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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Thirupathi, Chaithanyaka Yeddeli Sundaram, Jawahar Yeo, Kheng Cher Khan, Shujahat Ali Pritika Sellappan, Devaraju Energy and Resources Institute Charles Darwin University Faculty of Science and Technology Darwin Australia Christ Academy Institute for Advanced Studies Department of Computer Science and Applications Bengluru India Charles Darwin University Faculty of Science and Technology Darwin Australia School of Computing Science and Engineering VIT Bhopal University Bhopal India

ISBN: (纸本)9798350383652

In the current era, digital forensic investigators need specialized tools to extract digital footprints from hard disks. The number of crimes is rising dramatically. This has resulted in an increased number of unresolved cybercrime cases including malicious software, hacking and cyberfraud. Disk forensics or disk investigation is a massive task. It takes weeks to collect the traces of the 1TB hard drive and analyze them. It is a challenging task to detect reliable evidence because of the worldwide use and advancement of digital communication technologies. By using automated tools, only predictable areas of the disk are often investigated, and as a result, latent evidence in the hidden area might be missed. In this paper, we present Chaithu's Framework (CF) that gives us a clear picture of the steps to follow and implement a machine learning algorithm which is able to predict whether the request is related to malware or not by analyzing the large world Microsoft Malware Prediction dataset collected from Kaggle. It consists of a total of 7.8 million data samples with 84 features collected in real time from Windows 7,8,9 and 10 systems. Using predictive machine learning algorithms, frauds can be detected automatically and autonomous actions can be taken to prevent them. © 2024 IEEE.

关键词： Hard disk storage

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Data Augmentation Approach for Language Identification in Imbalanced Bilingual Code-Mixed Social Media Datasets 5

Data Augmentation Approach for Language Identification in Im...

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5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Md Suhaimin, Mohd Suhairi Ahmad Hijazi, Mohd Hanafi Moung, Ervin Gubin Mohamad Hamza, Mohd Azwan Universiti Malaysia Sabah Data Technology and Applications Research Group Faculty of Computing and Informatics Sabah Malaysia Ministry of Higher Education Malaysia Polytechnic and Community College Education Department Putrajaya Malaysia Creative Advanced Machine Intelligence Research Centre Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Malaysia Research Group Universiti Malaysia Pahang Pahang Malaysia

ISBN: (纸本)9798350304152

Addressing the problem of language identification in code-mixed datasets poses notable challenges due to data scarcity and high confusability in bilingual contexts. These challenges are further amplified by the associated imbalance and noise characteristic of social media data, complicating efforts to optimize performance. This paper introduces an augmentation approach designed to enhance language identification in bilingual code-mixed social media data. By incorporating reverse translation, semantic similarity, and sampling techniques alongside customized reprocessing strategies, our approach offers a comprehensive solution to these complex issues. To evaluate the effectiveness of the proposed approach, experiments were conducted on language identification at both the sentence and word levels. The results demonstrated the potential of the approach in optimizing language identification performance, offering a compelling combination of generation techniques for addressing the challenges of language identification in code-mixed data. © 2023 IEEE.

关键词： Semantics

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Copy-Move Forgery Detection Optimization Using Harmony Search with Clonal Selection Algorithm

Copy-Move Forgery Detection Optimization Using Harmony Searc...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Mohd Hanafi Ahmad Hijazi Ezlyn Sherlyn Josmin Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Center Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In today's world, where technology is advancing rapidly, the extensive use of digital images on social media brings up concerns about their authenticity. One common form of deception is image forgery, especially copy-move forgery, where parts of an image are duplicated and inserted elsewhere within the same image. Traditional detection methods, like randomized search algorithms, often struggle with adaptability. This study presents a new approach that uses the Harmony Search with Clonal Selection Algorithm (HSCSA) to improve the detection of copy-move forgeries by fine -tuning the hyperparameters of Convolutional Neural Network (CNN) models. Our method involves preprocessing the CoMoFoD dataset, optimizing essential hyperparameters using HSCSA, and comparing the performance of the optimized model with standard CNN models. The results showed a notable improvement, with accuracy rising from 67.72% to 78.20%.

关键词： Accuracy Social networking (online) Digital images Forgery Robustness Convolutional neural networks Particle swarm optimization Optimization Standards Convergence

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Simulate the Machine Learning Algorithm to Organize the CRAHN Network System 3

Simulate the Machine Learning Algorithm to Organize the CRAH...

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3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Prabakaran, P. Chandra Sekhar Reddy, L. Ravikumar, L.V.D. Verma, Manish Kumar School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Hyderabad Medchal India Shri Vishnu Engineering College for Women Department of Information Technology Bhimavaram India Lovely Professional University Department of Psychology Phagwara India

ISBN: (纸本)9798350330724

Using available channels in a wireless spectrum, cognitive radio (CR) may automatically adjust transmission settings to optimize radio operational behaviour. To function properly, a CR ad hoc network (CRAHN) has to be dynamically capable construct autonomous and decentralized networks without negatively impacting licensed main user (PU) systems. For this reason, an effective spectrum necessitates a system structure based on artificial intelligence. This research provides a model for network planning, learning, and dynamic configuration that is based on a distributed autonomous CRAHN network system that uses reinforcement learning. The proposed optimization techniques for spectrum sensing, ad hoc network design, and context-aware signal categorization are all derived from the system model and are based on machine learning. The cognitive and detection engines may be used to examine the spectrum utilization and neighbour network status in the immediate area. To adapt to the ever-changing nature of the wireless environment, the suggested policy engine may generate network operating policies, identify policy conflicts, and infer the best course of action. Together with the erudition engine, whereby apply the recommended machine-learning methods, the decision engine arrives at the best possible settings for the CRAHN. In addition, guarantee peaceful cohabitation with surrounding systems to have excellent signal context recognition ability. © 2023 IEEE.

关键词： Ad hoc networks

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Hybrid Signal Integrity Modeling and Analysis of Heterogeneous Integrated System With Neuromorphic Darwin Chip

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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The Performance Analysis and Comparison of Azure and AWS Hypervisor using Different Workloads in Virtualization

The Performance Analysis and Comparison of Azure and AWS Hyp...

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International Conference on ent computing and Control Systems (ICICCS)

作者： J Mary Ramya Poovizhi R Devi Department of Computer Science Schools of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Department of Computer Applications Schools of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai

The abstraction of IT infrastructure enables the integration and pooling of IT resources to be shared across several applications to compensate for declining resources. Growing business needs virtualization offers a conceptual encapsulation of current computing resources. It generates computer systems unrestricted by physical design. The implementation objective of virtualization in cloud computing is to simplify the provision of products by providing a platform for maximizing complex IT resources in a scalable way, hence decreasing the cost of cloud computing. Hypervisors are one of the essential components of the virtualization of Hardware. Many operating systems can run simultaneously on a single physical server in a virtualized environment. For cloud computing, the hypervisor is the best way to deliver a variety of operational environments. This work will compare the cloud centre hypervisor Azure and AWS with different environments and workloads. The result shows not all hypervisors will provide the same energy and power conception with workloads and environments.

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Multi-ensemble machine learning framework for omics data integration: A case study using breast cancer samples

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Informatics in Medicine Unlocked 2024年 47卷

作者： Tembhare, Kunal Sharma, Tina Kasibhatla, Sunitha M. Achalere, Archana Joshi, Rajendra HPC-Medical and Bioinformatics Applications Group Centre for Development of Advanced Computing Innovation Park Panchavati Pashan Maharashtra Pune 411008 India

Integration of voluminous omics data aids to unravel biological complexities associated with different disease phenotypes. Machine learning (ML) approaches provide insightful techniques for systematic multi-omics data integration. In this study, survival prediction of breast cancer patients was undertaken using omics data of 302 female patients from The Cancer Genome Atlas (TCGA). The data included gene expression, miRNA expression, DNA methylation and copy number variation. Three computational multi-ensemble ML pipelines were tested using Support Vector Machine (SVM), Random Forest (RF) and Partial Least Squares-Discriminant Analysis (PLS-DA) algorithms. To overcome the limitations associated with univariate feature selection criteria, the ML pipelines were built along with latent factors obtained by multivariate dimension reduction method. This facilitated investigation of background genetic networks and identification of potential hub genes. Analysis of the results obtained revealed that SVM with PLS-DA method (integrated with gene expression, DNA methylation, and miRNA expression modalities) was the best-performing model with an Area Under Curve (AUC) of 89% and an accuracy of 83% for survival prediction. This study not only corroborated previously reported breast cancer-specific prognostic biomarkers but also predicted additional potential biomarkers. The work demonstrates the effective use of a multi-ensemble ML model with efficient feature selection methods as a robust protocol for cancer genotype to phenotype correlation. © 2024

关键词： Biomarker prediction Breast cancer survival Multi-omics integration Supervised machine learning

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Enhancing Chest X-ray Pathology Prediction through Data-Driven Approaches and Transfer Learning

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Procedia Computer Science 2025年 258卷 3447-3456页

作者： Rekha R. Nair Kishore S Tina Babu Vishnu Vilashini S Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte (Deemed to be University) Nitte Institute of Professional Education Mangalore India Analyst Capgemini Bellandur Karnataka India

Chest X-ray pathology prediction play a very important role in early disease detection, enabling timely intervention and improving patient outcomes. This project undertakes a comprehensive exploration of medical imaging analysis, focusing on chest X-rays for pathological identification. Starting this inquiry is thorough getting of information and exploratory information assessment (EDA), which demonstrates the significant elements of the dataset’s people, disease characteristics, and intrinsic predicaments. By making use of sophisticated image processing techniques, the study uses a Super-Resolution Convolutional Neural Network (SRCNN) in refining the resolution of chest X-ray scans, leading to improvement in diagnostic accuracy. Dealing with natural data imbalance found in medical data sets, this project proposed a new method by including a weighted loss function during training. Thus designed, this system allows the model to practically learn from uneven class distributions ensuring strong efficiency throughout different pathologies. The neural architecture for chest X-ray classification is built upon transfer learning principles, utilizing pretrained ResNet18, Alex net, Custom Neural Network models. The models are designed to meet the characteristics of the medical dataset in particular way, having replaced last fully connected layer with one that gives outputs across 14 different pathologies for predictions. The training process involves fine-tuning the models with a meticulously crafted weighted loss function and adaptive learning rate schedules, ensuring optimal convergence.

关键词： MQ2 Sensor NodeMCU Data Logging Analytics Real-time Alerts

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Towards Sustainable Waste Management: Exploring Machine Learning and Deep Learning Solutions for Biodegradable and Non-Biodegradable Waste Identification

Towards Sustainable Waste Management: Exploring Machine Lear...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： S. Sasi Rekha P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

This study examines 20 research articles focused on AI-based waste management techniques, including machine learning, deep learning, and IoT sensors. Researchers aim to develop sustainable solutions for waste sorting and management. The articles highlight the significant role of deep learning and computer vision in waste classification, with a growing interest in recent years, particularly in 2022. These advanced technologies offer promising avenues for enhancing waste sorting accuracy, reducing manual labor reliance, and promoting recycling efforts. Moreover, the incorporation of IoT sensors plays a vital role in continuously monitoring waste levels and environmental parameters, thereby optimizing the entire waste collection and management workflow. These outcomes underscore the escalating adoption of sophisticated AI methodologies in waste management, suggesting a paradigm shift towards enhanced and environmentally conscious waste management practices. This research provides valuable insights for policymakers, waste management practitioners, and researchers seeking to address the challenges of waste management while promoting environmental sustainability.

关键词： Waste management Deep learning Pervasive computing Social networking (online) Green products Manuals Sensors

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Innovative Solutions for Sustainable IoT in Smart City Infrastructure

Innovative Solutions for Sustainable IoT in Smart City Infra...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Arul P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Smart cities are being created all over the world to enhance the safety and quality of life for their residents via the use of technology. Video surveillance, which includes placing cameras at key locations across the city to keep an eye on public areas and give law enforcement and other local authorities real-time access to recordings, is a crucial part of the architecture of smart cities. Cuttingedge technologies like deep learning, blockchain, edge computing, and cloud computing are used in modern video surveillance systems. This paper offers a comprehensive review of video surveillance systems for smart cities, examining both their benefits and drawbacks. In order to improve inhabitants’ safety, security, and overall well-being, this article aims to show the benefits of video surveillance systems in smart cities and offers suggestions for their application.

关键词： Smart cities Reviews Video surveillance Cameras Robustness Real-time systems Safety Blockchains Recording Research and development

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