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International Conference on Software Technology and Engineering (ICSTE)

作者： Rajermani Thinakaran J. Somasekar Vikram Neerugatti P. Guru Saran Faculty of Data Science and Information Technology INTI International University Negeri Sembilan Malaysia Department of Computer Science and Engineering Jain (Deemed-to-be University) Banglore Karnataka India

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

Efficient waste management relies on precise waste classification, which facilitates recycling, composting, and appropriate disposal. Conventional waste classification methods often involve manual sorting or simple rule-based systems, which are labor intensive and prone to errors. This study investigates the application of Convolutional Neural Networks (CNNs) for automated waste classification using image data. CNNs have shown significant success in various image classification tasks due to their ability to learn hierarchical features directly from pixel values. The methodology outlined covers data collection, preprocessing, model architecture design, and training procedures for the CNN model. Experimental results demonstrate the effectiveness of the CNN approach in accurately categorizing various waste types. Additionally, we discuss the practical implications of CNN-based waste classification systems for real-world waste management applications. By leveraging CNNs, waste management processes can be optimized, leading to better resource utilization and environmental sustainability. The authors also highlight potential avenues for future research, including exploring advanced CNN architectures and integrating emerging technologies like Internet of Things (IoT) sensors for improved waste management solutions. Overall, this study emphasizes the potential of CNNs to transform waste classification practices and enhance the efficiency of waste management systems.

关键词： Waste management Training computer architecture Transforms data models Software Sensors Convolutional neural networks Internet of Things Sorting

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COMMUNICATION-EFFICIENT FEDERATED LEARNING BASED ON EXPLANATION-GUIDED PRUNING FOR REMOTE SENSING IMAGE CLASSIFICATION

arXiv

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arXiv 2025年

作者： Klotz, Jonas Büyüktaş, Barış Demir, Begüm BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany

Federated learning (FL) is a decentralized machine learning paradigm, where multiple clients collaboratively train a global model by exchanging only model updates with the central server without sharing the local data of clients. Due to the large volume of model updates required to be transmitted between clients and the central server, most FL systems are associated with high transfer costs (i.e., communication overhead). This issue is more critical for operational applications in remote sensing (RS), especially when large-scale RS data is processed and analyzed through FL systems with restricted communication bandwidth. To address this issue, we introduce an explanation-guided pruning strategy for communication-efficient FL in the context of RS image classification. Our pruning strategy is defined based on the layer-wise relevance propagation (LRP) driven explanations to: 1) efficiently and effectively identify the most relevant and informative model parameters (to be exchanged between clients and the central server);and 2) eliminate the non-informative ones to minimize the volume of model updates. The experimental results on the BigEarthNet-S2 dataset demonstrate that our strategy effectively reduces the number of shared model updates, while increasing the generalization ability of the global model. The code of this work will be publicly available at https://***/rsim/FL-LRP. © 2025, CC BY.

关键词： Federated learning

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End-to-End Congestion Avoidance Scheme for Underwater High Speed Communication Networks 3

End-to-End Congestion Avoidance Scheme for Underwater High S...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shivank Jain, Romil Reena, R. Kadam, Prema S. Das, Sunanda Singh, Swati Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

The cease-to-give up Congestion Avoidance Scheme for Underwater high pace verbal exchange Networks, commonly known as E2E-UCAN, is a critical technique for lowering plausible throughput by using efficiently warding off congestion inside a community. E2E-UCAN is a disbursed congestion control mechanism which uses statistics obtained from cease-to-end feedbacks to regulate sending prices on the give up systems. By means of monitoring the differences among expected network overall performance and actual performance, E2E-UCAN is able to pick out congestion stages inside the device, and is therefore able to regulate to prevent congestion before it happens. This enables to improve the stableness of the community and to increase the throughput of a network. Moreover, E2E-UCAN is likewise capable of efficaciously coping with queuing, avoiding packet loss in non-uniform networks, and in preferred, permitting applications to maximize performance. © 2023 IEEE.

关键词： Community Congestion Lowering Mechanism Performance

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Cloud Computing Malware Detection Using Feature Selection Based on Optimized White Shark Algorithm (WSO)

Cloud Computing Malware Detection Using Feature Selection Ba...

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Cyber Resilience (ICCR), International Conference on

作者： Hossam M. J. Mustafa Mohammad H. Al-Zyod Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Al-Balqa Applied University Salt Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The majority of information technology organizations nowadays prefer cloud computing as their main choice due to improvements in information technology. However, as a result of cloud computing's vast and open architecture, which makes it vulnerable to hacker attacks, the security of data remains an important concern. As a consequence, ensuring the security and integrity of data has become crucial while constructing an abnormal intrusion detection system utilizing machine learning algorithms represents a method to potentially detect intrusions. Recurring clouding as well as prevention. Using machine learning algorithms and revised intrusion datasets is necessary to overcome the challenge of finding effective and optimal network intrusion detection systems. The White Shark Optimization approach works along with Support Vector Machines (SVM). The suggested algorithm's effectiveness is assessed using metrics including accuracy and recall. When results of previous research employing both of the two datasets (NSL-KDD and Kyoto) for predicting the detection of abnormal infiltration are compared to the proposed algorithm based on SVM, it is determined that the white shark optimization algorithm produced the best accuracy, at 99.8%.

关键词： Support vector machines Measurement Cloud computing Machine learning algorithms Sharks Organizations Prediction algorithms

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Advancements in Skin Cancer Detection: A Comprehensive Review of Convolutional Neural Network Approaches

Advancements in Skin Cancer Detection: A Comprehensive Revie...

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International Conference on Software Technology and Engineering (ICSTE)

作者： Rajermani Thinakaran J. Somasekar Vikram Neerugatti Krishna Ganga Faculty of Data Science and Information Technology INTI International University Negeri Sembilan Malaysia Department of Computer Science and Engineering Jain (Deemed-to-be University) Banglore Karnataka India

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

Skin cancer is one of the most prevalent types of cancer globally, with its incidence steadily rising over the past decades. Early and accurate detection of skin cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated image analysis and classification, offering promising results in various medical imaging tasks, including skin cancer detection. This paper provides a comprehensive review and analysis of CNN-based approaches for skin cancer classification. We discuss the challenges associated with traditional methods of skin cancer diagnosis and highlight the potential of CNNs in addressing these challenges. Furthermore, we survey state-of-the-art CNN architectures, dataset collections, preprocessing techniques, and evaluation metrics commonly used in skin cancer classification tasks. We also delve into the transfer learning and data augmentation strategies employed to improve model generalization and robustness. Additionally, we review recent advancements in interpretability techniques for CNNs, which enhance the transparency and trustworthiness of automated skin cancer diagnosis systems. Finally, we discuss the limitations and future directions of CNN-based approaches in skin cancer classification, emphasizing the need for large-scale, diverse datasets, standardized evaluation protocols, and interdisciplinary collaborations to facilitate the clinical translation and deployment of CNN-based diagnostic tool.

关键词： Surveys Translation Protocols Reviews Transfer learning Mortality Software Robustness Convolutional neural networks Skin cancer

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Exploring the Use of Machine Learning Techniques for Enhancing Real-Time data Access 3

Exploring the Use of Machine Learning Techniques for Enhanci...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rampal, Sourav Shreenidhi, H.S. Shrivastava, Manish Singh, Akhilendra Pratap Reena, R. Kumar, Santosh D. Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Actual-time statistics to date has updated, increasingly vital as the quantity of virtual information maintains updated growth. Effective choice-making depends on the capability to update access and analyze records. Device up-to-date techniques provide the capacity up-to-date enhance actual-time statistics up-to-date by using offering insights from disparate pieces of information. This paper addresses the potential benefits of using a system with date knowledge and updated, up-to-date strategies to broaden extra clever statistics up to date updated systems. It summarizes the contemporary nation of the art and highlights studies in machine learning and up-to-date imaginative and prescient real-time information up-to-date. It additionally discusses present-day challenges and shows viable instructions for future studies. The studies network up-to-date recall how gadgets are up to date know that updated strategies may be used to facilitate real-time get entry updated the maximum precious pieces of facts. © 2023 IEEE.

关键词： Machine learning

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Parallel Image Encryption Algorithm Using Partitioned Cellular Automata on Graphic Processor Unit

Parallel Image Encryption Algorithm Using Partitioned Cellul...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Fazlali, Mahmood Alihoseini, Amirhossein Zarei, Ebrahim Tabani, Myasar Pourmoafi, Seyedali University of Hertfordshire Cybersecurity and Computing Systems Research Group Hatfield United Kingdom Shahid Beheshti University Faculty of Mathematical Sciences Dept. of Data and Computer Sciensce Tehran Iran Khansar Campus University of Isfahan Dep. of Computer Science Isfahan Iran

ISBN: (纸本)9798350346473

Image encryption is a reliable method for securely transmitting images over a network. The time required to encrypt and decrypt an image in online applications is also very important. Although cellular automata cryptography is an appropriate technique for parallelizing and accelerating cryptographic methods, its capacity cannot be demonstrated only in multi-core platforms. Thus, it is needed to parallelize cellular automata cryptography on Graphic Processor Units (GPUs) in order to significantly decrease the encryption/decryption time. In this paper, we propose a new parallel algorithm for two-dimensional cellular automata cryptography that is implemented on GPU. The proposed algorithm uses multiple threads at once to accelerate the bit-level permutation and substitution operations by taking into account the capacity of cellular automata in parallel processing. According to the study experimental findings, the proposed algorithm performs faster on GPU compared to a multicore platform while maintaining the same level of security in comparison to the serial algorithm. © 2023 IEEE.

关键词： Graphics processing unit

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Beyond the Limit of Weight-Sharing: Pioneering Space-Evolving NAS with Large Language Models

Beyond the Limit of Weight-Sharing: Pioneering Space-Evolvin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiu Su Shan You Hongyan Xu Xiuxing Li Jun Long Yi Chen Chang Xu Big Data Institute Central South University School of Computer Science Faculty of Engineering The University of Sydney SenseTime Research School of Computer Science and Engineering University of New South Wales Institute of Computing Technology Chinese Academy of Sciences School of Computer Science Hong Kong University of Science and Technology

Large language models (LLMs) offer impressive performance across diverse fields, but their increasing complexity raises both design costs and the need for specialized expertise. These challenges are intensified for Neural Architecture Search (NAS) methods reliant on weight-sharing techniques. This paper introduces GNAS, a new NAS method that boosts the search process with the aid of LLMs for efficient model discovery. With insights from existing architectures, GNAS swiftly identifies superior models that can adapt to changing resource constraints. We provide a mathematical framework to facilitate the transfer of knowledge across different model sizes, thereby improving search efficiency. Our experiments conducted on ImageNet, NAS-Bench-Macro, and ChannelBench-Macro confirm the effectiveness of GNAS across both CNN and Transformer architectures.

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data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Joko Pebrianto Trinugroho Alam Ahmad Hidayat Rudi Nirwantono Faisal Asadi Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment.

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Robust WCET Estimation via Ensemble Learning with Fractional-Order Legendre Function Kernels

Robust WCET Estimation via Ensemble Learning with Fractional...

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Applied Research in Electrical Engineering (AREE), National and International Conference on

作者： Amir Hussein Ataei Monfared Mahdi Movahedian Moghaddam Sheedeh Sharif-Bakhtiar Kourosh Parand Keivan Borna Department of Computer Science Faculty of Mathematics and Computer Science Kharazmi University Tehran Iran Department of Computer and Data Sciences Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran Department of Cognitive Modeling Institute for Cognitive and Brain Sciences Shahid Beheshti University Tehran Iran

ISBN: (数字)9798350377170

ISBN: (纸本)9798350377187

Estimating Worst-Case Execution Time (WCET) as a regression problem has become increasingly challenging due to the complexity of modern hardware and software systems. Traditional statistical methods often fall short of providing accurate and reliable estimates. To address these limitations, ensemble learning methods have emerged as a promising approach for capturing edge cases in execution scenarios. This paper proposes a novel ensemble model that integrates fractional-order Legendre functions (FLF) to enrich feature representation. This integration reduces correlations between individual models within the ensemble, enhancing generalization capabilities. Our method achieves significant reductions in Mean Squared Error (MSE) for WCET estimates while maintaining high levels of safeness. Specifically, the proposed model maintained an error below one across most benchmarks, with safeness levels of $\% 100$ in 13 out of 16 benchmarks. These findings underscore the efficacy of our approach and its potential to provide robust and accurate WCET estimates for complex systems.

关键词： Support vector machines Accuracy Upper bound Estimation Benchmark testing Real-time systems Complexity theory Ensemble learning Time factors Kernel

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