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IEEJ Transactions on Electronics, Information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Revisiting Knowledge Transfer for Success Enterprise System Project

Revisiting Knowledge Transfer for Success Enterprise System ...

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2021 International Conference on Information technology, ICIT 2021

作者： Hussien, Jamal Abdullateef, Mansoor Kahtan, Hasan Sulaiman, Riza Bangi Malaysia Abdulgabber Abdulhak Consultant Researcher Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665428705

This paper re-examines the relationship between the Knowledge Transfer (KT) and the project management process groups. Knowledge Transfer as one of the knowledge management life-cycle is supposed to have a remarkable impact on the enterprise system success. However, project failure is one of the main problems in enterprise systems management and lack of proper documentation on the results of previous projects and knowledge transfer are the main reasons. In this paper, we have found that enterprise system project divided into three stages, with (6) process groups in the Pre-Implementation stage, (6) process groups during-Implementation stage, and (3) process groups in the post-Implementation, however, there are eleven (11) roles who are evolving to anticipate the fifth results (Project Proposal, Evaluation Ratings, Project Charter, Project Plan, Project Documents), to increase the understanding of knowledge in each project stages to improve the enterprise system project success. © 2021 IEEE.

关键词： Project management

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N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages 22

N-Gram-Based Machine Learning Approach for Bot or Human Dete...

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6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Kavadi, Durga Prasad Sanaboina, Chandra Sekhar Patan, Rizwan Gandomi, Amir Department Of Information Technology B V Raju Institute Of Technology Narsapur Telangana India Department Of Computer Science And Engineering University College Of Engineering Kakinada Jawaharlal Nehru Technological University Kakinada India Decentralized Science Lab College Of Computing And Software Engineering Kennesaw State University MariettaGA30060 United States Faculty Of Engineering And Information Systems University Of Technology Sydney UltimoNSW Australia

ISBN: (纸本)9781450396288

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection. © 2022 ACM.

关键词： Feature Selection

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Economic and environmental analysis of a solar-powered ev charging system in india—a case study 2nd

Economic and environmental analysis of a solar-powered ev ch...

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2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shrivastava, Prashant Soon, Teykok Mekhilef, Saad Ahmad, Furkan Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Power Electronics and Renewable Energy Laboratory Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia School of Software and Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Indian Institute of Technology New Delhi Delhi110016 India

ISBN: (纸本)9789811607486

With the growing environmental concern, the demand for electric vehicle is increasing in India. However, the limited existing charging facilities slow down the rate of adoption in the market. Presently, the solar-based charging system is gaining popularity, due to its low greenhouse gas emission. In this study, a case study for different solar charging systems suited in Delhi is presented. The economic and environmental analysis of different solar-based EV charging systems utilized for charging three types of commercial EVs such as 2, 3, and 4-W is explained. The cost of energy required for 100 km drive is lower with vehicle integrating PV charging system;however, it can act as a supportive system for driving range improvement. The analysis shows that the economically solar rooftop charging system is suitable for all types of EVs. The CO2 emission produced by all types of solar-based EV charging systems is lower than coal-based charging systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Solar energy

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A Soft Tissue Acupuncture Model Based on Mass-Spring Force Ne

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computers, Materials & Continua 2021年第10期69卷 727-745页

作者： Xiaorui Zhang Tong Xu Wei Sun Jiali Duan Sunil Kumar Jha Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Faculty of Information Technology University of Information Technology and ManagementRzeszow35-225Poland

In the simulation of acupuncture manipulation,it is necessary to accurately capture the information of acupuncture points and particles around ***,a soft tissue modeling method that can accurately track model particles is *** this paper,a soft tissue acupuncture model based on the mass-spring force net is *** is used as the auxiliary model and the SHF model is *** is used to establish a three-layer soft tissue model of skin,fat,and muscle,and a layer of the MSM based force network is covered on the surface of soft tissue to realize the complementary advantages and disadvantages of spherical harmonic function and *** addition,a springback algorithm is designed to simulate the springback phenomenon of soft tissue skin during *** evaluation results show that the soft tissue acupuncture modeling method based on mass-spring force net can effectively simulate the springback phenomenon of soft tissue surface during acupuncture surgery,and has good comprehensive performance in the application of virtual acupuncture surgery simulation.

关键词： Mass-spring model puncture simulation virtual surgery soft tissue simulation

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A Three-Stage Cutting Simulation System Based on Mass-Spring Model

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computer Modeling in engineering & Sciences 2021年第4期127卷 117-133页

作者： Xiaorui Zhang Jiali Duan Wei Sun Tong Xu Sunil Kumar Jha Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Faculty of Information Technology University of Information Technology and ManagementRzeszow35-225Poland

The cutting simulation of soft tissue is important in virtual *** includes three major challenges in computation:Soft tissue simulation,collision detection,and handling,as well as soft tissue *** order to address the earlier challenges,we propose a virtual cutting system based on the mass-spring *** this system,MSM is utilized to simulate the soft tissue *** stress is introduced to the model for simulating the shrinking effect of soft tissue in ***,a cylinder-based collision detection method is used to supervise the collision between surgical tools and soft ***,we simulate the cutting operation with a three-stage cutting method with swept volume,B´ezier curve,and an algorithm named shortest distance nodes matching *** order to verify the system performance,we carry out three validation experiments on the proposed system:Cutting accuracy experiment,collision detection validation,and practical cutting *** indicate that our system can well perform the shrinking effect of soft tissue in *** system has fast and accurate collision ***,the system can reconstruct smooth incisions vividly.

关键词： Mass-spring model collision algorithm virtual surgery soft tissue simulation

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification

A Novel Attention-based Explainable Deep Learning Framework ...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Gladys W. Muoka Ding Yi Chiagoziem Chima Ukwuoma Moindi Duke Martin Ahmet Arif Aydin Mugahed A. Al-Antari School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China Department of Computer Science Unicaf University Lilongwe Lilongwe Malawi Department of Computer Engineering Faculty of Engineering İnönü University Turkey Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul Korea

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study’s primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics.

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN computer Science 2023年第4期4卷 1-10页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Heart Disease Prediction Using Machine Learning

Heart Disease Prediction Using Machine Learning

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Haram Kamal Bakhtawar Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Muzzamil Mustafa Muhammad Atif Yaqub Hooria Umar Hoor Fatima Usama Nasir Computer Engineering Department Information Technology University Lahore Pakistan Department of Computer Science Bahria University Faculty of Information Technology University of Central Punjab Lahore Department of Artificial Intelligence University of Management & Technology Lahore Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Software Developer Pakistan Bahria University

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The heart plays a pivotal role in the functioning of living organisms, making its diagnosis and prediction of related diseases a matter of utmost importance. Approximately 17.9 million individuals succumb to cardiovascular disease, accounting for 32% of worldwide fatalities. This global concern highlights the importance of early detection, as timely treatment can significantly reduce heart disease-related mortality. Errors in diagnosis can lead to severe consequences, including fatigue or even death. The increasing prevalence of heart-related diseases necessitates the development of precise prediction systems to enhance awareness. Machine learning, a subset of Artificial Intelligence (AI), offers robust tools for predicting various events based on patterns learned from natural occurrences. This research paper evaluates the accuracy of machine learning algorithms, specifically k- nearest neighbor, decision tree, linear regression, and support vector machine (SVM), in predicting heart disease. We utilize the UCI repository dataset for training and testing and employ the Python programming language through the Anaconda Jupyter notebook for implementation and the experimental findings indicate an accuracy rate of 95.7% using the heart disease prediction model, leveraging its extensive libraries and header files to ensure accuracy and precision in our analysis.

关键词： Heart Support vector machines Training Accuracy Machine learning algorithms Predictive models Prediction algorithms Reliability Diseases Testing

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Classification of IncRNA and mRNA using k-mers and random forest 4

Classification of IncRNA and mRNA using k-mers and random fo...

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4th International Conference on Innovative Computing, ICIC 2021

作者： Nadir, Rana M. Mateen, Hafsa Din, Saif U University of Management and Technology Department of Computer Sciences Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan University of Management and Technology Software Engineering Department Lahore Pakistan

ISBN: (纸本)9781665400916

The Long non-coding RNA is involved important biological process in our body but dark part is it also involve in some major deceases like cancer. Very few research is done on long non-coding RNA due to its structural resemblance with mRNA, which is protein coding RNA. A proper distinction model between mRNA and Long non-coding RNA is required to do further research on Long non-coding RNA and its association with ***: First, k-mers with different k values are extracted and directly tested with our model for best selection of k-mers features. Moreover, the k-mers with high accuracy score are combined in one pool and screened with the help of extra tees classifier to identify most relevant features. Furthermore short listed features are given to our random forest (RF) classifier to extract classification results for lncRna and mRna sequences of humans. Finally, over classifier is compared with other machine learning classifiers like State Vector Machine (SVM), Neural Network (NN) and K Nearest Neighbor (KNN). Results proves that our proposed model score highest accuracy, precision, recall and ***: We have established the RF classifier on the bases of combination of 2 and 3 k-mers features, which provides the accuracy of 0.9984. Thus this score is better than Convolutional Neural Network (CNN) based and all previous proposed classification approaches. © 2021 IEEE.

关键词： RNA

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