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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Chisty, Tanjir Alam Rahman Rahman, Md. Mahbubur University of Professionals Department of Information and Communication Technology Bangladesh Dhaka Bangladesh Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

Ransomware, a form of malicious software, encrypts victim data and renders it inaccessible until a ransom is paid. Different ransomware variants employ various tactics to avoid detection during their attacks. Understanding the techniques used in ransomware development and deployment is crucial to counter these threats. This study presents a stacked ensemble-based machine learning (ML) approach for ransomware detection. The ensemble model consists of Naive Bayes (NB), Random Forest (RF), and K-Nearest Neibors (KNN) as base classifiers and Artificial Neural Network (ANN) as meta-classifiers. Additionally, Explainable AI (XAI) utilizing LIME and SHAP has been applied to gain insights into prediction outcomes and validate model correctness. The use of LIME and SHAP ensures the inter-pretability of the model, which is a crucial factor in real-world implications. The research employs a publicly available dataset and evaluates the model using multiple metrics. Experimental results reveal that the proposed model achieves an impressive accuracy of 98.57%. The model is benchmarked against existing ML techniques, demonstrating its superior performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Teleoperation in the Age of Mixed Reality: VR, AR, and ROS Integration for Human-Robot Direct Interaction 4

Teleoperation in the Age of Mixed Reality: VR, AR, and ROS I...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Nandhini, P. Chellammal, P. Jaslin, J.S. Harthy Ruby Priya, S. Uma, M. Kaviyaraj, R. Sri Shanmugha College of Engineering and Technology Department of Computer Science and Engineering Sankari India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350300093

Robotic systems that can be operated remotely are becoming more and more common in a variety of industries, including construction, disaster relief, space exploration, and industrial applications. Teleoperation, however, necessitates skilled operators who can handle sophisticated data processing and programming. This research introduces a unique teleoperation method that makes use of Virtual Reality (VR) and the Robot Operating System (ROS) to overcome this problem. By creating a 3D representation of the robot's workspace in Unity and connecting it to the actual robot with ROS, this technique optimizes control. Programming is not necessary because the robot's vision serves as the foundation for the user interface. Through ROS integration, a target point is established in Unity and sent to the robot as the end effector position. By simply setting up a new environment, reducing the requirement for knowledgeable operators, and improving situational awareness, this method may be used to any sort of robot. For even greater situational awareness, this technique may also be used with augmented reality (AR). AR can give the operator real-time information about the robot's surroundings by superimposing digital information over the physical world. This can be achieved by including AR markers in the physical surroundings that the VR system can detect. In the operator's field of view, the system can then superimpose information such as sensor data or robot status onto the real-world environment. © 2023 IEEE.

关键词： Augmented reality

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Cyber-physical energy system simulation tool: framework for network co-simulation, GridLAB-D and ns-3

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International Journal of Simulation and Process Modelling 2022年第4期18卷 314-328页

作者： Singh, Brijendra Pratap Gore, Manoj Madhava School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad India

A cyber-physical energy system consists of information and communication technology, advanced power electronic devices, energy sources, and smart appliances. A simulator is required for research and to further enhancements in the cyber-physical energy system. The integration of a power system and information and communication technology raises the problem of integration of time-driven activity and event-driven activity. The power system simulators are time-driven, while the network communication simulators are event-driven. The Pacific Northwest National Laboratory, Richland, Washington developed a tool named ‘framework for network co-simulation (FNCS)’ which co-simulates ns-3 (a network simulator) and GridLAB-D (a power distribution system simulator). This work details the enhancement of the FNCS application program (which is integrated into ns-3) to incorporate the functionalities, namely: 1) internal real-time price calculation;2) day-ahead charging schedule of a battery. The enhancement has been used for the development of cyber-physical application programs, namely, internal real-time pricing and battery charging schedules. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Application programs

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Dynamic generative R-CNN

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Neural Computing and Applications 2025年第10期37卷 7107-7120页

作者： Saffarini, Rasha Khamayseh, Faisal Awwad, Yousef Sabha, Muath Eleyan, Derar IT Engineering Arab American University Jenin Palestine Department of Computer Science Palestine Polytechnic University Hebron Palestine Department of Computer System Engineering Palestine Technical University Tulkarm Palestine Department of Multimedia Technology Arab American University Jenin Palestine Department of Applied Computing Palestine Technical University Tulkarm Palestine

Different techniques have been developed for object detection and recognition. These techniques can be divided into single-shot and two-shot methods. Single-shot methods focus on real-time applications, while two-shot methods are used in applications requiring higher accuracy. However, different versions of the two-shot techniques produce limited results in terms of accuracy and speed, or both. Therefore, this study proposes a novel model called dynamic generative R-CNN (DGR-CNN) that reduces the number of proposed regions using a dynamic programming model that applies the graph similarity method over graph-based image segmentation. Additionally, the proposed model employs DCGAN technique to improve detection performance. DGR-CNN reduces the overall detection and classification time and enhances the detection accuracy. The PASCAL VOC2007 and MS COCO datasets were utilized to evaluate the model. The results showed that DGR-CNN significantly reduces the number of candidate regions compared to the selective search algorithm employed in R-CNN and fast R-CNN. Although fast R-CNN utilizes 2000 regions and faster R-CNN utilizes 300 regions, DGR-CNN reduces the number of regions to approximately 130. The mean average precision of the proposed method was 75.1% on the PASCAL VOC2007, while fast and faster R-CNN scored 66.9% and 69.9%, respectively. Moreover, the DGR-CNN model significantly improved the classification accuracy when tested on the MS COCO dataset, achieving an MAP of 68.76%, compared with 32.64% and 42.3% for fast and faster R-CNN. This increase in accuracy was achieved without significantly compromising the speed compared with faster R-CNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Enhanced ML Based Content-Based Image Retrieval for Mobile Devices

Enhanced ML Based Content-Based Image Retrieval for Mobile D...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Meenakshi, K.M. Singh, Kirti Dilip Batta, Priya Chauhan, Rakhi Bhargava, Deepshikha Lohani, Bhanu Prakash Amity School of Engineering & Technology Amity University Greater Noida India Chandigarh University Department of Computer Science & Engineering Mohali India Geeta University Department of Computer Science & Engineering Haryana Panipat India

ISBN: (纸本)9798350379716

Image retrieval systems based on content can be developed for mobile applications that provide users with a seamless and efficient way to search for images based on their content. The development of CBIR systems for mobile applications presents unique challenges and opportunities. Mobile devices typically have limited computational resources, storage capacity, and network bandwidth compared to traditional computing platforms. Therefore, CBIR systems designed for mobile applications must be lightweight, efficient, and optimized to run smoothly on these devices. The Adaboost algorithm progressively learns weak classifiers and aggregates them to construct a robust final classifier. Subsequently, a weak learner is introduced, and the data undergoes reweighting. Misclassified test images are assigned greater importance while correctly classified ones are downweighted. Consequently, subsequent weak learners prioritize the query images previously misclassified by their predecessors. The average precision is increased in further iterations to 95.6% from the initial iteration of 94.9%. Average recall shows a little more improvement from the previous set by increasing from round 1 with 94.1%, and increasing to 95.9% towards the last round of iteration. The F-Measure keeps fairly stabilized at 95.1478 after the first round. The time taken by every iteration reduces slightly from 3. © 2024 IEEE.

关键词： Content based retrieval

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Automated Breast Cancer Diagnosis Using Deep Learning CNN Models 2

Automated Breast Cancer Diagnosis Using Deep Learning CNN Mo...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Saravanan, G. Jeevanantham Prabin, S.M. Parthasarathy, R. VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Chennai India

ISBN: (纸本)9798331534950

The study introduces a deep learning model, CCNN, developed to identify and classify eight types of breast cancer: normal adenosis, normal fibroadenoma, normal phyllodes tumor, normal tubular adenoma, malignant ductal carcinoma, malignant lobular carcinoma, malignant mucinous carcinoma, and malignant papillary carcinoma. This model was integrated with five pre-existing models - Xception, InceptionV3, VGG19, MobileNetV2, and ResNet101 - all trained for image classification using the ImageNet database, to categorize breast cancer MRI images. The dataset, sourced from Kaggle's repository, was further augmented using the Generative Adversarial Network (GAN) technique. For 30 experiments carried out to evaluate the introduced model and the transfer learning of five and proposed CCNN models, the F1-score values accuracy yielded were respectively: 98.04%, 96.13%, 98.54%, 97.11%, 94.20% and 98.98%. This study emphasizes the importance of improving the accuracy of early breast cancer detection to enhance the survival rates of patients. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Security in the Internet of Things: A Trust-Based Protocol for Resilient Communication

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SN computer science 2024年第1期5卷 4页

作者： Althaf Ali, A. Hussain, Mohammed Muzaffar Subramaneswara Rao, A. Lavanya, S. Feroz Khan, A.B. Department of Computer Applications Madanapalle Institute of Technology & Science (MITS) Andhra Pradesh Madanapalle India Department of Computer Science and Engineering C. Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India Department of Software Applications Thiruthangal Nadar College Tamil Nadu Chennai India Department of Computer Science Syed Hameedha Arts and Science College Tamil Nadu Kilakarai India

The quick increasing in the Internet of Things (IoT) devices has raised significant security concerns, particularly in the face of reactive jamming attacks. This paper proposes a trust-based protocol named Trust-Based Protocol for Resilient Communication (TBRC) that incorporates a robust key management technique to improve the security of IoT systems and mitigate the impact of reactive jamming attacks. To assess the effectiveness of our protocol, the work is compared with existing schemes such as CKMT, EDDK, and CEMT. Through extensive simulations under realistic conditions, we analyze key performance metrics, including energy consumption, packet loss, computational time, and throughput. The comparative analysis demonstrates that our proposed protocol outperforms CKMT, EDDK, and CEMT in terms of computational efficiency, throughput, and delay, making it a more effective solution for secure IoT communication. By leveraging a robust key management technique, our protocol provides a secure and resilient communication framework for IoT environments, effectively countering reactive jamming attacks. The integration of this technique strengthens the overall security posture of IoT networks, ensuring reliable and secure communication between devices. This work contributes to advancing IoT security by introducing a trust-based protocol with a robust key management technique that effectively mitigates the impact of reactive jamming attacks. The comparative evaluation against existing schemes validates the superiority of our proposed approach in terms of security, efficiency, and reliability. The results of our evaluation highlight the improved performance of our protocol, positioning it as a promising solution for real-world IoT deployments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DDoS IoT Security Trusted metrics

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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Wave Deep Neural Network for Multi-Class MRI Brain Image Classification 2

Wave Deep Neural Network for Multi-Class MRI Brain Image Cla...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Ramasamy, Viswanathan Vinodhini, V. Saravanan, T.R. Kumar, S. Dinesh Lavanya, S. Kl University Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Sona College of Technology Department of Computer Science and Engineering Tamil Nadu Salem India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Muthayammal Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798350347579

The human brain is considered to be the most important organ in the body. Since the causes of brain cancer is still unknown, early detection is required for proper treatment. Magnetic Resonance Imaging (MRI) is an imaging technology used to depict inside structure of human body in details. This research study makes a contribution to the development of an image-based classification system as well as to the detection of brain cancer. Texture-based energy descriptors are retrieved using Discrete Wavelet Transform (DWT)-based sparse representation systems. A Convolutional Neural Network (CNN) in wavelet domain is used for further processing in order to improve the classification between normal and abnormal classes of MRI brain images. The performance of the proposed system is assessed by utilizing the standard collection of brain tumor images available in the REMBRANDT database. Results prove that the proposed system provides 98% accurate results for brain cancer classification. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Comparative Methods of Implementation for Different Question Answering Systems 5

Comparative Methods of Implementation for Different Question...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Naveen, C. Amutha, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798350385649

This research introduces an innovative Question Answering (QA) system tailored explicitly for government department inquiries regarding individuals. Harnessing the prowess of cutting-edge language models such as BERT and T5 (Text-to-Text Transfer Transformer), the system excels in understanding complex queries within diverse governmental domains. Moreover, it incorporates a specialized Knowledge Graph meticulously curated with interconnected information about people across various departments. By integrating BERT and T5 for versatile query comprehension and answer generation alongside a comprehensive People-centric Knowledge Graph, this system aims to revolutionize information retrieval within government entities. The seamless fusion of these technologies promises accurate, contextually rich responses, optimizing operational efficiency across government departments and fostering streamlined access to crucial information. © 2024 IEEE.

关键词： Knowledge graph

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