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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Srivastava, Dolley Pandey, Himanshu Agarwal, Ambuj Kumar Sharma, Richa Department of Computer Science and Engineering Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology University of Lucknow Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819937158

Heart disease is rapidly overtaking other causes of death in India, and it is a major threat to both men and women. Among the top causes of mortality throughout the globe, heart disease ranks first. Therefore, it is crucial to accurately and rapidly forecast cardiac problems at an early stage to guarantee the lives of millions of people. In the realm of medicine, machine learning has become more important. The realm of machine learning is where the idea of boosting first appeared. The underlying premise is that by merging multiple cases into a more accurate forecast, the accuracy of a rather ineffective classifying tool may be improved. Subsequently, this overarching idea was implemented in statistical modeling. The purpose of this research is to evaluate and contrast the performance of four popular boosting algorithms for machine learning in the context of cardiac illness diagnosis and prediction. Test results show that among the models evaluated, XGBoost performed best in terms of classification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine learning

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Stock Price Prediction Using Fuzzy-RNN and Portfolio Management with Genetic Algorithm 8th

Stock Price Prediction Using Fuzzy-RNN and Portfolio Manage...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Jaiswal, Hrishikesh Anjali Arya, K.V. Department of Information Technology ABV-Indian Institute of Information Technology and Management Gwalior474015 India Department of Computer Science and Engineering ABV-Indian Institute of Information Technology and Management Gwalior474015 India

ISBN: (纸本)9789819738588

Stock prediction and portfolio management are vital in the financial industry, aiding investors in informed decisions. This study introduces a novel approach termed the Stock Prediction and Portfolio Management Model, which amalgamates advanced machine learning algorithms with portfolio optimization techniques. The Stock Prediction and Portfolio Management Model framework substantially enhances the precision of stock price predictions, leading to an elevated performance of investment portfolios while concurrently mitigating inherent risks. The initial phase of the model focuses on predicting stock prices by harnessing historical data and employing technical indicators like the Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD). In the subsequent phase, the SPPMM system meticulously constructs optimal portfolios utilizing genetic algorithms, further solidifying its efficacy in aiding investors in making informed decisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

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Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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An efficient machine learning based CPU scheduler for heterogeneous multicore processors

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International Journal of information technology (Singapore) 2025年第4期17卷 2493-2501页

作者： Allaqband, Sugariya Firdous Nazish, Mir Allaqband, Saltanat Firdous Bashir, Janibul Banday, M. Tariq Department of Electronics & amp Instrumentation Technology University of Kashmir Srinagar India Department of Computer Science & amp Engineering Indian Institute of Technology Madras India Department of Information Technology National Institute of Technology Srinagar India

While machine learning has excelled in various domains, its impact on computer architecture has been limited. Despite powerful, flexible multicore CPUs, efficient thread scheduling remains challenging due to mapping overhead. This study introduces a novel approach that leverages machine learning to enhance CPU scheduling in heterogeneous multicore systems by developing a mapping methodology that improves system throughput. The proposed scheduling approach uses Long Short-Term Memory, Artificial Neural Networks, and Linear Regression to predict thread parameters and Instructions Per Cycle for threads running on different cores. The predicted values are then utilized to schedule threads using the mapping that maximizes Instructions per Cycle count. The results demonstrate that the artificial neural network outperforms Linear Regression for predicting Instructions Per Cycle values. In addition, compared to traditional heterogeneous scheduling, the proposed machine learning-based scheduling approach enhances the performance of the heterogeneous system by about 1.2 times and increases throughput by 20%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ANN CPU scheduling Linear regression LSTM Machine learning

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The Probability of Connectivity in the 2 Way / 2 Lane Platoon-Based V2I Communication Network 15

The Probability of Connectivity in the 2 Way / 2 Lane Platoo...

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15th International Conference on Developments in esystems Engineering, DeSE 2023

作者： Al-Nasir, Ahmed Waleed K. Mubarek, Foad Salem College of Computer Science and Information Technology University of Anbar Department of Computer Science Ramadi Anbar Iraq

ISBN: (纸本)9798350335149

Congestion on major highways can be alleviated by moving in platoons. By exchanging information about their vehicles over VANETs, platoon members may keep the distance between their vehicles and inside the platoon consistent. To be sure, the strength of a VANET's connections can be gauged in part by its connection quality. This research investigates the probabilities of access, reachability and the connectivity between nodes (vehicles) and roadside-units (RSUs) in a two-way platoon-based VANETs that make use of such infrastructure. We call this the connectivity probability, and it is the likelihood that, during a given number of hops, moving vehicles on the express-way will be able to reach at least (1) RSU other than the road. In addition to looking at the connection on the same route, we also look at the link via vehicles on nearby surrounding roads and how they connect to the RSU. The analytical results (mathematical equations and calculations) have been validated by simulations, which show that a network's existence of platoons can boost the likelihood of connectivity. There is a greater chance of connectivity in a vehicle-To-infrastructure (V2I) network with multiple communication channels than in a network with only one. The results of this study can help eliminate the traffic jams and promote safer, more considerate motorists. Then everyone who is using the roads can travel in greater ease and security. These findings also provide substantial theoretical support for the design of a relish intelligent transportation system (ITS). © 2023 IEEE.

关键词： Vehicles

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A systematic Literature Review of Food Recommender systems

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SN computer science 2024年第1期5卷 174页

作者： Mahajan, Pratibha Kaur, Pankaj Deep Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India Computer Science and Engineering Department Guru Nanak Dev University Regional Campus Jalandhar India

The enormous variations in food choices and lifestyle in today’s world have given rise to the demand of using recommender system as a suitable tool in making appropriate food choices. Need for choosing nutritious food items is becoming important in today’s modern lifestyle as people are getting indulged in eating unhealthy food and thus leading to miserable health status. Many lifestyle-related diseases, such as diabetes and obesity, can also be reversed by proper diet. Filtering techniques in recommender system use a dataset of items and user’s preferences as input to discover a list of well-judged items as suggestions. In light of the above said, this paper presents an exploratory study of the recommendation approaches and methods utilized for food and recipe recommendations in the past decade. For this purpose, several relevant papers published in the food domain from 2006 to 2023 have been extensively studied. Further, the research articles are categorized based on the filtering techniques, methods, functionalities and data sources used by the researchers. A comparative study of the recommendation approaches revealed the advantages and disadvantages of different approaches. Also, the paper emphasizes the importance of food recommendation techniques in health and nutrition management of an individual. The review highlights the need to further explore the implementation of recommender systems in the food industry. Furthermore, the findings of this survey provide researchers with insights and future directions on recommender systems in the domain. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Food Food/Recipe/Menu recommender system information retrieval Menu Recipe Recommender system

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Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第15期16卷 172-186页

作者： Ilias, Irma Syarlina Che Ramli, Suzaimah Wook, Muslihah Hasbullah, Nor Asiakin Department of Computer Science Faculty of Defense Science and Technology National Defence University of Malaysia Selangor Malaysia Computer Engineering Technology Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

Images for social media are an essential component of any blog or social media post. As we now live in the age of the ‘camera in everyone’s pocket’, a new dynamic era of image creation and content has emerged. However, people use media due to specific motives or gratifications that lead to their satisfaction of social media use. This research study utilized uses and gratification theory (UGT), a sociology theory related to the motives of people using the media. The UGT factors include enjoyment, entertainment, social influence, social interaction, and information sharing. There were 441 data that were collected and analysed. IBM SPSS Statistics 20 and Smart PLS 3.3.3 were used for the data analysis. Result showed that information sharing was the most influencing factors on users’ satisfaction to use image in social media. Therefore, the factors identified in these studies could be used as a guideline and references in future study related to social media awareness and implications. © 2022. International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Social networking (online)

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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Identification of Emergent Properties Occurrences Factors in system-of-systems

Identification of Emergent Properties Occurrences Factors in...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Subramanium, Lehka Hassan, Sa'adah Zulzalil, Hazura Osman, Mohd. Hafeez Universiti Putra Malaysia Faculty of Computer Science and Information Technology Selangor Malaysia

ISBN: (纸本)9798350302684

systems-of-systems (SoS) is a term used to describe independent systems that collaborate to accomplish a task they are unable to complete separately. One of the challenges of developing SoS is to manage the emergent properties that arise from the interaction of independent systems. The emergent properties can be desirable or undesirable. It is important to comprehend the undesirable emergent properties by foreseeing them before they manifest due to potentially adverse effects. Identifying and anticipating them as soon as they appear to minimize any adverse effects is crucial. Notwithstanding the thorough research and efforts, undesirable emergent behavior continues to appear in SoS. Such emergent properties are essential to prevent the SoS from deviating from its intended requirements and objectives. One way to understand the emergent properties is to examine the factors that influence their occurrence. Therefore, a study has been conducted to identify factors of undesirable emergence properties in SoS across different phases of the software development lifecycle. The identified factors could further derive solutions for overcoming the challenges of undesirable emergent properties in SoS. Hence, SoS can have long lifespans as each constituent system evolves. © 2023 IEEE.

关键词： Software design

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Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles 10

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

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10th IEEE information technology International Seminar, ITIS 2024

作者： Purnama, Iwan Pane, Rahmadani Sooai, Adri Gabriel Edi, Firman Chamid, Ahmad Abdul Adi, Puput Dani Prasetyo Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Batam City Indonesia Muria Kudus University Department of Informatics Engineering Kudus Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This article presents a simulation study on the use of LoRaWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for Autonomous Vehicles, utilizing LoRaWAN parameters such as bit rates, spreading factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular and random motion patterns. The aim is to assess the performance of LoRaWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spreading factor (SF) increases, and bit rates tend to decrease, indicating a drop in communication performance over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss increases as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoRaWAN parameters for real-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks. © 2024 IEEE.

关键词： Packet loss

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