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Digital Society and Intelligent systems (DSInS), International Conference on

作者： Wahidur Rahman Md Shadman Sakib Md Solaiman Hosen Mir Mohammad Azad Hani Alshahrani Adel Sulaiman Mana Saleh Al Reshan Asadullah Shaikh Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of Computer Science and Engineering University of Information Technology & Sciences Dhaka Bangladesh Department of Computer Science Iowa State University USA Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science College of Computer Science and Information Systems Najran University Saudi Arabia Emerging Technologies Research Lab (ETRL) College of Computer Science and Information Systems Najran University Saudi Arabia Department of Information Systems College of Computer Science and Information Systems Najran University Saudi Arabia

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

Because of poor blood flow and susceptibility to infections, breast cancer can be challenging to heal and, if not treated, might cause major problems, including limb amputation and a lowered quality of life. Though several systems exist to identify breast cancer, few combine machine learning (ML), deep learning (DL), and optimization strategies. This work presents a novel method leveraging complex algorithms to precisely identify breast cancer from ultrasound images. The study utilizes a dataset from a secondary source, categorizing it into three identical classes: normal (Class 0), benign (Class 1), and malignant (Class 2). It extracts features using pre-trained convolutional neural networks (CNNs), optimizes these features using the Differential Evolution Algorithm (DEA), and classifies images using standard machine learning algorithms. To show how well it detects breast cancer, the approach combines seven conventional ML classifiers with ten pre-trained CNN models. Using DenseNet121 + DEA + Xtreme Gradient Boosting (XGB) Classifier, DEA chooses important features derived by CNNs with a high accuracy of 97.76%. However, the proposed method can be very effective for real-time breast cancer screening using ultrasound images.

关键词： Limbs Ultrasonic imaging Machine learning algorithms Feature extraction Breast cancer Real-time systems Classification algorithms Convolutional neural networks Standards Optimization

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Understanding the Impact of AI-driven Clinical Decision Support systems 15

Understanding the Impact of AI-driven Clinical Decision Supp...

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

作者： Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Nomula, Varun Kumar Gupta, Ketan Dhanasekaran, S. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States University of the Cumberlands Department of Information Technology KY United States Georgia Institue of Technology Department of Analytics Industrial & Systems Engineering United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of engineering Department of Electronics and Communication engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

Medical selection aid structures (CDSSs) are facts systems designed to assist healthcare experts in the decision-making manner. Conventional CDSSs rely upon analytical fashions and understanding-based algorithms to guide choices. However, the latest advances have visible the emergence of AI-pushed CDSSs that utilize artificial intelligence (AI) to help clinicians in their selection-making. AI-pushed CDSSs are predicted to enhance selection-making accuracy and results by way of leveraging massive datasets and greater state-of-the-art modeling strategies. But, there is nonetheless a lot to understand about the impact of AI-driven CDSSs on healthcare shipping and results. This paper evaluates the present-day literature on the impact of AI-driven CDSSs, exploring the results of this generation for healthcare experts and the healthcare gadget as an entire. It highlights areas in need of additional research and suggests possibilities for the design and implementation of AI-pushed CDSSs. © 2024 IEEE.

关键词： Decision making

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Predictive Analysis of Jordan Road Accidents Severity Using Machine Learning

Predictive Analysis of Jordan Road Accidents Severity Using ...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Dheya Mustafa Layan Sawaged Safaa M. Khabour Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan

Jordan has a high and growing level of traffic accidents reaching 160,600) accidents in 2021, (11,241) of them had human injuries, (589) deaths, and (320) million JOD losses. Road traffic accidents are currently the 8th leading cause of death worldwide, accounting for about 1.35 million fatalities annually. Understanding the primary causes of these accidents and the circumstances in which they occur is essential if governments throughout the world are to put policies in place to limit the number of fatalities brought on by traffic accidents. This project has two objectives: (i) to learn more about Jordan's present road accident situation by performing an exploratory study on several road accident datasets, and (ii) to investigate the effectiveness of various machine-learning techniques in predicting the severity of road accidents in Jordan. The dataset entities were collected, collated, explored, and prepared for use in the model. To assess their predictive performance, five different classification algorithms were trained and tested. The findings of the present study demonstrate that the best algorithm was Logistic Regression, which had an accuracy of 98.1%.

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Device Identification using Network Traces in a Smart Home IoT Network

Device Identification using Network Traces in a Smart Home I...

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International Symposium on Advanced Networks and Telecommunication systems (ANTS)

作者： Jwngfu Brahma Rakesh Matam Nikumani Choudhury Dept. of Computer Science & Engineering Indian Institute of Information Technology Guwahati India Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Hyderabad India

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques.

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Air Traffic Data Analysis Using Recurrent Neural Network (RNN) Classifier During COVID-19 17

Air Traffic Data Analysis Using Recurrent Neural Network (RN...

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17th International Conference on Signal-Image Technology and Internet-Based systems, SITIS 2023

作者： Sheeja Rani, S. Ali, Amal Ibrahim Al Marie, Attiea El-Bannany, Magdi Khedr, Ahmed M. Sharjah University Dept. of Computer Science Sharjah27272 United Arab Emirates Sharjah University Dept. of Information Systems Sharjah27272 United Arab Emirates Umm Al Quwain University College of Business Administration Umm Al Quwain536 United Arab Emirates

ISBN: (纸本)9798350370911

Air navigation achieves a worldwide transportation system for economic growth, creates jobs, and facilitates international traffic and sightseeing. In recent times, COVID-19 has been considered as a most dangerous disease that affects many people. When people travel on air flights, COVID-affected people can easily spread fluids into the air. Thus, prediction of air traffic data is considered a significant problem in identifying COVID disease. Few research works have been developed to perform traffic data analysis but, the accuracy of data analysis was not improved with less time. The Concept Drift Regressive Recurrent Neural Network classifier (CDR-RNNC) model is proposed to analyze air traffic data during COVID-19. The main aim of the developed CDR-RNNC model is to identify air traffic with less time and higher accuracy to perform accurate traffic data prediction. Initially, air traffic data with different features are taken as input and fed into first hidden layer for presenting feature identification to attain traffic data analysis with higher accuracy. Based on identified data features, air traffic data classification is performed by applying Deming regression analysis and identifying the relationship between traffic data. Then, analyzed air traffic data is used to estimate concept drift for classifying traffic data as lesser or higher. Finally classified results for predicting the spread of COVID-19 disease with less time. Experimental evaluation of the CDR-RNNC model is conducted based on metrics such as air traffic data prediction accuracy, time complexity, and false alarm rate with respect to number of air traffic data. The experiment results demonstrate that the developed model increases prediction accuracy with minimum amount of time required for air traffic data prediction when compared to state-of-the-art works. © 2023 IEEE.

关键词： COVID-19

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Weapon detection system for surveillance and security

Weapon detection system for surveillance and security

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2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023

作者： Khalid, Shehzad Waqar, Abdullah Ain Tahir, Hoor Ul Edo, Onome Christopher Tenebe, Imokhai Theophilus Bahria University Dept. of Computer Engineering Islamabad Pakistan Auburn University at Montgomery Department of Information Systems Alabama United States Mineta Transportation Institute San Jose State University CA United States

ISBN: (纸本)9781665463720

Weapon detection is a critical and serious topic in terms of public security and safety, however, it's a difficult and time-consuming operation. Due to the increase in demand for security, safety, and personal property protection, the requirement for deploying video surveillance systems capable of recognizing and interpreting scenes and anomalous occurrences plays an important role in intelligence monitoring. In certain regions of the globe, mass shootings and gun violence are on the increase. Timely detection of the presence of a gun is critical to prevent loss of life and property. Several object detection models are available, which struggle to recognize firearms due to their unique size and form, as well as the varied colors of the background. In this study, we proposed a state-of-the-art system based on a deep learning model YOLO V5 for weapon detection that will be sufficiently resilient in terms of affine, rotation, occlusion, and size. We evaluated the performance of our system on a publicly available dataset and achieved the F1-score of 95.43%. For the detection and segmentation of firearms, we implemented Instance segmentation or Pixel level segmentation which employed Mask-RCNN. The system achieved the detection accuracy (DC) of 90.66% and 88.74% Mean intersection over union (mIoU). The purposed methodology combined different data augmentation and preprocessing methods to improve the accuracy of the proposed weapon detection system. © 2023 IEEE.

关键词： Object detection

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Observation of The Use of Renewable Energy Charging Infrastructure in Electric Vehicles

Observation of The Use of Renewable Energy Charging Infrastr...

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2022 International Conference on Science and Technology, ICOSTECH 2022

作者： Faturahman, Adam Oktavyra, Regina Dinda Aris Widya, Tilly Raycitra Habibillah, Thufail Rafii University of Raharja Dept. System Computer Tangerang Indonesia University of Raharja Dept. Faculty of Science and Technology Tangerang Indonesia University of Raharja Dept. Information Systems Tangerang Indonesia South Tangerang City Government Dept. Industry and Commerce Tangerang Indonesia

ISBN: (纸本)9781665479851

To accommodate the rapid adoption of the commu-nity to new types of transportation, namely electric vehicles whose demand is increasing rapidly, the need for reliable charging materials has increased. In addition, alternative energy sources such as wind and solar energy become needed to help sustain the supply of local electricity grids that are under pressure and need support abundant and cheap alternative energy systems, excellent and affordable alternative energy systems are supposed to help provide energy. Hi, current electricity needs. Therefore, this new refueling station has emerged globally for being praised for being innovative based on renewable energy. Looking at the development of studies related to this type of alternative refueling, We wrote our observations on studies related to alternative refueling in this paper. Resources, potential, planning, control, and pricing are areas of comprehensive research that we present in this paper. Methods such as the appropriate load arrangement of the interval system on storage and control methods on design management and others will be discussed in the article. They will focus on controlling resources that will address the problems that previous research has had. And learning and clarifying the challenges faced and providing suggestions for solutions that meet the challenges regarding this study are also part of the observations we made in this paper. Providing an understanding and overview of this study is our goal in creating this paper so that readers can better understand this study more deeply and build the foundation for further research on this field. Providing solutions to be able to create charging resources that can utilize renewable resources more efficiently and develop use in the community is the goal of this research. © 2022 IEEE.

关键词： Solar energy

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Method and Algorithm of Successive Approximation Analog-to-Digital Conversion of information in Management systems

Method and Algorithm of Successive Approximation Analog-to-D...

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International Conference on Advanced computer information Technologies (ACIT)

作者： Lubomyr Petryshyn Mykhailo Petryshyn dept. of Enterprise Management AGH University of Krakow Krakow Poland dept. of Computer Science and Information Systems Precarpathian National University Ivano-Frankivsk Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

Modeling methods and algorithms for analog-to-digital conversion of information in control systems are significant components in the development and design of devices for analog signals digitizing. This process involves several steps, including sampling, quantization, and encoding, to convert continuous signals into a digital format that can be easily manipulated and analyzed by digital *** quest to enhance the speed of converters is driven by the ever-increasing demand for rapid and efficient data processing. The research into additive successive approximation methods has yielded a novel approach: bitwise balancing. This innovative method, supported by a robust mathematical model, promises to revolutionize analog-to-digital conversion by significantly reducing the conversion time. The empirical analysis suggests that this reduction could range from 6 to $25 \%$, marking a substantial improvement over existing methods. This breakthrough shows not only the potential for faster data processing but also sets a new direction for the development of technical apparatuses designed for swift information form conversion.

关键词： Additives Quantization (signal) Data processing Approximation algorithms Control systems Mathematical models Encoding information technology Analog-digital conversion information systems

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VascularVision: Mixed Reality Based Teaching and Learning Environment for Cardiovascular Conditions

VascularVision: Mixed Reality Based Teaching and Learning En...

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Advancements in Computing (ICAC), International Conference on

作者： Hashith Nissanka Sajini Wijayasundara Jude Rajarathne Sahan Vidanagamage Didula Thanaweera Arachchi Pradeep K. W. Abeygunawardhana Dept. Information Technology Sri Lanka Insitute of Information Technology Kandy Sri Lanka Dept. Information Technology Sri Lanka Insitute of Information Technology Colombo Sri Lanka Dept. Information Technology Sri Lanka Insitute of Information Technology Malabe Sri Lanka Dept. Computer Systems Engineering Sri Lanka Insitute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Developments in Augmented Reality (AR) and Virtual Reality (VR) have the potential to revolutionize medical education, especially in the areas of Coronary Angiography and Cardiac Auscultation. By giving medical and nursing students realistic, interactive experiences, the suggested VR and AR settings seek to modernize medical education by addressing the issues regarding the lack of real-world practice. Interactive 3D models, such as a realistic virtual body, rotatable heart models, ECG recordings, and real-life heart sounds, are available in the VR Cardiac Auscultation Environment. With the use of these components, students can practice diagnosing diseases including bradycardia, tachycardia, and cardiac murmurs in a dynamic and risk-free setting, improving their diagnostic skills. Similar to this, the AR Cardiac Auscultation Application combines interactive quizzes, machine learning for diagnostic verification, and real-time ECG scans to provide a powerful tool for teaching students how to recognize and understand various heart diseases and noises, this also can be a verification tool for junior doctors. By giving students firsthand experience with intricate medical procedures, virtual reality and augmented reality platforms offer a compelling and realistic approach to medical education. According to the research, these instruments can greatly enhance diagnostic proficiency, which will ultimately result in better educational outcomes and more qualified healthcare workers.

关键词： Heart Solid modeling Three-dimensional displays Tachycardia Education Medical services Electrocardiography Medical diagnostic imaging Augmented reality Diseases

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Engineering Scientific Assistants using Interactive Structured Induction of Programs

arXiv

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arXiv 2025年

作者： Surana, Shraddha Srinivasan, Ashwin Thoughtworks Technologies National Games Road Pune India Dept. of Computer Science & Information Systems BITS Pilani Goa India

We are interested in the construction of software that can act as scientific assistants to domain specialists. It is expected that such assistants will be needed to accelerate the identification of ways to address complex problems requiring urgent solutions. In this paper, our focus is not on a specific scientific problem, but on the software-engineering of such ‘science accelerators’. Recent developments in ‘No Code’ techniques would seem to suggest that scientist can simply hypothesise solutions simply by conversing with a large language model (LLM). However, for complex scientific problems, this seems unlikely given the current state of LLM technology. What does appear feasible is that a software engineer can use LLMs to rapidly construct programs for use by a domain-specialist, including the specialist’s requirements expressed in natural language. We propose the design of an interactive form of ‘structured’ inductive programming in which a software-engineer and an LLM collaboratively construct an ‘assistant’ for a scientific data analysis. The paper describes a simple implementation called iStrucInd that adapts a ‘2-way Intelligibility’ protocol to implement the interaction between the software engineer and the LLM. We test the tool on two different non-trivial scientific data analysis tasks. Specifically, we compare the system constructed by iStrucInd against systems constructed manually and by Low Code/No Code methods along dimensions of: (a) program performance;(b) program quality;and (c) programming effort. The results show iStrucInd allows a software engineer to develop better programs faster suggesting interactive structured induction can play a useful role in the rapid construction of scientific assistants. © 2025, CC BY-NC-ND.

关键词： C (programming language)

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