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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Ransome Ware Detection Using Machine Learning and Deep Learning Models 12th

Ransome Ware Detection Using Machine Learning and Deep Learn...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Vivek, Vinnakota Sai Lokesh, Vadlamudi Kaushik, Tata Kumaran, U. Umah, B. Devarajan, Gopal Ganesh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

This research addresses the critical challenge of ransomware detection through the use of deep learning and machine learning methods. Because ransomware is a serious threat to cybersecurity, it is imperative that advanced techniques be used for prompt detection and mitigation. Using a large dataset, the study concentrates on pertinent features necessary for efficient machine learning model training. The suggested model shows promising results in identifying and averting ransomware attacks by utilizing cutting-edge algorithms. The model’s effectiveness is confirmed by the evaluation metrics (F1 score, recall, accuracy, and precision). By providing a proactive defense against emerging ransomware threats, the findings enhance cybersecurity measures. The study not only provides a strong methodology for ransomware detection but also encourages more research in the area with the goal of continuously bolstering cyber defenses against evolving cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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PROMO: PROactive MObility-support model for task scheduling in fog computing

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International Journal of computers and Applications 2022年第11期44卷 1092-1101页

作者： Kaur, Navjeet Kumar, Ashok Kumar, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fog computing

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A task clustering based QoS aware scheduling algorithm for task execution in cloud-Iot model for education services

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Multimedia Tools and Applications 2023年第29期82卷 44783-44800页

作者： Chowdhary, Sunil Kumar Rao, A.L.N. Faculty of Computer Science and Engineering Dr. A P J Abdul Kalam Technical University Uttar Pradesh Lucknow India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Noida India

Some of the conventional approaches to task scheduling are discussed in this section. The scheduling of tasks in the Internet of Things (IoT) application is a complex job in cloud computing due to the heterogeneity characteristics of the IoT. Therefore, an efficient scheduling approach is vital in improving the performance of the Cloud-IoT applications. The improvement in QoS mainly depends on efficient task scheduling and allocating the task to the appropriate Virtual Machine (VM). This paper uses a clustering-based approach to design an optimal solution for task scheduling and VM allocation for IoT applications in education services. The workflow is simplified with horizontal and vertical clustering, and VM is allocated. The task is classified into coarse and fine-grained, based on the average execution time of the total number of tasks. The average execution time is calculated based on the best and worst performance of VM. The vertical clustering approach clusters the tasks into sequential and parallel, thereby turning the huge volume of tasks into simple ones. The horizontal clustering approach selects the VM with the help of a bipartite matching graph which enhances the Quality of Service (QoS). The proposed task clustering has been evaluated in metrics like makespan, response time, throughput, total execution time, task completion time, and VM utilization. The proposed approach achieved a makespan of 2500 ms and throughput of 83 Mbps for 100 tasks, a response time of 0.12 ms, a total execution time of 640 ms, and a total task completion time of 340,000 ms for a 2 arrival rate based on performance evaluation results. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Quality of service

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Real-Time Pattern Recognition in Augmented Reality Application Through the Integration of TensorFlow Lite and Unity

Real-Time Pattern Recognition in Augmented Reality Applicati...

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2024 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Mulyo, Dwi Rante, Hestiasari Sukaridhoto, Sritrusta Arissabarno, Cahyo Politeknik Elektronika Negeri Surabaya Department of Creative Multimedia Technology Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350368802

The integration of machine learning with augmented reality (AR) is vital for traditional batik preservation, especially in real-time pattern recognition. Traditional AR applications often rely on external servers, increasing latency and reducing interaction fluidity. This paper presents real-time batik pattern recognition using quantized TensorFlow Lite (TFLite) in a mobile AR application. Using the Single Shot MultiBox Detector (SSD) architecture with quantization, model size is reduced by 75%, while maintaining 96% accuracy, 0.06 seconds inference time, and 46 frames per second (FPS). Compared to YOLO, which achieves 93% accuracy and 40 FPS, TFLite offers faster inference and reduced computational load, making it ideal for mobile AR. By embedding TFLite into an AR platform, users can interact with batik patterns in real-time without server reliance. This research shows that quantized TFLite enhances mobile AR performance, balancing speed, accuracy, and efficiency. © 2024 IEEE.

关键词： Object detection

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A Neuromorphic Spiking Neural Network Using Time-to-First-Spike Coding Scheme and Analog Computing in Low-Leakage 8T SRAM

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2024年第5期32卷 848-859页

作者： Chen, Chao-Yu Dai, Yan-Siou Hong, Hao-Chiao Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Novatech Microelectronics Corporation Technology Innovation Department Hsinchu300092 Taiwan

This article demonstrates the first functional neuromorphic spiking neural network (SNN) that processes the time-to-first-spike (TTFS) encoded analog spiking signals with the second-order leaky integrate-and-fire (SOLIF) neuron model to achieve superior biological plausibility. An 8-kb SRAM macro is used to implement the synapses of the neurons to enable analog computing in memory (ACIM) operation and produce current-type dendrite signals of the neurons. A novel low-leakage 8T (LL8T) SRAM cell is proposed for implementing the SRAM macro to reduce the read leakage currents on the read bitlines (RBLs) when performing ACIM. Each neuron's soma is implemented with low-power analog circuits to realize the SOLIF model for processing the dendrite signals and generating the final analog output spikes. No data converters are required in our design by virtue of analog computing's nature. A test chip implementing the complete output layer of the proposed SNN was fabricated in 90-nm CMOS. The active area is 553.4 118.6 {m}{2}. The measurement results show that our SNN implementation achieves an average inference latency of 196 ns and an inference accuracy of 81.4%. It consumes 242 {W} with an energy efficiency of 4.74 pJ/inference/neuron. © 1993-2012 IEEE.

关键词： Neural networks

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Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

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SN computer Science 2024年第1期5卷 190页

作者： Almahadin, Ghayth Subburaj, Maheswari Hiari, Mohammad Sathasivam Singaram, Saranya Kolla, Bhanu Prakash Dadheech, Pankaj Vibhute, Amol D. Sengan, Sudhakar Department of Networks and Cybersecurity Faculty of Information Technology Al Ahliyya Amman University Country Amman Jordan School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India Department of Networks and Cybersecurity Information Technology Al Ahliyya Amman University Amman Jordan Department Computing Technology School of Computing SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Chennai 603203 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan (SKIT) Rajasthan Jaipur 302017 India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) MH Pune 411016 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Identifying suspicious activities or behaviors is essential in the domain of Anomaly Detection (AD). In crowded scenes, the presence of inter-object occlusions often complicates the detection of such behaviors. Therefore, developing a robust method capable of accurately detecting and locating anomalous activities within video sequences becomes crucial, especially in densely populated environments. This research initiative aims to address this challenge by proposing a novel approach focusing on AD behaviors in crowded settings. By leveraging a spatio-temporal method, the proposed approach harnesses the power of both spatial and temporal dimensions. This enables the method to effectively capture and analyze the intricate motion patterns and spatial information embedded within the continuous frames of video data. The objective is to create a comprehensive model that can efficiently detect and precisely locate anomalies within complex video sequences, specifically those featuring human crowds. The efficacy of the proposed model will be rigorously evaluated using a benchmark dataset encompassing diverse scenarios involving crowded environments. The dataset is designed to simulate real-world conditions where millions of video footage need to be continuously monitored in real time. The focus is on identifying anomalies, which might occur within short time frames, sometimes as brief as five minutes or even less. Given the challenges posed by the massive volume of data and the requirement for rapid AD, the research emphasizes the limitations of traditional Supervised Learning (SL) methods in this context. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection Autoencoders LSTM Machine learning Spatio-temporal features Suspicious activities

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Leveraging Hybrid CNN Model for Enhanced Classification on Imbalanced Datasets 1

Leveraging Hybrid CNN Model for Enhanced Classification on I...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Murugan, R. Sekar, K. Sariga, G. Nikhilesh, V. Tejashwini, J.S. Dheepa, T. National Institute of Technology Silchar Department of Ece Silchar India Rajalakshmi Engineering College Department of Aiml Chennai India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This study addresses the challenge of class imbalance in the CIFAR-10 dataset, where certain classes are significantly underrepresented. To tackle this issue, data augmentation and resampling techniques, particularly oversampling, are employed to achieve a balanced class distribution. A Convolutional Neural Network (CNN) is trained using a class-weighted loss function to effectively mitigate residual imbalances. The model's performance is evaluated on test images, with a ResNet model incorporated for comparison. This hybrid approach enables a comprehensive assessment of classification accuracy and robustness. The proposed model achieves accuracy, precision, and recall scores of 0.95, 0.88, and 0.93, respectively. The results demonstrate significant improvements in the prediction accuracy of minority classes, underscoring the effectiveness of the applied methods. This study highlights the critical role of oversampling and class-weighted loss functions in addressing class imbalance challenges, ultimately enhancing the performance of image classification models. © 2025 IEEE.

关键词： Convolutional neural networks

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Exploring the Potential of Quantum Computing for Revolutionary Problem-Solving and Computational Advancements

Exploring the Potential of Quantum Computing for Revolutiona...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Guru Vimal Kumar, M. Paramesh, J. Sriram, K.P. Anbalagan, E. Sasikumar, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India Information Technology St. Joseph's Institute of Technology OMR Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

Quantum computing is a model of computing a new approach to perform and solve computational problems and comes up with amazing advantages such as exponential speed-up of specific tasks that lead to a revolution of industries. In this study, Quantum Computing potential is discussed in multiple fields - drug discovery and optimization, and quantum machine learning contrasted with traditional methods based on the running time, accuracy, and scalability of quantum and classical algorithms. Applying several quantum algorithms to molecular simulation, optimization problem, and image classification data sets, quantum algorithms solved problems with at least 70% less number of iterations and at worse the same accuracy as classical methods. For instance, researchers noted that quantum neural networks performed better than classical networks in image recognition. The quantum algorithms in this study were implemented and simulated using IBM Qiskit, D-Wave's Ocean SDK and TensorFlow Quantum. Based on the literature reviewed;it was established that quantum computing has potential to revolutionalise endeavours in drug design, cryptography and artificial intelligent applications. While there are issues in qubit stability quantum decoherence, errors and other related complications, it is predicted that all these problems will be solved by qubit stability and quantum error correction shortly and therefore, realistic, scalable quantum systems are in the next few years. Further research is required on the quantum technology one which may bring a revolution in the way different industries solve their problems. © 2024 IEEE.

关键词： Qubits

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Recognition of Human Emotions Through The Use of Facial Expressions

Recognition of Human Emotions Through The Use of Facial Expr...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Singh, Nongmeikapam Thoiba Chaudhary, Amrita Dhiman, Richa Singh, Akanksha Chanu, Asem Debala Chandigarh University Department of Computer Science and Engineering Punjab India Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798350308167

Emotions play a pivotal role in human life, influencing communication, relationships, and productivity. Recognizing and understanding these emotions is crucial, and various methods have been explored, including monitoring brain activity. However, traditional approaches, such as electroencephalography (EEG), are costly and time-consuming, often yielding accuracy rates ranging from 31% to 81%. In this study, we propose an alternative approach: accurately detecting human emotions through facial expressions. Leveraging an existing simulator and neural networks, we achieved remarkable accuracy rates of up to 96%. We present the methodology, including image processing, differential readings, and facial expression detection, and compare our approach to other methods. The study sheds light on the potential of neural network-based systems for high-accuracy emotion recognition from facial expressions, offering a promising avenue for human-computer interaction and intelligent surveillance systems. © 2023 IEEE.

关键词： brain activity emotion recognition facial expression fuzzy logic human-computer interaction neural network

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