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Intelligent surveillance support system

Discover Internet of Things 2023年第1期3卷 9页

作者： Saketh, Meduri Nandal, Neha Tanwar, Rohit Reddy, B. Pradyun Department of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Engineering Sreenidhi Institute of Engineering and Technology Hyderabad India

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance. © The Author(s) 2023.

关键词： Face recognition

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Aggregating the response in time series lag models using quad weight kernel: impact of environmental factors on schizophrenia admissions

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International Journal of Information Technology (Singapore) 2024年 1-18页

作者： Thomas, Jobin Parameswaran, Murali School of Computer Science and Engineering Presidency University Bengaluru India

There has been a growing apprehension in the past few years concerning the issue of pollution and climate change. Several articles have shown the impact of air pollutants and atmosphere factors like temperature and relative humidity on health. This encompasses a deterioration in cognitive function and a heightened susceptibility to neurological diseases like schizophrenia. This work constructed a new dataset for hospital admissions of schizophrenia patients and daily environmental values from various locations in Bangalore City, India. The number of admissions to hospitals is used as a proxy for the incidence of schizophrenia emergence. In this kind of time series data where a response has a delayed impact on dependent variables, distributed lag models (DLM) are applicable. The newly created data is used to analyze the impact of pollution and climate factors on hospital admissions in Bengaluru, India. We have compared the applicability of the aggregated response technique using the distributed lag non-linear model (ARDLNM) in comparison with existing techniques, the distributed lag non-linear model and the non-linear autoregressive distributed lag model. ARDLNM using the Epanechnikov kernel showed improved performance over DLNM by 25%, 15%, 17% and 72% for performance metrics MSE, MAE, MAPE and R² respectively. Within aggregation methods, we have introduced a quad-weight kernel that further improved performance over the Epanechnikov kernel by 17% in terms of MSE. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Air pollution Environmental factors Machine learning Time lag effect Time series analysis

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

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Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

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SndGLM-SA: A Social Network Debate Text Generation Model Based on Sentiment Analysis

SndGLM-SA: A Social Network Debate Text Generation Model Bas...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Tian, Tao School of Computer Science and Engineering Chengdu China

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367942

Text generation for social network debates aims to generate clear and logical dialogue texts based on a correct understanding of the context. This task involves context sentiment analysis and providing factual information to support statements that align with the predetermined stance, while refuting viewpoints from opposing stances. This has significant application value in the era of We-Media. For instance, opinion leaders or influencers on social networks offer examples or advice to others based on their own views and statements. However, their opinions may contain errors or biases, potentially leading to widespread behavioral or opinion misguidance. To mitigate such misguidance, it is necessary to use artificial intelligence technology to intelligently generate rebuttal arguments. To this end, this paper proposes a debate text generation model based on sentiment analysis, aimed at generating rebuttal and corrective debate texts. A sentiment analysis module is introduced to make fine-grained sentiment judgments on the text, while a knowledge enhancement module is used to improve the model's commonsense reasoning ability, ultimately generating debate texts. Experimental results on the dataset show that the proposed model performs better than the baseline methods in the task of debate text generation. © 2024 IEEE.

关键词： Knowledge engineering Analytical models Sentiment analysis Social networking (online) Network analyzers Speech enhancement Commonsense reasoning

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Enhancing Fabric Defect Detection with Attention Mechanisms and Optimized YOLOv8 Framework

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IEEE Access 2025年 13卷 96767-96781页

作者： Mao, Yonghua Wang, Guowen Ma, Yingcang Gui, Xiaolin Xi’an Polytechnic University School of Computer Science School of Science Xi’an710048 China Xi’an Jiaotong University School of Electronic and Information Engineering Xi’an710049 China

Fabric defect detection is a critical task in the textile industry, requiring high precision and recall to ensure effective quality control. This study presents an enhanced YOLOv8-based framework that integrates novel attention mechanisms and advanced architectural modules to improve detection accuracy and robustness. The framework incorporates the SimAM attention mechanism within the SPPF module and adopts an optimized Dilation-wise Residual (DWR) structure in the backbone. Comprehensive ablation studies and comparisons with state-of-the-art methods validate the effectiveness of the proposed approach. The enhanced model achieves a mAP50-95 of 74.3%, outperforming the baseline by 4.7 percentage points, with marked improvements in detecting challenging defect categories. While the framework demonstrates significant advancements, limitations in dataset diversity and computational efficiency are acknowledged. Future work will focus on resource optimization, dataset augmentation, and extending the framework’s applicability to other domains. © 2013 IEEE.

关键词： Textile industry

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Causal Label Enhancement

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IEEE Transactions on Knowledge and Data engineering 2024年第7期37卷 3933-3946页

作者： Liu, Xinyuan Zhu, Jihua Yue, Kun Zheng, Qinghai Li, Zhongyu Tian, Zhiqiang Zhu, Mingchen Xi'an Jiaotong University School of Software Engineering Xi'an China Yunnan University School of Information Science and Engineering Kunming China Fuzhou University College of Computer and Data Science Fuzhou China

Label enhancement (LE) is still a challenging task to mitigate the dilemma of the lack of label distribution. Existing LE work typically focuses on primarily formulating a projection between feature space and label distribution space from discriminative model perspective, which preserves the relevance consistency that the sign of recovered label distribution should be consistent with the logical label. Different from previous algorithms, we formulate this problem from a causal perspective and present a novel LE method via the structured causal model (LESCM). Specifically, the proposed LESCM deliberates establishing the causal graph with assuming that label distribution is a cause of feature and logical label, which naturally satisfies the definition of label distribution learning (LDL). With capturing the underlying causal relationships, we can significantly boost the interpretability and identifiability of label enhancement. Meanwhile, except for the relevance consistency, LESCM are encouraged to sustain the order consistency that assigns higher description degree of the recovered label distribution to the positive labels, as compared with the negative labels. Empirically, sufficient experiments on several label distribution learning data sets validate the effectiveness of LESCM. © 1989-2012 IEEE.

关键词： Contrastive Learning

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An Efficient Approach for VM and Database Segmentation of Cloud Resources Over Cloud Computing

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SN computer science 2024年第8期5卷 977页

作者： Manoli, Sunil Metipatil, Prabhuraj Raghavendra Nayaka, P. School of Computer Science and Engineering REVA University Bangalore India

Virtual machine (VM) clusters are used in cloud computing models to safeguard resources from failure and provide redundancy. Tasks for cloud users are planned by choosing appropriate resources to carry out the work within the virtual machine cluster. Issues with pre-configuration, downtime, complicated backup procedures, and disaster management plague the current VM clustering methods. High availability resources with dynamic and on-demand configuration are provided by virtual machine infrastructure. To improve efficiency and availability, the suggested technique supports the VM clustering process, which places and allocates virtual machines (VMs) based on the size of the requested task and the bandwidth level based on the migration, the suggested clustering procedure is divided into pre- and post-clustering stages. The task and bandwidth classification procedure groups jobs that can run in a virtual machine cluster with sufficient bandwidth. Depending on the VM’s availability inside the cluster, the bandwidth is mapped to the virtual machine. Several performance metrics, including bucket size, task execution time, VM lifetime, and VM utilization, are used in the VM clustering process. The suggested VM clustering’s primary goal is to map tasks to bandwidth-efficient virtual machines (VMs) in order to achieve high availability and dependability. As opposed to current algorithms, it shortens the time allotted for tasks and their execution. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bandwidth Cloud computing Database segmentation Virtual machine (VM) VM clustering

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An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits

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Chinese Journal of Electronics 2024年第5期33卷 1165-1180页

作者： Yuhao ZHOU Zhenxue HE Jianhui JIANG Xiaojun ZHAO Fan ZHANG Limin XIAO Xiang WANG School of Software Engineering Tongji University Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.

关键词： Integrated circuits Microorganisms Annealing Logic circuits Circuits Benchmark testing Delays

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DMFCNN-HBO: deep maxout fusion convolutional neural network model enabled with honey badger optimization for DDoS attack detection

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2347-2354页

作者： Rakshe, Dhananjay Shripad Jha, Sweta Bhaladhare, Pawan R. School of Computer Science and Engineering Sandip University Maharashtra Nashik India

In recent years, Distributed Denial of Service (DDoS) attacks have developed one of the greatest communal and disrupting threats to network security. These attacks flood focused systems with traffic, establishment them inaccessible to valid users. The growing intricacy and difficulty of DDoS attacks involve improving strong and adaptable detection methods. Existing DDoS detection systems struggle to discriminate between real and fraudulent network traffic, specifically with high traffic volumes and developing attack approaches. In this study, we proposed a novel Deep Maxout Fusion Convolutional Neural Network (DMFCNN) enabled hybridization model for DDoS attack detection. In this study, we collected the data from the CICIDS2017 dataset for DDoS attack detection. Next, we divide the dataset into training and testing sets. Then, we used a preprocessing methodology, including Min–max normalization. Honey Badger Optimization (HBO) is applied for feature selection. The proposed method is compared to the other traditional algorithms. The overall performance is computed in terms of recall (98.75%), accuracy (95.03%), precision (97.26%), and F1-score (98.03%). These results demonstrate the model’s ability to identify different forms of DDoS attacks with high accuracy, improving network security robustness. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： DDoS attack detection Deep maxout fusion convolutional neural network (DMFCNN) Hybridization model Network security

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