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2024 International Conference on Circuit, systems and communication, ICCSC 2024

作者： Elalaouy, Ouafae Aghoutane, Bilal El Ghzaoui, Mohammed Das, Sudipta Foshi, Jaouad El Faylali, Hanan University of Moulay Ismail Faculty of Sciences and Techniques Errachidia Errachidia Morocco Faculty of Sciences IbnTofail University Informatics Systems and Optimization Laboratory Department of Computer Science Kenitra Morocco Sidi Mohamed Ben Abdellah University Faculty of Sciences Dhar El Mahraz Fes Morocco Imps College of Engineering and Technology Department of Electronics and Communication Engineering W.B Malda India

ISBN: (纸本)9798350365306

In the contemporary era, harnessing millimeter-wave frequencies emerges as a compelling strategy to fulfill the swift data transfer requirements for 5G communications. Consequently, this study involves designing a metamaterial-based multiple-input multiple-output (MIMO) antenna with a focus on achieving high isolation and gain within the millimeter-wave spectrum. The suggested MIMO is printed on a Rogers RT5800 measuring 18 × 38 × 0.8 mm3. The single antenna element demonstrates a broad frequency coverage from 27.2 to 28.72GHz. To elevate the antenna's effectiveness, a two-port MIMO configuration is implemented by introducing a second antenna element parallel to benchmark antenna. However, the isolation between the two elements is deemed satisfactory. To address this, a 4 × 1 metamaterial unit cell is introduced, significantly enhancing isolation from 31 dB to 39 dB at 28 GHz. Furthermore, it improves the gain by 0.5 dBi at 28 GHz, elevating it from 8.5 to 9 dBi. The collective features of the suggested two ports MIMO antenna, encompassing its compactness and simplicity, position it as an excellent option for upcoming portable and shrink devices in Internet of Things uses. © 2024 IEEE.

关键词： Microwave antennas

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Hotel Information system Management Audit Using COBIT 2019 - APO12 2

Hotel Information System Management Audit Using COBIT 2019 -...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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BrainTract: segmentation of white matter fiber tractography and analysis of structural connectivity using hybrid convolutional neural network

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Neuroscience 2025年 580卷 218-230页

作者： Puranam Revanth Kumar B Shilpa Rajesh Kumar Jha Department of Electronics and Communication Engineering Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education Hyderabad India Department of Artificial Intelligence and Machine Learning School of Engineering Malla Reddy University Hyderabad India. Electronic address: revanth123451.rk@***. Department of Computer Science and Engineering AVN Institute of Engineering and Technology Hyderabad India. Department of Electronics and Communication Engineering Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education Hyderabad India.

Tractography uses diffusion Magnetic Resonance Imaging (dMRI) to noninvasively reconstruct brain white matter (WM) tracts, with Convolutional Neural Network (CNNs) like U-Net significantly advancing accuracy in medical image segmentation. This work proposes a metaheuristic optimization algorithm-based CNN architecture. This architecture combines the Inception-ResNet-V2 module and the densely connecting convolutional module (DI) into the Spatial Attention U-Net (SAU-Net) architecture for segmenting WM fiber tracts and analyzing the brain's structural connectivity. The proposed network model (DISAU-Net) consists of the following parts are; First, the Inception-ResNet-V2 block is used to replace the standard convolutional layers and expand the network's width; Second, the Dense-Inception block is used to extract features and deepen the network without the need for any additional parameters; Third, the down-sampling block is used to speed up training by decreasing the size of feature maps, and the up-sampling block is used to increase the maps' resolution. In addition, the parameter existing in the classifiers is randomly selected with the Gray Wolf Optimization (GWO) technique to boost the performance of the CNN architecture. We validated our method by segmenting WM tracts on dMRI scans of 280 subjects from the human connectome project (HCP) database. The proposed method is far more efficient than current methods. It offers unprecedented quantitative evaluation with high tract segmentation consistency, achieving accuracy of 97.10%, dice score of 96.88%, recall 95.74%, f1-score 94.79% for fiber tracts. The results showed that the proposed method is a potential approach for segmenting WM fiber tracts and analyzing the brain's structural connectivity.

关键词： DenseNet Diffusion magnetic resonance imaging Grey wolf optimization Spatial Attention U-Net White matter tract segmentation

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Deep Learning Framework for Breast Cancer Detection and Segmentation Using EfficientNet and U-Net with Hyperparameter Optimization

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SN computer science 2025年第5期6卷 1-24页

作者： Abdel-Wahab, Ahmed Pulipati, Venkateswararao Joshi, Shubham Srikanteswara, Ramya Menon, Sindhu Dutta, Papiya Faculty of Computer Studies Arab Open University (AOU) Riyadh Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad 500043 India Department of AI-DS Vishwakarma Institute of Technology Savitribai Phule Pune University Pune Maharashtra Pune India Department of CSE Nitte Meenakshi Institute of Technology Bangalore India School of Computing and Information Technology REVA University Karnataka Bangalore India Department of Electronics and Communication Engineering Bharat Institute of Engineering and Technology Telangana Hyderabad India

Early detection of breast cancer is critical for improving survival rates, as it remains one of the leading causes of mortality among women worldwide. Timely and accurate diagnosis through medical imaging plays a pivotal role in effective treatment planning. Despite advancements in imaging techniques, challenges remain in the accurate segmentation of tumors from complex mammogram data, which hinders the efficiency of a breast cancer diagnosis. Existing models often struggle with the trade-off between segmentation accuracy and computational efficiency. This study proposes a deep learning-based framework that combines EfficientNet for multi-scale feature extraction and U-Net for pixel-level tumor segmentation to address these challenges. The model's performance is further enhanced by the integration of Particle Swarm Optimization (PSO), which optimizes key hyperparameters such as learning rates and batch sizes. The framework was evaluated using the CBIS-DDSM and MIAS mammogram datasets. The system achieved classification accuracies of 98.7% and 97.9%, with tumor segmentation results of 98.8%- and 98.3%-pixel accuracy. Additional performance metrics such as Intersection over Union (IoU) and Dice coefficient demonstrated superior results, with IoU scores of 91.0% and Dice coefficients exceeding 91%, outperforming standard models like ResNet50 and DenseNet. The proposed deep learning framework demonstrates high accuracy and efficiency in both breast cancer detection and tumor segmentation, offering a scalable solution for medical imaging tasks. Its robust performance suggests that the method could be applied to other medical imaging challenges beyond breast cancer detection. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Breast cancer Deep learning in healthcare Hyperparameter optimization Mammogram analysis Medical image processing Tumor detection Tumor segmentation

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Improved Metaheuristics with Deep Autoencoders for COVID-19 Detection and Classification 2

Improved Metaheuristics with Deep Autoencoders for COVID-19 ...

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2nd International Conference on Electrical, Electronics, Information and communication Technologies, ICEEICT 2023

作者： Assudani, Manish K Nirmala, B. Kanimozhi, N. Chandre, Shanker Sunalini, K.K. Gopatoti, Anandbabu G H Raisoni University Department of Computer Science and Engineering Maharashtra Amravati India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Department of Computer Science and Engineering Chennai India School of Computing SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SR University Department of Computer Science & Artificial Intelligence Telangana Warangal India Koneru Lakshmaiah Education Foundation Department of English Andhrapradesh Guntur India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350397635

A healthcare monitoring system requires the support of current technologies like big data, artificial intelligence (AI), and machine learning (ML), particularly during the outbreak of COVID19. This pandemic caused millions of death worldwide. ML and AI are used to detect and combat COVID19 in the initial phase. Deep learning refers to a subdivision of ML that utilizes NN for making predictions and learning from data. In the context of COVID19 detection, deep learning (DL) methods can be trained for examining medical images like CT scans and chest X-rays and identify the presence of virus. This study presents an improved whale optimization algorithm with deep autoencoder (IWOA-DAE) algorithm for COVID-19 detection and classification. The presented IWOA-DAE technique makes use of DL model with metaheuristic hyperparameter optimizer for COVID-19 classification. To accomplish this, the IWOA-DAE technique employs bilateral filtering (BF) process to eliminate noisy data. Next, the IWOA-DAE technique uses NASNetLarge model for feature vector generation purposes. For COVID-19 detection, the IWOA-DAE technique uses deep autoencoder (DAE) model with IWOA based optimal hyperparameter adjustment process. To highlight the improved performance of the IWOA-DAE algorithm, a wide-ranging simulation analysis takes place. The simulation outcomes highlighted the enhanced performance of the IWOA-DAE technique over other methods. © 2023 IEEE.

关键词： COVID-19

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Pre-trained Self-Attention Framework: An Efficient Mechanism for Source Separation 13th

Pre-trained Self-Attention Framework: An Efficient Mechanism...

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13th International Conference on Information Technology and its Applications, CITA 2024

作者： Minh Tan, Ha Fhadli, Muhammad Kim Quoc, Nguyen Vu, Duc-Quang Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh City Viet Nam Department of Informatics Faculty of Engineering Khairun University Ternate City Indonesia Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam

ISBN: (纸本)9783031741265

In this work, the pre-trained model of the self-attention framework is proposed for single-channel speech separation. Firstly, all layers in the pre-trained self-attention framework are frozen. The model is then retrained through three stages using the scheduling mechanism for learning rates and the layers of the framework are unlocked following the schedule. This way, the model is relearned, enhanced, and updated from previous knowledge. This is an effective way to improve the advanced model performance while significantly reducing the time and cost of a training model. This method is beneficial in applying existing models to perform a similar task or enhancing model performance. In this strategy, the pre-trained system outperforms the non-pre-trained system since the following phases of the model’s training repurpose characteristics extracted through the previously trained early phases. The suggested method is tested and assessed on a conventional dataset. The findings from experiments suggest that the methodology has higher performance than the baseline framework and outperforms current methods for the monaural speech separation task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： fine-tuning framework pre-trained model self-attention mechanism speech separation time masking framework Transformer

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Assessing Virtual Reality Sickness in Highly Immersive Virtual Laboratory Environments : Simulator Sickness Questionnaire and Mitigation Strategies 3

Assessing Virtual Reality Sickness in Highly Immersive Virtu...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Ahmed Alnagrat, Ahmed Jamah Salem Shagluf, Abdulaziz E Mahmoud Sulieman, Rajaa. F. Almintisir Abubaker Akeel, Abubaker Ismail, Rizalafande Che Zulkarnain Syed Idrus, Syed Higher Institute of Science and Technology Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya Faculty of Technical Engineering Mesallata Department of Information Technologyn Msallata Libya University of Benghazi Faculty of Science and Arts Gaminis Department of Computer Science Benghazi Libya Wadi Alshatti University Faculty of Education Department of Mathematics and Computer Wadi Alshatti Fezzan Libya Arau02600 Malaysia Department of Communication Arau02600 Malaysia

ISBN: (纸本)9798350319897

Virtual Reality (VR) sickness, a common challenge among VR users, has increasingly gained attention in academic research due to its adverse side effects. The Simulator Sickness Questionnaire (SSQ) has been traditionally employed to measure simulator motion sickness;however, the extent to which it measures symptoms specifically attributable to VR sickness remains underexplored. This study investigates the applicability of SSQ in assessing VR sickness in a virtual semiconductor laboratory environment, with a focus on reducing motion sickness in VR devices. A total of 97 participants performed target selection tasks using HP Reverb G2 and Oculus Quest 2 headsets in a virtual laboratory setting. The original SSQ was revised to create a VR Sickness Questionnaire, which served as the measurement index for VR-induced symptoms. The primary objective of this research was to determine the prevalence of VR sickness and explore ways to mitigate motion sickness in VR devices. The Results indicate that the majority of VR-related side effects pertain to nausea dysfunction, with a limited number of symptoms linked to cybersickness arising from various factors. These findings hold significant implications for future studies aiming to measure and design interventions for simulator sickness in VR environments. By shedding light on the effectiveness of the SSQ for evaluating VR sickness, this study contributes to the development of improved VR experiences and technologies. © 2023 IEEE.

关键词： Virtual reality

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Intrusion Detection systems in Internet of Things and Mobile Ad-Hoc Networks

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computer systems science & engineering 2022年第3期40卷 1199-1215页

作者： Vasaki Ponnusamy Mamoona Humayun NZJhanjhi Aun Yichiet Maram Fahhad Almufareh Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKamparMalaysia Department of Information Systems College of Computer and Information SciencesJouf UniversityAl-JoufSaudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia

Internet of Things(IoT)devices work mainly in wireless mediums;requiring different Intrusion Detection system(IDS)kind of solutions to leverage 802.11 header information for intrusion ***-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired *** survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods,IDS techniques,IDS placement strategies,and traffic data analysis *** paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific ***,the Knowledge Discovery in Databases(KDD)Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network(WN).The paper starts with a review of various intrusion detection techniques,data collection methods and placement *** main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless *** detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural *** this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless *** main wireless environments to look into would be Wireless Sensor Networks(WSN),Mobile Ad Hoc Networks(MANET)and IoT as this are the future trends and a lot of attacks have been targeted into these *** it is very crucial to design an IDS specifically to target on the wireless networks.

关键词： Internet of Things MANET intrusion detection systems wireless networks

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Examining the Impact of Data Mining on Decision-Making Processes 3

Examining the Impact of Data Mining on Decision-Making Proce...

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

作者： Singh, Vaishali Pandey, Pratik Aggarwal, Prateek Reena, R. Bhagwat, Suvarna R. Sunil, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology 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 Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

Statistics mining has emerged as a crucial device for agencies in the past two decades that significantly impact selection-making methods. It can be used to gain insights from massive datasets and might assist with an employer's marketing approach, operational performance, customer segmentation, etc. This paper will explore the impact information mining has on decision-making approaches, especially regarding coming across new styles and relationships. It will discuss the blessings of using records mining and how it may help decorate choice-making. It will also look at various methods used to extract statistics from massive datasets and how they facilitate the selection-making method. Finally, it will discuss a number of the constraints of information mining and the ability of answers to overcome them. Standard, this paper aims to pick out the effect of information mining on the choice-making system and how it may be leveraged as a practical device. © 2023 IEEE.

关键词： discuss extract information overcome relationships

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Cloud-Integrated Clean Fuel Generation for Solar-Hydrogen Production with Wireless Sensor Networks 7

Cloud-Integrated Clean Fuel Generation for Solar-Hydrogen Pr...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Naveenkumar, N. Sridevi, V. Bharathi, R. Mishra, Nilamadhab Murugan, S. Velmurugan, S. Department of Computer Science And Engineering Namakkal Tamil Nadu Rasipuram India Academy of Maritime Education And Training Deemed To Be University Electrical And Electronics Engineering Tamil Nadu Chennai India Srm Institute of Science And Technology Faculty of Management Chennai Tamil Nadu Kattankulathur India Vit Bhopal University School of Computing Science And Engineering Madhya Pradesh India Saveetha Institute of Medical And Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India T.J.S. Engineering College Department of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

This research study proposes a novel system that integrates Wireless Sensor Networks (WSNs) with cloud-based platforms to efficiently generate renewable energy and produce clean fuel. By enhancing the safety, efficiency, and reliability of solar power and hydrogen generation, the system integrates optimization strategies, data analysis, and real-time monitoring. Critical parameters such as temperature, sun irradiance, electrolysis efficiency, and hydrogen purity are monitored using WSNs, with data transmitted and processed via cloud techniques to identify patterns, outliers, and areas for improvement. Proactive decision-making and rapid adjustments are facilitated through remote monitoring and control capabilities, leading to maximized energy production and clean fuel output. Security measures including encryption, access controls, and regular audits safeguard data privacy and integrity, while multiple sensor deployments, robust communication protocols, and a resilient cloud infrastructure ensure system reliability. This integrated approach offers an effective solution for advancing renewable energy generation and clean fuel production, contributing to environmental sustainability and a greener future. © 2024 IEEE.

关键词： Energy efficiency

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