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3rd International Conference on Intelligent Cybernetics Technology and applications, ICICyTA 2023

作者： Singh, Hakam Witarsyah, Deden Misra, Alok Tiwari, Raj Gaurang Chitkara University School of Engineering and Technology Department of Computer Science and Engineering Himachal Paradesh Baddi India Telkom University School of Industrial and System Engineering Information System Departement Bandung Indonesia Lovely Professional University School of Computer Application Punjab Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350394559

Due to its many applications in a variety of domains, including clustering, cloud computing, and power systems, metaheuristic algorithms have attracted a lot of interest from the scholarly community. By using their self-explorative and adaptive processes, these algorithms provide certain benefits over conventional methods. One well-known metaheuristic approach is effective in resolving optimization problems with constraints and without constraints. This technique was inspired by nature, notably the echolocation ability of bat species. The Bat Algorithm (BA) makes use of a powerful search mechanism to provide the best results when compared to other algorithms of the same class. The theoretical underpinnings of the BA are explored in depth in this research, along with several changes and advancements that have improved its functionality. These enhancements include adjustments to search tactics as well as other developments. The report also thoroughly examines how the BA may be used across a variety of fields, demonstrating its competence and adaptability. The BA has been effectively used in several real-world applications, from cloud computing to clustering, and each one is carefully investigated. The study also offers a thoughtful examination of the BA's possible advancements and future possibilities. It also entails investigating novel modifications and tactics that might improve its effectiveness in handling challenging optimization issues. This study seeks to direct practitioners and academics toward more effective and efficient metaheuristic algorithms, thereby stimulating improvements in optimization approaches. It does this by summarising the present state of the BA and imagining its future development. © 2023 IEEE.

关键词： Cloud computing

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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

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computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

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Filtering of Spurious Sonar Signals 14

Filtering of Spurious Sonar Signals

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Srutheeshna, U. Ashraf, Sharik Koshy, Litty Department of Master of Computer Application SCMS School of Engineering &Technology VidyaNagar Karukutty Ernakulam India Naval Physical and Oceanographic Laboratory P.B.NO.6 NGO Quarters Thrikkara Vazhakkala Kochi India Department of computer science & Engineering SCMS School of Engineering & Technology VidyaNagar Karukutty Ernakulam India

Navy uses a harmonic filter to extract the propel sounds in ships. Usually, Sonar processing uses the harmonic filter to remove unwanted harmonics. The proposed system is used for filtering unwanted noise from lower frequency noise using the machine learning technique. Some sonar may have higher frequency sounds, which can be difficult to hear. Using this sound frequency humans can identify their target by experience. Automate these sounds and using a trained neural network, Classify the sounds. Retrieve non-frequency voice and avoid harmonics using a neural network. © Grenze Scientific Society, 2023.

关键词： High pass filters

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Dual-head alternating attention-based adaptive convolutional framework for improving low-resource and imbalanced text classification

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Neural Computing and applications 2025年 1-21页

作者： Agbesi, Victor Kwaku Chen, Wenyu Hossin, Md Altab Ukwuoma, Chiagoziem C. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China School of Innovation and Entrepreneurship Chengdu University No. 2025 Chengluo Avenue 610106 Sichuan Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Low-resource imbalance texts pose significant challenges due to class imbalance, adversely affecting the performance of deep learning approaches. To address this issue, we propose a dual-head alternating attention-based adaptive convolutional framework with label embedding (AltXLM-AdaCLE), a novel framework designed for imbalanced text classification in low-resourced datasets. Our framework features a new alternating attention model, which combines a dual-head attention mechanism with a bidirectional long short-term memory network, facilitating dynamic filter generation that adapts to the input data’s characteristics and enhances the capture of relevant features and long-range dependencies. A key innovation in this study is the dual-head attention mechanism, which utilizes an alternating reading sequence to extract information from multiple document segments, thereby minimizing information loss and improving the understanding of complex relationships. Furthermore, we introduce a method for embedding class labels in a shared vector space with the text, allowing the model to leverage label information throughout the classification process, which is particularly beneficial for imbalanced datasets. Our framework also incorporates an adaptive convolutional layer that processes text features and label embeddings, generating more representative features of the underlying data distribution and improving classification performance. Experimental results on six low-resourced imbalance text datasets validate the effectiveness of AltXLM-AdaCLE with an average AUC gain of 26.42%, demonstrating its superiority over existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Vector spaces

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Malaria and Pneumonia Disease Prediction Using Deep Learning 2

Malaria and Pneumonia Disease Prediction Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Vijendra Singhal, Manoj Dwivedy, Bhoopendra Jain, Pallavi Ritika Abes Institute of Technology Department of Information Technology Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Information Technology Greater Noida India School of Computer Science and Engineering Galgotia's University Greater Noida India School of Computer Application and Technology Galgotia's University Greater Noida India

ISBN: (纸本)9798350371055

Early identification, intervention, and efficient treatment planning all rely heavily on disease prediction. This study examines the methods for disease prediction based on deep learning, concentrating on pneumonia and malaria in particular. Pneumonia and malaria are two common, sometimes fatal infections, especially in underdeveloped areas. Traditional diagnostic techniques for these disorders can be time-consuming and frequently need for specialized knowledge. Therefore, it is necessary to investigate automated and reliable illness prediction methods. This study's goal is to assess how well deep learning algorithms can foresee malaria and pneumonia from medical imaging data. The study makes use of a broad dataset that includes chest X-ray pictures for the identification of pneumonia and blood smear images with malaria infection. To improve the dataset's quality and variety, preprocessing procedures including picture normalization and augmentation are used. The results of this research demonstrate the potential of deep learning algorithms for malaria and pneumonia prediction, which advances the field of medical diagnostics. These automated prediction algorithms could help medical personnel diagnose patients quickly, allowing for quick treatment to start and better patient outcomes. © 2024 IEEE.

关键词： Backpropagation CNN Deep Learning Inspection V3 Model Jupiter Python Transfer Learning

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Deploying Reinforcement Learning Approaches for Smart Home Automation 15

Deploying Reinforcement Learning Approaches for Smart Home A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sen, Amit Prakash Goyal, Manish Kumar Shalini Arka Jain University Department of Electrical & Electronics Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Management School of Mangement - Pg Karnataka Bangalore India

ISBN: (纸本)9798350370249

Reinforcement mastering is an area of synthetic intelligence that enables machines to adopt actions that optimize their environment or maximize a pre-defined reward. This paper investigates the potential makes use of reinforcement studying for clever domestic automation. Some of distinctive algorithms had been advanced that leverage reinforcement getting to know strategies for clever houses. The paper discusses the advantages of using reinforcement learning for optimizing electricity consumption, increasing comfort, and presenting protection as well as the potential challenges of such deployment. The paper also evaluations several present reinforcement studying-based totally methods applicable for smart domestic automation and highlights ability studies possibilities to address present methodological gaps in an effort to enable the reliable deployment of reinforcement getting to know for clever domestic automation. © 2024 IEEE.

关键词： Deployment Distinctive Domestic Methodological Potential Protection Reinforcement

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Bharat Tripathi Madan Lal Saini Manish Jain Apoorva Saxena Narendra Pal Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids. learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion.

关键词： Hair Privacy Neurons Signal processing algorithms Signal processing Hearing aids Fall detection Transient analysis Recruitment Microphones

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Design and Development of a Smart Antenna System for Electromagnetic Compatibility 2

Design and Development of a Smart Antenna System for Electro...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Goyal, Manish Kumar Malathi, H. Choudhary, Binod Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India School of Sciences Department of Biotechnology and Genetics Karnataka Bangalore India ARKA JAIN University Department of Electrical & Electronics Engineering Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

This technical abstract introduces the design and development of a smart Antenna gadget for Electromagnetic Compatibility (EMC). The clever antenna device pursuits to lessen unwanted interference in wireless verbal exchange systems, and also to locate time Electromagnetic interference (EMI) alerts and optimize running conditions. This antenna gadget is built on both software and hardware systems. Its design relies on the improvement of antenna arrays, which make use of adaptive sign-processing techniques with the intention of reaping the desired EMC necessities. The antenna array, with its signal-processing architecture, gives the possibility of at the same time optimizing radiation samples and electrical parameters by way of electronically scanning the antenna in more than one direction and taking into consideration disturbances in the environment. The layout additionally incorporates an optimized weight matrix that is used to optimize the antenna's signal-processing structure. Subsequently, the advanced system is simulated and evaluated for its overall performance, and the results are compared with theoretical predictions. © 2024 IEEE.

关键词： Antenna arrays

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Leveraging Adaptive Algorithms for Real-Time Data Analysis 15

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

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