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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Prakash, Vallakati Bhanu Shirisha, Dhanavath Reddy, Gopu Suryaprakash Hariharan, Shanmugasundaram Kukreja, Vinay Prasad, Andraju Bhanu Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350340600

The majority of the earth's water bodies are currently contaminated. This occurs primarily as a result of massive amounts of waste being dumped into it. The majority of the garbage is made up of toxic materials, non-biodegradable materials, and e-waste, which severely harms the marine ecosystem. Additionally, it causes the loss of human life as a result of natural disasters. It ceases throwing this waste into the water bodies in order to solve these issues and keep them tidy and clean. If it can't be done, it would be better to start the cleaning process, which involves getting rid of these kinds of things. The study will be crucial in resolving this issue through the use of some machine learning techniques, including image classification and image recognition. Here, sizable collection of related e-waste image datasets is used to train the model in this procedure. Further, the machine will begin recognizing it so that it can be distinguished from the water bodies. Since the use of these kinds of procedures will make nature more environment friendly. In the end, it promotes economic expansion and sustainable development. © 2023 IEEE.

关键词： Object detection

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Classification of Malignant or Benign Cancer using Neural Networks 7

Classification of Malignant or Benign Cancer using Neural Ne...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Haidar, Gaby Abou Achkar, Roger Semaan, David Owayjan, Michel American University of Science and Technology Beirut Lebanon Faculty of Engineering and Computer Science Department of Computer and Communications Engineering Lebanon

ISBN: (纸本)9798350325850

Cancer detection and predetection are being highly researched and studied recently due to the high density spread of this disease. It occurs when abnormal cells divide continuously without limitation where it can spread randomly to different parts of the body. Different types of cancer are recorded targeting the blood, lungs, bones, liver, and many other organs. People with cancer have similar symptoms such as lumps, abnormal bleeding, eating problems, fatigue, weight loss, and some more severe issues. Breast Cancer, which will be addressed in this work, is a type of cancer that hits women in general. It is dangerous especially when cells around the breast area overgrow and cause lumps on the breasts. In order to pre-detect and try to avoid breast cancer., it's important to perform annual mammography check. This work is aimed to create a classification system that uses neural network algorithms to conclude whether a certain tumor is a malignant or a benign tumor. Back Propagation algorithm as well as supervised learning were used and tested with highly satisfying successful classification. © 2023 IEEE.

关键词： Diseases

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Cloud Security in a Multicloud World: Best Practices for Protecting Data and Applications Across Multiple Platforms 4

Cloud Security in a Multicloud World: Best Practices for Pro...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Srinivasulu, Bijjam. Anil, Degala Sowmya M, Lalitha Katuri, Lalitha Swapna Rani, K. Vidya Jyothi Institute of Technology Department of Information Technology Hyderabad India Kg Reddy College of Engineering and Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350352931

As multicloud continues to evolve at a fast pace, it is critical to have sound security measures to protect data and applications. In this research, a closer look will be taken at advanced security algorithms that have been developed to work well within the multicloud systems These are Homomorphic Encryption (HE), Federated Identity Management (FIM), Intrusion Detection Systems (IDS), and Risk Based Access Control (RBAC). HE is assessed for its capacity to ensure the privacy of data with slight detriment to the efficiency, with encryption efficiency of 92%. From the FIM perspective it is evaluated for strong end user authentication on various levels of the applications which draws 95% effectiveness in the access. The under consideration IDS has revealed 90 % discovery rate of intrusion attempts as compared to traditional systems. The analysis for the for the dynamically provided access control by RBAC model which is structured at 93% effectiveness for risk determination and access limitation. Altogether, these advanced algorithms integrate and improve security measures, which are 15-25% higher than conventional techniques in terms of different parameters. It is therefore evident that these complex approaches need to be incorporated to manage the security issues of multicloud systems accordingly. © 2024 IEEE.

关键词： Cloud security

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： Data quality

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Exploring the Role of Automated Reasoning in Big Data Analysis 15

Exploring the Role of Automated Reasoning in Big Data Analys...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Neetha, S.S. Sahoo, Girija Shankar Karthigaikuar, P. Jayasudha, S. Shinde, Gitanjali R. Lone, Zeeshan Ahmad Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350370249

In large-scale data analysis, computer reasoning is an important role. This era can pull significant files from substantial statistics and in a well-timed manner. In this way, through automatic feel, facts scientists and researchers can pick styles out of your statistics or even locate anomalies that could not have been detected in records units. Additionally, it speeds up constructing decision-making models and stimulates artificial intelligence. Artificial intelligence permits herculean records mining and semantic analysis methods, including device learning, ontologiesystem gaining knowledge and natural language processing. Likewise, it offers a platform for creating and monitoring valuable findings, trends or patterns. This also helped to enhance the accuracy of analytic results and provides a more efficient way for statistics scientists & researcher s to analyze large amounts of data. This generation poses a chance to enhance state-of-the-art strategies by making the correct picks. © 2024 IEEE.

关键词： Aberrations Discriminations Electronic Mechanical Mindset

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Optimizing Skin Lesion Segmentation with UNet and Attention-Guidance Utilizing Test Time Augmentation 6

Optimizing Skin Lesion Segmentation with UNet and Attention-...

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6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Ahamed, Md. Faysal Hossain, Md. Munawar Mary, Mekhala Mariam Islam, Md. Rabiul Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh International Standard University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350385779

To properly diagnose and screen for skin disorders, precise segmentation of the affected area is required. Segmenting skin lesions is difficult because of factors such as the form of the lesion, its proximity to surrounding skin, and the presence of hair. To effectively tackle this matter, we present a pioneering technique that utilizes deep learning to achieve comprehensive automation in skin lesion segmentation. This approach incorporates advanced preprocessing methodologies to enhance the overall performance. The architecture Attention-UNet is employed for the purpose of selectively segmenting images of skin lesions. The high detection rate of this design allows for precise melanoma segmentation in real-time. Finally, in the post-processing phase, we applied test-time augmentation (TTA) to further enhance segmentation precision. Both the ISIC 2017 and ISIC 2018 skin lesion datasets were used for training and testing the suggested technique. The model's mDSC, mIoU, recall, and precision were all 91.93%, 88.15%, 92.72%, and 91.15%, respectively, when trained independently on the ISIC 2017 datasets. Separate training on the ISIC 2018 datasets achieved similar results: 92.17% mDSC, 90.26% mIoU, 92.34% recall, and 92.01% precision. After being trained on a dataset consisting ISIC data from both 2017 and 2018, the proposed method achieved mDSC scores of 92.30% percent, mIoU scores of 90.06% percent, recall rates of 93.91% percent, and precision rates of 90.74% percent. Our model demonstrates superior FPS scores compared to other models, indicating effective real-time performance. Due to its scalability and reliability, the suggested methodological framework may be utilized to create a completely automated computer-aided skin lesion diagnostic system. © 2024 IEEE.

关键词： Deep learning

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VGE: Gene-Disease Association by Variational Graph Embedding

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International Journal of Crowd science 2024年第2期8卷 95-99页

作者： Han, Peng Zhang, Xiangliang Department of Computer Science University of Electronic Science and Technology of China Chengdu611731 China College of Engineering University of Notre Dame Notre DameIN46556 United States

Disease-gene association, an important problem in the biomedical area, can be used to early intervene the treat of deadly diseases. Recently, models based on graph convolutional networks (GCNs) have outperformed previous linear models on predicting the new disease-gene associations, due to its strong capability to capture the relevance of disease and gene in the new semantic embedding space. However, a single embedding vector cannot informatively represent a disease or gene and cannot characterize the uncertainty of their features. We propose to learn a distribution for a disease or gene under the variational autoencoder framework, which enables disease-gene associations to be modeled by the Kullback-Leibler divergence. The experiment results show that our model outperforms the state-of-the-art models significantly in various metrics. © The author(s) 2024.

关键词： Genes

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A V-shaped Distributed Array for Drone-Borne Signal Positioning Using Low-Cost SDRs

A V-shaped Distributed Array for Drone-Borne Signal Position...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Chiu, Sheng-Chun Ye, Min-Rong Lin, Shou-Sheu Department of Computer and Communication Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (纸本)9798350389210

This paper proposes a two-dimensional coordinate positioning system based on a V-shaped distributed antenna array. The system employs two low-cost dual-channel software-defined radio (SDR) platforms, where each SDR functions as an independent uniform linear array (ULA). These ULAs form a distributed antenna array in a V-shaped configuration. The received signals are processed using the Multiple Signal Classification (MUSIC) algorithm to estimate two independent directions of arrival (DOA) of target signal. By applying triangulation principles, the system is able to calculate the DOA and range of the target simultaneously. Most previous works typically conduct short-range experiments in indoor environments. To overcome the low reception sensitivity of off-the-shelf, low-cost SDRs, a drone-borne SDR with a power amplifier is used to extend the testing range. We conducted real-world outdoor flight tests, and the results show that the accuracy of range and angle estimation can reach 9.91% and 8.36%, respectively. © 2024 IEEE.

关键词： Triangulation

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Cardiovascular Disease Prediction with Convolutional Neural Networks and Hyperparameter Tuning 8th

Cardiovascular Disease Prediction with Convolutional Neural ...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Metwally, Abdelnasser Abdelwakil Khan, Asif Ali Amin, Sherif Tawfik Hamdan, Alfadil Ahmed Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer and Network Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence & Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

CVD continues to pose a substantial global health burden, demanding the need for the creation of precise predictive models that can aid in the timely identification and evaluation of risks. This research paper aims to provide a thorough examination of the prediction of CVD by utilizing CNN in aggregation with hyperparameter tuning techniques. This paper examines the effectiveness of three widely recognized CNN architectures, namely ResNet, VGGNet, and Xception CVD. The Tree-structured Parzen Estimator (TPE) is utilized as a method for tuning hyperparameters in order to optimize the CNN models. The study commences with a comprehensive examination of the dataset, encompassing a wide array of cardiovascular CT image data. The chosen CNN architectures serve as the fundamental framework for our predictive models. The selection of these architectures is based on their established track record in effectively addressing image analysis tasks and their capacity to accurately capture complex patterns and features present in medical images. The models undergo training using a dataset in order to acquire knowledge about distinctive features that are linked to various cardiovascular conditions. The experimental findings presented in the study indicate that the utilization of the Xception architecture, in aggregation with TPE for hyperparameter optimization, yields the most favorable predictive accuracy. Specifically, the achieved accuracy rate of 98.26% is notably remarkable. This result highlights the significance of not only choosing advanced CNN architectures but also optimizing their hyperparameters to fully exploit their potential in predicting cardiovascular disease. This establishes it as a promising tool for the early diagnosis and risk assessment of CVD. This study establishes a fundamental basis for future investigations in the advancement of precise and dependable predictive models applicable to various medical imaging contexts. © The Author(s), under exclusive license

关键词： Diseases

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Internet of Things Assisted Human Detection and Rescue Robot: A Game Changer in the Disaster Environment 7

Internet of Things Assisted Human Detection and Rescue Robot...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Akhtar, Md Jawed Singh, Manmohan Suneel, Sajja Sutar, Anand Nayaki, A. Periya Khan, Nikhat Raza Ies College of Technology Department of Computer Science & Engg Bhopal India Ies College of Technology Department of Computer Science And Engineering Mp Bhopal India Hyderabad Telangana Dundigal500043 India Bharati Vidyapeeh College of Engineering Dept of Electronics And Telecommunication Navi Mumbai India Velammal College of Engineering And Technology Department of Information Technology Tamilnadu Madurai India Ies College of Technology Department of Computer Science And Engineering Bhopal India

ISBN: (纸本)9798350359299

In the realm of disaster response, the integration of Internet of Things (IoT) technologies has paved the way for innovative solutions to enhance human detection and rescue operations. This research work introduces a revolutionary approach through the development of the Challenging Human Detection and Rescue Robot (CHDRR). This robot incorporates advanced sensors, including the MQ6 Gas Sensor, LM335 Temperature Sensor, Humidity Sensor, and a Passive Infrared (PIR) Human Body Detection sensor, collectively forming a comprehensive sensory suite. The CHDRR leverages the power of IoT to enable real-time data collection, analysis, and communication, transforming disaster response capabilities. The MQ6 Gas Sensor ensures the detection of hazardous gases in the environment, providing crucial information for safe rescue operations. The LM335 Temperature Sensor and Humidity Sensor contribute to environmental monitoring, allowing the robot to adapt its strategies based on prevailing conditions. The PIR Human Body Detection sensor serves as a key element in the CHDRR's human detection capabilities. Through sophisticated algorithms and machine learning, the robot can identify and locate human presence amidst challenging disaster scenarios. The fusion of these sensors with IoT technology empowers the CHDRR to make informed decisions autonomously, optimizing its efficiency in dynamically changing environments. © 2024 IEEE.

关键词： Internet of things

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