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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Pant, Piyush Rajawat, Anand Singh Goyal, S.B. Kemat, Baharu Bin Mihaltan, Traian Candin Verma, Chaman Raboaca, Maria Simona School of Computer Sciences and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia City Graduate School City University Petaling Jaya Malaysia Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest1053 Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Romania

ISBN: (纸本)9798350321388

The exploration of Mars has provided vast amounts of weather data that present unique challenges for analysis and prediction. To address these challenges, this research paper focuses on the application of machine learning techniques for the analysis of Mars weather data. The objective is to develop models that can effectively extract patterns, uncover hidden relationships, and enable accurate predictions in the Martian weather system. The research begins with a comprehensive review of the available Mars weather data. The dataset, consisting of historical records, serves as the foundation for training and evaluating machine learning models. The analysis of the Mars weather dataset reveals the planet's icy and harsh climate, with average maximum temperatures of around -21°C and average minimum temperatures of -80°C. The temperature variations show that the minimum temperature fluctuates within a narrower range of 20-30°C over the course of Mars sols from 0 to 2000, while the maximum temperature experiences larger variations of about 40-50°C. During this time, the atmospheric pressure on Mars fluctuates between 720 Pa and 950 Pa. In addition, using the elbow method revealed that 3 clusters were the ideal number for identifying distinct patterns in the weather data. The linear regression model also attained an accuracy of 85%, demonstrating its efficacy in forecasting weather patterns on Mars. © 2023 IEEE.

关键词： Sols

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A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

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computers, Materials & Continua 2023年第6期75卷 5697-5715页

作者： Seema Rani Anju Anupma Sangwan Krishna Kumar Kashif Nisar Tariq Rahim Soomro Ag.Asri Ag.Ibrahim Manoj Gupta Laxmi Chandand Sadiq Ali Khan Department of CSE Guru Jambheshwar University of Science&TechnologyHisar125001India Department of Hydro and Renewable Energy Indian Institute of Technology RoorkeeIndia College of Computer Science and Information Systems Institute of Business ManagementKorangi Creek RoadKarachi75190SindhPakistan Faculty of Computing and Informatics Universiti Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Computer Science Department UBIT Karachi UniversityKarachi75270Pakistan

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale *** of the major issues in such a network is the localization of underwater *** is required for tracking objects and detecting the *** is also considered tagging of data where sensed contents are not found of any use without *** is useless for application until the position of sensed content is *** article’s major goal is to review and analyze underwater node localization to solve the localization issues in *** present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes ***,a detailed subdivision of these localization schemes is ***,these localization schemes are compared from different *** detailed analysis of these schemes in terms of certain performance metrics has been discussed in this *** the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

关键词： Underwater wireless sensor networks localization schemes node localization ranging algorithms estimation based prediction based

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Analysis of the Impact of an Execution Algorithm with an Order Book Imbalance Strategy on the Financial Market Using an Agent-based Simulation

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Transactions of the Japanese Society for Artificial Intelligence 2024年第4期39卷

作者： Endo, Shuto Mizuta, Takanobu Yagi, Isao Information and Computer Sciences Graduate School of Engineering Kogakuin University Japan SPARX Asset Management Co. Ltd Japan Department of Information Science Faculty of Informatics Kogakuin University Japan

One measure of financial markets is order book imbalance (OBI), defined as the number of buy orders minus the number of sell orders in the order book around the best quote. Thus, OBI is an indicator of an imbalance in supply and demand, which can potentially impact the price as price movements in the financial market. There is known to be a positive correlation between OBI and returns, and some investors attempt to exploit this characteristic to improve their investment performance. In financial markets, an investor sometimes want to place a large order. When the investor places the large order at one time, some other investors find out someone wants to trade large amount and trade ahead of time, or the market price gets rough and losses are incurred. Therefore, execution algorithms are used, by which a machine automatically divides orders into smaller lots to avoid distorting the market price. Execution algorithms could be expected to achieve improved performance when OBI is taken into account, but no such findings have been reported so far. One reason for the lack of research findings is that it is virtually impossible to measure the impact of OBI alone on the performance of execution algorithms, since there are many external factors that can affect financial markets. Multi-agent simulation is one way to solve problems that are difficult to analyse using empirical research and conventional methods. In a multi-agent simulation, individual actors in the world are regarded as agents and the behavioural rules and interactions of these agents constitute a model. In this context, a financial market constructed using multi-agent simulation is called an artificial market. In this study, we modelled an execution algorithm considering OBI, investigated how the model is affected by the market under several market patterns using artificial markets, and analysed the mechanism. The results showed that in stable markets, the performance of the execution algorithm with the OBI st

关键词： Financial markets

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Challenges and Recommendation of the Information Technologies Application in Hazardous Medical Waste Management amidst Pandemic Covid-19 2

Challenges and Recommendation of the Information Technologie...

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2020 2nd International Conference on Science and Technology, ICoST 2020

作者： Rahayu, P. Rohajawati, S. Fairus, S. Saragih, H. Akbar, H. Information System Computer Science Faculty Universitas Mercu Buana Jakarta Indonesia Information System Informatics and Environmental Engineering Universitas Bakrie Jakarta Indonesia Department of Informatics Computer Science Faculty Universitas Esa Unggul Jakarta Indonesia

Handling of Covid-19 patients requires more medical equipment than normal conditions, increasing the amount of hazardous medical waste. The management of hazardous medical waste has many challenges;therefore, it needed a strategy to solve it using information technology. Based on a filter of 376 articles, this review adopted a Systematic Literature Review approach to evaluating the recent challenge and recommendation in the field of Hazardous Medical Waste Management amidst Covid-19. Through a four-phase workflow consisting of searching, screening, excluded, and included literature search, this study identified the most influential journals, scholars, and articles that have been influential in the domain of Hazardous Medical Waste Management. These literature review results are four challenges in Hazardous Medical Waste Management, including Regulation, Technology, Financial and Awareness. The other finding is IT application recommendations such as IoT, Big Data, DSS, AI and GIS. By providing the latest research about the challenges and recommendations in the domain of Hazardous Medical Waste Management amidst Covid-19, the paper serves as a preliminary recommendation for practitioners and researchers to link current research to future trends. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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High-Level Deep Features and Convolutional Neural Network for Evaluating the Classification Performance of File Cluster Types 1

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Ali, Rabei Raad Dawd, Lahib Nidhal Mostafa, Salama A. Rachmawanto, Eko Hari Jubair, Mohammed Ahmed National University of Science and Technology Thi-Qar Nasiriyah Iraq Computer Techniques Engineering Department Dijlah University College Baghdad Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Department of Informatics Engineering Dian Nuswantoro University 207 Imam Bonjol Street Semarang50131 Indonesia Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna66002 Iraq

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Accurate file recovery of the corrupted or fragmented file is a very important task when missing the file system, since file recovery procedure involves methods, techniques, and tools that analyze and classify the contents of each data cluster including JPG file or non-JPG file. For classification problems, convolutional neural networks and high-deep learning have been applied to be very efficient and effective in file recovery. This study aims at finding a systematic comparison between a support vector machine (SVM) classifier method and an extreme learning machine (ELM) classifier method for classification issues. The classification methods automatically classify the files in a continuous series of data clusters based on three deep features which are rate of change (RoC), byte frequency distribution (BFD), and entropy. The methods are automatically assigning a class label of JEG file or non-JPG file for fragmented data clusters. The RABEI-2017 dataset was used for evaluating the performance of this study. The results demonstrate that the SVM method outperforms the ELM method with a high accuracy rate to classify fragmented JPG files. The accuracy of classification of the SVM method is 90.15% and of the ELM method is 96.21%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Support vector machines

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A brain stroke detection model using soft voting based ensemble machine learning classifier

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Measurement: Sensors 2023年 29卷

作者： Srinivas, A. Mosiganti, Joseph Prakash Department of Computer Science and Engineering Faculty of Informatics University College of Engineering Osmania University Telangana Hyderabad India SRR Govt. Arts & Science College Telangana Karimnagar India Department of Computer Science and Engineering MCET Telangana Hyderabad India

A brain stroke is a medical emergency that occurs when the blood supply to a part of the brain is disturbed or reduced, which causes the brain cells in that area to die. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. The proposed model is an ensemble machine learning algorithm, which integrates predictions obtained from several individual classifiers like Random Forest, Extremely Randomized Trees and Histogram-Based Gradient Boosting to make a final prediction. Each classifier provides a probability estimate for each class, final prediction is based on weighted average of these probabilities. The weights assigned to each classifier can be based on their performance on a validation set or can be set uniformly. The proposed soft voting model improved accuracy and robustness of final prediction compared to a single classifier. The limitation of study classification of stroke type can lead to appropriate use of resources and help to reduce healthcare costs. To solve this issue, future introduced a swarm intelligence-based optimization for improve classification accuracy. The proposed model obtained an accuracy of 96.88%. In order to carry out the investigation, the stroke prediction dataset is collected from UCI machine learning repository. © 2023 The Authors

关键词： Forecasting

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Blockchain-Enhanced Energy-Efficient Architectures for Sustainable Internet of Things Ecosystems 1st

Blockchain-Enhanced Energy-Efficient Architectures for Susta...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Goyal, S.B. Rajawat, Anand Singh Verma, Chaman Illés, Zoltán Singh, Jaiteg Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Sciences and Engineering Sandeep University Nashik India Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031860683

Novel technique are required to ensure the long-term viability and maximize the efficiency of energy consumption in the constantly changing Internet of Things (IoT) landscape. In order to accomplish these objectives, this study examines the potential use of blockchain technology into IoT architectural concepts. Our approach utilizes blockchain technology to effectively manage and enhance energy consumption in IoT devices and networks. This is achieved by capitalizing on the decentralized, secure, and transparent characteristics of blockchain. Our objective is to do this by developing a decentralized ledger system that documents all energy transactions and interactions inside an Internet of Things (IoT) environment. This strategy utilizes blockchain technology to provide accountability and real-time monitoring of energy consumption, hence facilitating improved energy allocation and consumption. We delve into the concept and execution of smart contracts that ensure transactions and automate energy-conservation procedures, thereby diminishing the necessity for centralized authority and alleviating energy wastage. In addition, we examine the possibility of blockchain technology to enable direct energy transfer between Internet of Things (IoT) devices. This feature facilitates the development of a self-sustaining Internet of Things ecosystem by enabling devices with surplus energy to vend it to those requiring it. We also tackle the difficulties associated with the integration of blockchain with IoT, such as the energy consumption resulting from blockchain activities and the ability to effectively handle large volumes of data. Furthermore, we offer resolutions to these problems. The proposed framework improves the energy efficiency of IoT ecosystems and promotes their sustainability by advocating for the use of renewable energy sources and decreasing carbon footprints. Through the utilization of simulations and real-world illustrations, we showcase the efficacy of our bl

关键词： Clean energy

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Streamlit Application for Helmet Detection Based on YOLOS: Case Study Indonesia

Streamlit Application for Helmet Detection Based on YOLOS: C...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Habib Habib Kahlil Muchtar Al Bahri Teuku Yuliar Arif Fitri Arnia Safrizal Safrizal Taufik Fuadi Abidin Department of Electrical and Computer Engineering Universitas Syiah Kuala Aceh Indonesia Department of Informatics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Aceh Indonesia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Traffic violations are still the biggest problem in the development of traffic in Indonesia. The three biggest violations are (1) running a red light, accounting for 42% of violations, (2) not wearing a helmet, accounting for 23% of violations, and (3) violating traffic signs, accounting for 9% of violations. The violation of not wearing a helmet is the second largest violation in Indonesia, which is due to the lack of awareness of safety while driving. Additionally, the supervision carried out by the security forces is also somewhat inadequate. Supervision is only conducted in certain areas, leading to a high number of violations. To address these issues, the YOLOS (You Only Look at Once Sequence) model, using Vision Transformer and Pytorch, was developed with the assistance of the AI City Challenge 2023 dataset. This model, trained for 200 epochs, is capable of detecting riders who wear helmets and those who do not. It achieves average precision and average recall values for objects with large area coverage in the precision and recall curves (large area), namely 38.8% and 57.2%, and on average precision with an IoU parameter of 0.5, obtaining a value of 63.5%. The implementation of the model in the Streamlit framework to create a system that can be used for detection was successfully completed. The system is capable of performing detection on a single image, webcam video, and video from RTSP CCTV.

关键词： YOLO Performance evaluation Head Webcams Urban areas Streaming media Transformers Safety Performance analysis Security

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Implementation of Face Detection to Count the Number of Mall Visitors

Implementation of Face Detection to Count the Number of Mall...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Gilang Ryan Fernandes Didik Wiguna Fauzan Natsir Triyadi Nandang Suwela Achmad Birowo Informatics Engineering Faculty of Engineering and Computer Science Indraprasta PGRI University South Jakarta Indonesia

ISBN: (纸本)9781665453905

In the current digital era, various data can be generated with the capabilities of a computer system, making it easier and faster to make decisions. Mall is a place for shopping as well as entertainment that presents many well-known brands. In marketing the room to tenants, data is needed that can attract tenants to cooperate with the mall. The method used in this research is the Viola-Jones method which is used to detect objects because this method has an efficient algorithm, so it does not take long to detect objects. There are four main contributions to the Viola-Jones theory, including the Haar Feature, Integral image, Adaptive Boosting or AdaBoost, and the Combination Classifier of Cascade. The purpose of this study is to analyze and evaluate the number of visitors who come as a reference for determining prices for tenants. The results of this study found that the Viola-Jones method can effectively calculate the number of visitors with a face detection approach and make it easier for mall management to get mall visitor data.

关键词： Java Runtime Face recognition Lighting Entertainment industry Boosting Feature extraction

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A Comparison of RGB and RGNIR Color Spaces for Plastic Waste Detection Using The YOLOv5 Architecture 4

A Comparison of RGB and RGNIR Color Spaces for Plastic Waste...

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4th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2022

作者： Tamin, Owen Moung, Ervin Gubin Dargham, Jamal Ahmad Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Kota Faculty of Computing and Informatics Kinabalu Malaysia Universiti Malaysia Sabah Kota Faculty of Engineering Kinabalu Malaysia Higashi-Hiroshima Japan School of Computer Science Taylor s University Lakeside Campus Selangor Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Plastic waste is a serious environmental issue that damages human health, wildlife, and habitats. Many researchers have come out with multiple solutions on the problem. One of the most efficient ways is to implement machine learning approaches to detect plastic waste in common areas. Deep learning is a powerful machine learning approach that automatically learns image features for object recognition tasks using an object detector. Therefore, this paper proposed a recent object detection model, YOLOv5m, to develop a plastic waste detection model. Two plastic waste datasets, which consist of red, green, and blue (RGB) and red, green, and near-infrared (RGNIR) images, are introduced to train the proposed model. The performance of the proposed model is evaluated using 10-fold cross-validation on the two datasets. The proposed model achieves the best result on RGNIR datasets for validation and testing with an average mAP@0.5:0.95 value of 69.39% and 69.45%, respectively. These results indicate that near-infrared information can be a valuable feature representation in machine learning. This opens more possible opportunities, such as the development of automated plastic detection for the robotic and waste management industry. © 2022 IEEE.

关键词： computer vision

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