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Advances in Signal Processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： Prasandika Balage Nuwan Jayawardene School of Computer Science and Engineering University of Westminster London United Kingdom Department of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles.

关键词： Human computer interaction Visualization Accuracy Visual impairment Education Lighting Color Blindness Signal processing Internet of Things

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Distributed Gradient Clustering: Convergence and the Effect of Initialization 58

Distributed Gradient Clustering: Convergence and the Effect ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Armacki, Aleksandar Sharma, Himkant Bajović, Dragana Jakovetić, Dušan Chakraborty, Mrityunjoy Kar, Soummya Carnegie Mellon University PittsburghPA United States Indian Institute of Technology Kharagpur Kharagpur India University of Novi Sad Faculty of Technical Sciences Department of Power Electronics and Computer Engineering Novi Sad Serbia University of Novi Sad Faculty of Sciences Department of Mathematics and Informatics Novi Sad Serbia

ISBN: (纸本)9798350354058

We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the K-means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization. © 2024 IEEE.

关键词： K-means clustering

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Automated Estimation of Left Myocardial Strain from Cine CTA: A Comparison with Cine MRI

Automated Estimation of Left Myocardial Strain from Cine CTA...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Sun, An-Yu Hsu, Li-Yueh Chang, Lin-Ching Bandettini, W Patricia Chen, Marcus Y. National Institutes of Health National Heart Lung and Blood Institute BethesdaMD United States Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States National Institutes of Health Clinical Center BethesdaMD United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States

ISBN: (纸本)9798350362480

Cardiovascular disease, the leading cause of death in the U.S., affects both the heart and blood vessels. Contrast-enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for assessing heart and coronary artery morphology, aiding in the diagnosis of cardiovascular disease. Cine CTA, which captures multiple 3D heart images throughout the cardiac cycle, is particularly useful for evaluating nonischemic cardiomyopathy, a common cause of heart failure unrelated to coronary artery disease. Key biomarkers, such as left ventricular ejection fraction, which measures the amount of blood pumped from the heart's lower chambers per contraction, and myocardial strain, which evaluates the deformation of the heart muscle during contraction and relaxation, are vital for assessing heart function. We developed an automated artificial intelligence (AI) framework to segment various cardiac structures across all image volumes representing different phases of the cardiac cycle. The framework also automatically extracts three short-axis slices to calculate the myocardial strain in the left ventricle. The results are compared with strain measurements obtained from cardiovascular magnetic resonance (CMR) cine imaging. Our study shows both radial and circumferential strains from cine CTA are comparable to those from cine CMR. The proposed AI framework facilitates a comprehensive evaluation of myocardial function throughout the cine CTA, supporting improved diagnostic accuracy. © 2024 IEEE.

关键词： computerized tomography

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Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms

arXiv

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arXiv 2024年

作者： Amin, Al Sarkar, Anik Islam, Md Mahamodul Miazee, Asif Ahammad Islam, Md Robiul Hoque, Md Mahmudul Department of Computer Science and Engineering Uttara University Dhaka Bangladesh National Institute of Technology Silchar India Department of Informatics University of Oslo Oslo Norway Department of Computer Science Maharishi International University Iowa United States Department of Computer Science and Engineering CCN University of Science & Technology Comilla Bangladesh

The Internet has become an essential tool for people in the modern world. Humans, like all living organisms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate approach for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food. © 2024, CC BY.

关键词： Sentiment analysis

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Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

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SN computer Science 2022年第3期3卷 229页

作者： AlShourbaji, Ibrahim Kachare, Pramod Zogaan, Waleed Muhammad, L.J. Abualigah, Laith Department of Computer and Network Engineering Jazan University Jazan 45142 Saudi Arabia Department of Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology Nerul Maharashtra Navi Mumbai 400706 India Department of Computer Science Jazan University Jazan 45142 Saudi Arabia Department of Computer Science Faculty of Science Federal University of Kashere P.M.B. 0182 Gombe Nigeria Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Breast cancer (BC) has been one of the significant causes of death worldwide, and its early detection can play a vital role in increasing the survival rate of this disease. This paper suggests a novel feature learning method for BC diagnosis using the artificial neural network (ANN) by optimizing the hidden layers. It uses a systematic hyper-parameter search method to optimize number of hidden layers, neurons in the hidden layers, activation functions, learning rate, batch size, and training epochs of the ANN. The number of hidden layers and the number of neurons in the hidden layer are investigated using t-distributed stochastic neighbor embedding (t-SNE) technique to specify the best hyper-parameters that achieved the highest testing accuracy while avoiding model overfitting. Comparative analysis showed that the proposed method achieved higher performance than the ANN-based models that utilizes particle swarm optimization (PSO), multi-verse optimizer (MVO), BAT algorithm, and firefly algorithm (FFA) as feature selection (FS) methods, with the best classification accuracy of 0.9948, sensitivity of 0.9815, and specificity of 0.9882 for 10 iterations of holdout cross-validation. The performance of the proposed method is also comparable with several earlier reported state-of-the-art models in the literature. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ANN Brest cancer Classification Feature selection Machine learning

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CoDescribe: An Intelligent Code Analyst for Enhancing Productivity and Software Quality 10th

CoDescribe: An Intelligent Code Analyst for Enhancing Produ...

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10th International Symposium on Applied Computing for Software and Smart systems, ACSS 2023

作者： Das, Souvick Deb, Novarun Cortesi, Agostino Chaki, Nabendu Department of Environmental Science Informatics and Statistics Ca’ Foscari University Venezia30172 Italy Indian Institute of Information Technology Vadodara Gujrat Gandhinagar382028 India Department of Computer Science and Engineering University of Calcutta 67 Sukanta Park Kolkata700101 India

ISBN: (纸本)9789819977826

Automating the documentation, explanation, and modification of source code has been an area of intense research interest for several decades. This paper proposes a novel framework for analyzing source codes to generate natural language summaries, answer complex user queries by reasoning, and even support syntactic error checking and code conversion to other languages. The framework incorporates transformer-based models and knowledge graphs to understand the context of the code and the user’s queries. The contributions of this proposed approach represent a significant leap forward by overcoming the limitations of the most advanced ChatGPT model. The framework ensures the accuracy and reliability of the responses while handling multiple source code files efficiently, making it a valuable asset for software developers and stakeholders. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis

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Journal of Dermatological Science 2025年第1期119卷 9-17页

作者： Sakai, Takashi Sawada, Ryusuke Ichinose, Otoha Terabayashi, Takeshi Hatano, Yutaka Yamanishi, Yoshihiro Ishizaki, Toshimasa Department of Dermatology Faculty of Medicine Oita University Oita Japan Department of Pharmacology Okayama University Graduate School of Medicine Dentistry and Pharmaceutical Sciences Okayama Japan Department of Bioscience and Bioinformatics Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of Pharmacology Faculty of Medicine Oita University Oita Japan Department of Complex Systems Science Graduate School of Informatics Nagoya University Aichi Japan

Background: The development of medical treatments has traditionally relied on researchers leveraging scientific knowledge to hypothesize disease mechanisms and identify therapeutic agents. However, the depletion of novel therapeutic targets has become a significant challenge, resulting in stagnation within pharmaceutical research. Objective: To address the scarcity of therapeutic targets, we developed a machine learning (ML)-based system capable of predicting therapeutic target molecules for diseases. To validate its utility, we applied this system to psoriasis, aiming to identify novel treatment strategies. Methods: Our approach utilized a large clinical database to calculate reporting odds ratios for all drugs associated with the prevention of diseases of interest. We identified target proteins by analyzing large chemical structure databases to discover proteins commonly associated with preventive drug candidates. Experimental validation was conducted by administering a predicted therapeutic candidate in an imiquimod-induced psoriasis mouse model. Results: The ML-based predictions identified drugs for Parkinson's disease as potential preventive candidates for psoriasis. Further analysis highlighted dopamine receptor D2 (DRD2) as a therapeutic target. Administration of a DRD2 agonist alleviated psoriasis symptoms in mice, evidenced by the downregulation of mRNA expression in the IL-17 pathway and reduced serum tumor necrosis factor-α levels. Conclusion: This study demonstrates the utility of a novel ML-based system for identifying therapeutic targets, as shown by its successful application in uncovering the role of DRD2 in psoriasis. Beyond psoriasis, this system offers significant potential for exploring pathological mechanisms and discovering therapeutic targets across various diseases. © 2025 The Authors

关键词： artificial intelligence big data dopamine receptor D2 machine learning psoriasis

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Classification of Wind Turbine Defects based on the SqueezeNet Neural Network 19

Classification of Wind Turbine Defects based on the SqueezeN...

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19th IEEE International Conference on computer Science and Information Technologies, CSIT 2024

作者： Dubchak, Lesia Sachenko, Anatoliy Wolff, Carsten Vasylkiv, Nadiia Bernas, Zenovii West Ukrainian National University Department of Computer Engineering Ternopil Ukraine West Ukrainian National University Research Institute for Intelligent Computer Systems Ternopil Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Tele-Informatics Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany West Ukrainian National University Department of Information Computer Systems and Control Ternopil Ukraine

ISBN: (纸本)9798331542627

The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significantly reduce their efficiency. The study proposes using image classification techniques with neural networks, particularly SqueezeNet, to automate defect detection. The compact nature of SqueezeNet makes it suitable for real-time applications with limited computing resources. Through training and testing, the network achieved an accuracy of 89%, demonstrating its potential to improve wind turbine maintenance and reduce operational costs. © 2024 IEEE.

关键词： defect detection image classification neural network SqueezeNet wind turbine

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Modeling Blockage in High Directional Wireless Systems 2

Modeling Blockage in High Directional Wireless Systems

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Koutsonas, Evangelos Boulogeorgos, Alexandros-Apostolos A. Trevlakis, Stylianos E. Ali, Tanweer Tsiftis, Theodoros A. University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece InnoCube P.C. Research and Development Department Thessaloniki55535 Greece Manipal Institute of Technology Manipal Academy of Higher Education Department of Electronics and Communication Engineering Manipal576104 India University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

ISBN: (纸本)9798331530099

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage stage, i.e. either fully blocked or blockage-free links. However, in realistic scenarios, a link can be partially blocked. Motivated by this, in this paper, we present two low-complexity models that are based on tight approximations and accommodates the impact of partial blockage in high-frequency links. To demonstrate the applicability of the derived framework, we present closed-form expressions for the outage probability for the case in which the distance between the center of the receiver plane and the blocker's shadow center follow uniform distribution. Numerical results verify the derived framework and reveal how the transmission parameters affect blockage. © 2024 IEEE.

关键词： Blockage high-frequency communications modeling outage probability

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An Artificial Intelligence (AI) Based X-AODV Routing Protocol in Wireless Networks 4

An Artificial Intelligence (AI) Based X-AODV Routing Protoco...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Hussain, Khalid Ullah Sheikh, Md Amin Jazri, Husin Bin Muzzaffar, Saira Chowdhury, Sohini Kavita School of Computing and Informatics Albukhary International University Kedah Alor Setar Malaysia School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India

ISBN: (纸本)9798350353266

The multi-hopping mechanism features in the wireless network environment, in which all nodes contain the information of link and connectivity in any infrastructure less manner. When mobile devices either jump from their original network or change their location without describing any fashion, it is named mobile ad hoc network MANET. To ensure the up to date information related to the location and to provide the routing traffic of the node, numerous protocols have been used to remain efficient in achieving this for instance, AODV, DSR, DSDV etc. While we, the peer-to-peer networking, have challenges in many places, security is something that warrants attention more than any other things. The signal corruption hampers data transmission in bedwire networks when the attacks take place. This kind of activity can touch anyone including the layers lying above the physical and below the network layer. In the current discussion we developed, proposition of diversified, efficient and secure AI-based X-AODV routing protocol. We have investigated our proposed protocol against the recent modified two protocols which are SAODV and AraN. The outcome of simulation reveals that our proposed routing protocol takes place in successfully performing of attack detection and is also the best when it comes to providing security and performance. The Simulation outcomes exhibit the Networks' performance improvement. © 2024 IEEE.

关键词： Network layers

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