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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics Pune India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the evolving landscape of 5G social networks, the surge in connectivity and data exchange has amplified the susceptibility to malicious attackers, undermining the trust and security integral to these networks. This paper introduces an innovative approach to bolster trust-based attacker detection mechanisms through the integration of advanced artificial intelligence (AI) control techniques. Leveraging the prowess of machine learning (ML) algorithms and AI-driven analytics, our proposed framework enhances the accuracy and efficiency of identifying potential threats within 5G social networks. By implementing a hybrid model that combines anomaly detection algorithms with trust scoring systems, we aim to establish a robust security posture that dynamically adapts to emerging threats. The core of our work lies in the development of a predictive model that utilizes deep learning techniques to analyze network behaviors and user interactions, facilitating the early detection of anomalous activities indicative of malicious intent. Through extensive simulation and real-world application, our results demonstrate a significant improvement in detection rates and a reduction in false positives, thereby fortifying the trust and reliability of 5 G social networks. This study not only contributes to the advancement of secure network architectures but also sets a new precedent for the application of AI in cybersecurity, offering a scalable and efficient solution to safeguard against sophisticated attackers. © 2024 IEEE.

关键词： 5G mobile communication systems

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COVID-19 Fake News Detection in Malaysia – A Supervised Approach 23

COVID-19 Fake News Detection in Malaysia – A Supervised App...

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12th International Conference on Software and computer Applications, ICSCA 2023

作者： Ramakrishnan, Kalaimagal Balakrishnan, Vimala Chong, Soo Mun Faculty of Science and Engineering University of Nottingham Malaysia Faculty of Computer Science and Information Technology University Malaya Malaysia

ISBN: (纸本)9781450398589

Social media has been flooded with enormous amounts of COVID-19-related information ever since the COVID- 19 pandemic started back in 2020. Since then, Malaysian citizens have become more reliant than ever on social media for consumption of COVID-19 information. However, the lack of COVID-19 news regulations on social media platforms encouraged people to post unverified, fake and misleading COVID-19 related information. Because of the time-consuming nature of fact-checking, people often take these unverified COVID-19 news for granted. Consequently, people inadvertently spread these fake COVID-19 news to their families, friends and relatives on social messaging platforms like WhatsApp. The spread of COVID- 19 fake news online in Malaysia can have severe sequences, causing widespread panic among fellow Malaysians. In this paper, we proposed a supervised learning approach to detect COVID-19 fake news. The fake news on COVID-19 were scraped from the website called Sebenarnya, and real news were scraped from The Star website. We applied a semantic model with different word representations which include Bag of Words (BOW), Term Frequency - Inverse Document Frequency (TF-IDF), Word2Vec and Global Vectors (GloVe). In the evaluation step, 6 supervised machine learning algorithms were applied such as Multinomial Naive Bayes, Support Vector Machines, Decision Tree, Random Forest, Logistic Regression and Adaboost. Afterward, 10-fold cross validation was used to train and evaluate the 6 supervised algorithms according to performance metrics such as accuracy, precision, recall, AUC-ROC, F1-score. The results showed that Random Forest with the word representation of TF-IDF per- formed the best with over 97% accuracy in contrast to numerous conventional supervised machine learning classifiers. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： COVID-19

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IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis 10

IoT Sensor Deployment in the Wildland Urban Interface: Lever...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Purcell, Richard Naik, Kshirasagar Zaman, Marzia Lung, Chung-Horng Sampalli, Srinivas Mutakabbir, Abdul Dhindsa, Manavjit Singh Das, Ursula Dalhousie University Faculty of Computer Science HalifaxNS Canada University of Waterloo Electrical and Computer Engineering WaterlooON Canada Research and Development Cistel Technology OttawaON Canada Carleton University Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350373011

This paper investigates algorithms for distributing Internet of Things sensors within the Wildland-Urban Interface to enhance early wildland fire detection. Using geospatial data analysis and a validated wildland fire growth model, we generated burn maps to guide sensor placement strategies across a defined region. We evaluated even grid distribution, random distributions, and genetic algorithm-based methods, testing each against 50,000 burn maps with sensor counts ranging from 50 to 800. Results indicate that while even grid distribution achieved the highest detection rates, its practicality in real-world applications is limited. Genetic algorithms showed promise but require further exploration to simulate field deployment accurately. Notably, weighting sensor placement based on wildland fire growth risk did not significantly impact detection effectiveness, highlighting the need for further research into the representativeness of burn maps. © 2024 IEEE.

关键词： Genetic algorithms

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Enhancing Quality of Service in Mobile Ad-Hoc Networks through Secure Routing with Multi-Constrained Network Feature Approximation

Enhancing Quality of Service in Mobile Ad-Hoc Networks throu...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

ISBN: (纸本)9798350359688

In the realm of Mobile Ad-Hoc Networks (MANETs), achieving optimal Quality of Service (QoS) amidst the challenges of security threats and dynamic network topology remains a paramount concern. This paper introduces a groundbreaking secure routing strategy empowered by a multi-constrained network feature approximation technique aimed at bolstering QoS in MANETs. The core innovation lies in the fusion of a multi-dimensional optimization framework with an advanced feature approximation algorithm, tailored to navigate the intricacies of MANETs' decentralized architecture and fluctuating conditions. By focusing on critical QoS parameters such as throughput, delay, packet delivery ratio, and jitter, alongside ensuring robust security measures, our proposed method significantly enhances the reliability and performance of data transmission within MANETs. A comprehensive simulation environment is utilized to evaluate the effectiveness of our approach, revealing substantial improvements in QoS metrics compared to conventional secure routing protocols. Through a meticulous examination of these results, the study underscores the proposed method's efficiency in managing the dual objectives of security and QoS optimization. This research not only marks a significant advancement in secure routing protocols for MANETs but also sets the groundwork for future explorations into integrating multi-constrained optimization techniques for network feature approximation in ad-hoc networks. © 2024 IEEE.

关键词： Constrained optimization

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Towards At-the-Edge ECG Signal Processing with Accuracy-tunable Approximate Adders 12

Towards At-the-Edge ECG Signal Processing with Accuracy-tuna...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Hama, Hiroyuki Sato, Toshinori Fukuoka University Graduate School of Engineering Department of Electronics Engineering and Computer Science Fukuoka Japan Fukuoka University Faculty of Engineering Department of Electronics Engineering and Computer Science Fukuoka Japan

ISBN: (纸本)9798350340181

Edge devices used in healthcare require high energy efficiency due to battery operation and replacement challenges. Approximate computing is effective in reducing energy consumption for applications tolerant to errors. Specifically, we investigate the application of approximate addition in the Pan-Tompkins algorithm for ECG processing. Our initial findings suggest that certain processes within the algorithm, such as high-pass filtering and derivative, are well-suited for approximation. We also observed that low-pass filtering is not as amenable to approximation. One noteworthy observation was that the handling of negative numbers through approximate addition did not introduce any adverse effects, reinforcing the viability of using this approach in the context of ECG processing for edge devices in healthcare applications. Our preliminary experiments demonstrate that a 16.5% reduction in power during additions is achieved. © 2023 IEEE.

关键词： Electrocardiograms

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Multi-Modal Facial Expression Recognition through a Hierarchical Cross-Attention Graph Convolutional Network

Multi-Modal Facial Expression Recognition through a Hierarch...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the rapidly evolving field of computer vision and machine learning, recognizing human facial expressions with high accuracy remains a pivotal challenge. This paper introduces a novel Multi-Modal Facial Expression Recognition (MMFER) framework, leveraging a Hierarchical Cross-Attention Graph Convolutional Network (HCA-GCN) to enhance the accuracy and robustness of facial expression recognition. Our proposed HCA-GCN model systematically integrates multi-modal data sources, including visual imagery and depth information, to capture the complex and subtle nuances of human facial expressions. The hierarchical structure of our model allows for the efficient processing of multi-modal data through distinct pathways, ensuring that relevant features from each modality are effectively extracted and combined. The cross-attention mechanism within our framework meticulously aligns and emphasizes inter-modal features, fostering a synergistic integration that boosts the model's ability to discern intricate facial expressions. Through extensive experiments on benchmark datasets, our approach demonstrates significant improvements over existing methods, showcasing superior performance in recognizing a wide range of facial expressions under various conditions. The implementation details, including algorithms and mathematical expressions, are meticulously described to facilitate replication and further research. Results and discussions highlight the model's efficacy, underscored by comparative analysis and visualizations. Our findings indicate that the HCA-GCN model not only advances the state-of-the-art in facial expression recognition but also opens new avenues for research in multi-modal learning. © 2024 IEEE.

关键词： Spatio-temporal data

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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence

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A New Chaotic-Based Analysis of Data Encryption and Decryption 18th

A New Chaotic-Based Analysis of Data Encryption and Decrypti...

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18th International Conference on Data science and Artificial Intelligence, ICDSAI 2022

作者： Johora, Fatema Tuj Alamin-Ul-Islam Yesmin, Farzana Rahman, Md. Mosfikur Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Faculty of Science and Information Technology Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031161773

Because the amount of exchange hypersensitive data via the Internet is growing at an exponential rate, network and data security have recently been the most pressing worry. On the subject of data security, many approaches are available, including "cryptography." When data is sent from the sender to the receiver, it is encrypted using an encryption method, and when it is received by the receiver, it is decrypted using a decryption algorithm to see the exact and true data that was sent by the sender previously. Data encryption can be done in a number of ways. With algorithms like AES, DES, and RSA for data encryption and decryption, this research proposes a novel CRSA (chaotic random seed algorithm) technique. CRSA was also compared to other algorithms to see how well they performed. The experimental data presented in this work are used to examine those algorithms as well as our new CRSA algorithm. Cryptography, encryption, decryption, random reed, millisecond, and data security are all terms used in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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Chopin: Combining Distributed and Centralized Schedulers for Self-Adjusting Datacenter Networks 26

Chopin: Combining Distributed and Centralized Schedulers for...

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26th International Conference on Principles of Distributed Systems, OPODIS 2022

作者： Rozen-Schiff, Neta Foerster, Klaus-Tycho Schmid, Stefan Hay, David School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Computer Science Department TU Dortmund Germany TU Berlin Germany Faculty of Computer Science Universität Wien Austria

ISBN: (纸本)9783959772655

The performance of distributed and data-centric applications often critically depends on the interconnecting network. Emerging reconfigurable datacenter networks (RDCNs) are a particularly innovative approach to improve datacenter throughput. Relying on a dynamic optical topology which can be adjusted towards the workload in a demand-aware manner, RDCNs allow to exploit temporal and spatial locality in the communication pattern, and to provide topological shortcuts for frequently communicating racks. The key challenge, however, concerns how to realize demand-awareness in RDCNs in a scalable fashion. This paper presents and evaluates Chopin, a hybrid scheduler for self-adjusting networks that provides demand-awareness at low overhead, by combining centralized and distributed approaches. Chopin allocates optical circuits to elephant flows, through its slower centralized scheduler, utilizing global information. Chopin’s distributed scheduler is orders of magnitude faster and can swiftly react to changes in the traffic and adjust the optical circuits accordingly, by using only local information and running at each rack separately. © Neta Rozen-Schiff, Klaus-Tycho Foerster, Stefan Schmid, and David Hay.

关键词： Topology

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A DL-DeNoiseNet Framework for Robust Image Denoising and Artifact Reduction in Medical Image Diagnostics 2

A DL-DeNoiseNet Framework for Robust Image Denoising and Art...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Nanthini, S. Maindola, Meenakshi Madhu, R. Ravishankar, M.D. Gandhi, Jay Patil, Harshal Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering The National Institute of Engineering Mysore India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India School of Computer Science and Engineering IILM University Greater Noida India

ISBN: (纸本)9798331504960

For precise illness diagnosis and therapy planning, medical imaging diagnostics are necessary. Nevertheless, diagnostic errors can occur due to noise and aberrations that are inherent to imaging modalities including CT, MRI, and ultrasound, which can hide important features. In order to overcome this obstacle, we present a medical diagnostics-specific framework for deep learning-based picture denoising and artifact reduction. Our method improves feature extraction while maintaining picture integrity by combining a Convolutional Neural Network (CNN) with a Residual Dense Block (RDB). To guarantee optimal performance across varying degrees of noise and image types, the framework is trained on a broad dataset of medical photos, which includes images that are artifact-laden and noisy. A combination of supervised and adversarial learning techniques is used for this purpose. Adaptive filtering for preprocessing, the CNN-RDB network for denoising, and a refinement module that uses perceptual loss to keep things clinically relevant for postprocessing are the three primary steps in the methodology. The model outperforms traditional denoising methods when tested on various medical imaging metrics, including a PSNR of 40.3 dB and an SSIM of 0.97. Furthermore, the suggested architecture successfully mitigates artifacts by an average of 35% as assessed by the Normalized Root Mean Squared Error (NRMSE), guaranteeing that essential diagnostic characteristics are preserved. The results show that the model can improve image clarity, which means that diagnoses can be made more accurately and fewer scans are needed. Better healthcare outcomes are a direct result of the substantial enhancements to picture quality that this framework provides to physicians and radiologists. © 2024 IEEE.

关键词： Adaptive filtering

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