

The condition known as Cardio Vascular Disease can result in heart attacks, Angina, and brain assaults due to the restriction of blood flow to the myocardium. Among the most important causes of death and mortality worldwide, heart disease is also one of the main sources of disability for both young and old people. Moreover, it is one of the reasons behind weariness and mortality on Earth. Interruption can prompt benevolence, adjustments in lifestyle, and tailored therapy interventions, which ultimately lead to peaceful outcomes and reduced healthcare expenses. The data gathered in this question about work is processed beforehand using information cleaning and information standardization techniques. Measurable highlights, higher-order measurable highlights (for example, why measure weights are not measured), correlation-based IDM, weight standard deviation-related entropy, and the degree of relationship between the data and homogeneity, which in turn are removed. From the extricated features, the foremost ideal highlights are chosen via the relief-f demonstration. By selecting the most suitable features, one can prepare the crossover classifier that generates the heart illness prediction. The crossover classifier could be a combination of MLP and O-RBM. To advance the expectation exactness of the anticipated heart malady location show, the weight of Limited Boltzmann Machines is fine-tuned through the Self-Adaptive TLBO (SA- TLBO) demonstration. The result from the crossover classifier is the identified result. This SA-TLBO show is an extended version of the standard TLBO show. By analyzing the "Heart Malady Dataset" of the Kaggle store, it is established that the proposed show has 95% precision and 96% accuracy with an FNR of 0.05. This demonstration is then tested against the real models to show whether a model matches those of the proposed one. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Demonstrations

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