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

作者： Rana, Md. Sohel Azad, Tamanna Mondal, Saikat Nahid, Mizanur Rahman Ahmed, Md. Eftiar Shakil, Md. Omar Faruq Biswas, Anup Nirjhar Ahad, Md Abdul Moniruzzaman, M. Northern University of Business and Technology Khulna Department of Electrical and Electronic Engineering Khulna9100 Bangladesh Bangladesh Army University of Science and Technology Department of Electrical and Electronic Engineering Saidpur Bangladesh University of Information Technology and Sciences Department of Electronics and Communication Engineering Dhaka Bangladesh Washington University of Science and Technology Department of Information Technology VA United States

ISBN: (纸本)9798350370249

In this study, a microstrip patch antenna operating at a frequency of 2.45 GHz was built, simulated, and examined. The primary objective during the initial design of this antenna was to cater to S-band wireless communication applications. The antenna base was constructed using Fr-4 (lossy) material, which has a dielectric permittivity of 4.3 and a loss tangent of 0.025. The completed antenna was modeled using computer Simulation Technology, and the simulation results were seen using Origin Pro. Following the simulation, the antennas exhibited a directivity gain of 5.6 dBi, a return loss of -39.80 dB, and a voltage standing wave ratio (VSWR) of 1.0206. The primary objective of this study was to enhance the overall efficiency of the antenna by reducing its return loss and boosting its directivity gain, hence achieving a reduced VSWR. The objective of formulating this comprehensive technique was to enhance the performance of the antenna, which will be utilized in wireless applications. © 2024 IEEE.

关键词： Slot antennas

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Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

Towards Efficient Deep Hashing Retrieval: Condensing Your Da...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Tao Zhang, Jie Liu, Huashan Wang, Zhijie Pang, Shengyuan College of Information Science and Technology Donghua University Shanghai China Department of Computer Science ETH Zurich Zurich Switzerland College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

Deep hashing retrieval has gained widespread use in big data retrieval due to its robust feature extraction and efficient hashing process. However, training advanced deep hashing models has become more expensive due to complex optimizations and large datasets. Coreset selection and Dataset Condensation lower overall training costs by reducing the volume of training data without significantly compromising model accuracy for classification task. In this paper, we explore the effect of mainstream dataset condensation methods for deep hashing retrieval and propose IEM (information-intensive feature-Embedding Matching), which is centered on distribution matching and incorporates model and data augmentation techniques to further enhance the feature of hashing space. Extensive experiments demonstrate the superior performance and efficiency of our approach. © 2025 IEEE.

关键词： Dataset Condensation Deep Hashing Retrieval

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Investigating the Determinants of Compliance Intention in Behavior Change Support Systems 29

Investigating the Determinants of Compliance Intention in Be...

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29th Annual Americas Conference on information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Ekpezu, Akon Obu Oinas-Kukkonen, Harri Wiafe, Isaac Faculty of Information Technology and Electrical Engineering Oulu Advanced Research on Service and Information Systems University of Oulu Finland Department of Computer Science University of Ghana Ghana

ISBN: (纸本)9781713893592

The effectiveness of behavior change support systems (BCSS) in promoting health and well-being is unflinching. However, its long-term effectiveness is hindered by non-compliance. Research in BCSS that focuses on compliance is scarce, particularly the determinants of compliance intention are not well established. To address this gap, this study investigated the determinants of compliance intention in BCSS by extending the persuasive system design model to include competence, effectiveness, enjoyment, and persuasiveness. By surveying a sample of prospective BCSS users (N=234), partial least squares structural equation modeling was used to evaluate the proposed model. The findings suggest that the prospective users’ perceptions of primary task support and dialogue support, as well as social support and credibility support significantly influenced their perceptions of self-competence and system effectiveness respectively. These perceptions influenced enjoyment, then persuasiveness, and finally compliance intention. Future studies will validate these findings on the compliance behavior of BCSS. © 2023 29th Annual Americas Conference on information Systems, AMCIS 2023. All rights reserved.

关键词： Behavior Change Support Systems Compliance Intention Persuasive Systems Design Persuasive Technology Physical Activity

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Deep Learning PNN Based Fault Monitoring System for Three Phase Industrial Drive System 7

Deep Learning PNN Based Fault Monitoring System for Three Ph...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chempavathy, B. Raju, K. David Mani, P.K. Vijayalakshmi, S. Janaki, N. Karthikeyan, D. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Malla Reddy Engineering College for Women Department of Electrical and Electronics Engineering Hyderabad India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electrical and Electronics Engineering Chennai India K. Ramakrishnan College of Technology Department of Electrical and Electronics Engineering Tiruchirappalli Tamil Nadu Samayapuram India Technology and Advanced Studies Vels Institute of Science Department of Electrical and Electronics Engineering Tamilnadu Chennai India SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350372816

Asynchronous motors are widely used in a variety of sectors. Even though induction motors are strong and dependable, they are susceptible to a variety of problems. Faults in induction motors can result in horrible events such as operator injuries, manufacturing disruption, and raw material loss. As a result, defect identification has become more crucial in induction motor management. Among the different problems that can occur in the motor, bearing failure is a severe issue that can cause catastrophic damage to the machine if not detected at an early stage of the fault. As consequently, the functioning of bushings in induction machinery must be checked on a regular basis. This research proposes a novel method for analyzing speed using discrete wavelet transform (DWT) and identifying bearing difficulties using Probabilistic Neural Network (PNN) optimized by improved salp swarm algorithm. The stator electricity is examined and classified when an induction motor is operating under varied loading conditions with good and troublesome bearings. Scale-Invariant Feature Transform (SIFT) is a feature extraction method that condenses visual content into a set of points that may be used to find similar patterns in other images. The proposed PNN classifier can classify different types of bearing faults, and the empirical findings validate the created approach. PNN-based motor bearing recognizing defects and diagnosis outperforms SVM and ANN classifiers in this regard. The Shortest path accuracy of the PNN Classifier is 98.5% respectively. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Detection of Customer Satisfaction in In-Person Telecommunication Services Using Automated Facial Image Analysis with Artificial Intelligence 11

Detection of Customer Satisfaction in In-Person Telecommunic...

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11th International Symposium on Telecommunication, IST 2024

作者： Gangan, Hadiseh Arab Rohani, Mehrdad Hosseini, Seyed Ali Mansouri, Alireza Birjand University Department of Electrical and Computer Engineering Birjand Iran Information Technology Faculty & ICT Research Institute Tehran Iran

ISBN: (纸本)9798350356250

In the present era, improving customer experience and enhancing services in the telecommunications industry are of paramount importance. One of the fundamental criteria for evaluating customer experience is their level of satisfaction with services at physical sales points. This paper focuses on the automated analysis of facial images using artificial intelligence to determine the satisfaction level of inperson visitors to telecommunications service booths. Advanced technologies such as transfer learning and the use of the EfficientNet network architecture have been employed to provide a more accurate and efficient method for facial emotion analysis. The results of this study demonstrate that the proposed method significantly improves the accuracy and efficiency of emotion detection and, consequently, the satisfaction or dissatisfaction of visitors in both implementation stages. This research not only contributes to the improvement of service processes and the enhancement of customer experience but also confirms the superiority and reliability of this method by comparing the results with previous approaches. These findings can assist telecommunications businesses in improving services and increasing customer satisfaction, guiding them towards optimized performance and success. This study is introduced as a solution for improving the customer experience process during in-person visits to telecommunications service offices, which has extensive applicability in enhancing communication systems, increasing employee productivity, improving service delivery supervision and management, and enhancing responsiveness and service quality at telecommunications service counters. Additionally, it provides for increased customer satisfaction and loyalty, optimization of internal company processes, improved marketing strategies, and more accurate assessment of customer experience status. © 2024 IEEE.

关键词： Emotion Recognition

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Neural Network-Based Analysis of Mammography Images for Identifying Breast Cancer Histological Subtypes

Neural Network-Based Analysis of Mammography Images for Iden...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Alqaaf, Muhammad Technology, Ahmad Kamal Nasution Graduate School Of science And Supriyanti, Retno Asaduzzaman, Abu Ono, Naoaki Altaf-Ul-Amin, Md. Kanaya, Shigehiko Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Jenderal Soedirman University Department of Electrical Engineering Banyumas Indonesia Wichita State University Department of Electrical and Computer Engineering Wichita United States

ISBN: (纸本)9798350391213

Breast cancer (BC) is a complex disease with multiple histological subtypes that exhibits distinct biological and clinical characteristics. Accurate identification of these subtypes is crucial for the implementation of personalized treatment strategies and the improvement of patient outcomes. This study aimed to leverage the potential of neural networks and mammography data to identify specific histological subtypes of BC, with a focus on distinguishing between in situ and invasive carcinoma. We used the Digital Database for Screening Mammography (DDSM) and its curated subset, CBIS-DDSM, which provides mammography images of normal and malignant cases with verified pathological information. A convolutional neural network (CNN) architecture was designed to extract relevant features from mammography images. The model was trained using data augmentation techniques to enhance diversity and mitigate overfitting, resulting in training and validation accuracies ranging from 0.98 to 1.0. The features extracted by the trained model were then used for clustering analysis using Kmeans on PCA (KM-PCA), which identified nine well-separated clusters with minimal overlap. The clustering results were validated using a subset of mammography images with confirmed invasive carcinoma types from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Two validation approaches were incorporated to strengthen the hypothesis and further validate the results. Overall, this study demonstrates the potential of combining neural networks and mammography data to accurately identify BC histological subtypes, which could pave the way for personalized and effective treatment strategies. © 2024 IEEE.

关键词： Lung cancer

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Advances in Medical Image Processing for Liver Tumour Recognition: A Comprehensive Survey 2

Advances in Medical Image Processing for Liver Tumour Recogn...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Deepa, B. Marimuthu, C.N. Geetha, S. Maheswari, S. Anna University Faculty of Information and Communication Engineering Chennai600025 India Nandha Engineering College Department of Electronics and Communication Engineering Erode638052 India Nandha Engineering College Department of Computer Science and Engineering Erode638052 India

ISBN: (纸本)9798350387933

The last several decades have seen a lot of activity in the medical image processing domain especially when it comes to the segmentation of liver and liver tumours. The survival rate of liver cancer is rather low when compared to some other types of cancer. Nevertheless, early detection greatly improves the prognosis of liver cancer patients. As a consequence, multiple researchers have developed automated deep-learning (DL) systems for forecasting the development of cancer cells using various medical imaging modalities. A summary of some previously published studies on liver cancer diagnosis is currently lacking in review publications. But new mechanisms and architecture in the prognosis of liver cancer were beyond the possibility of these investigations. This review emphases on the topologies of DL for liver cancer diagnosis. The survey that follows examines the datasets used, describes existing DL-based designs, assesses the benefits and drawbacks of earlier studies, and discusses image processing techniques. Furthermore, a thorough examination of several imaging modalities, performance metrics and results, challenges and future research goals are provided. The comparison study of different methods reveals that HFCNN has the highest accuracy, at 97.22%. © 2024 IEEE.

关键词： Medical imaging

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Unravelling stress levels in continuous speech through optimal feature selection and deep learning 2

Unravelling stress levels in continuous speech through optim...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Duvvuri, Kavya Kanisettypalli, Harshitha Masabattula, Teja Nikhil Vekkot, Susmitha Gupta, Deepa Zakariah, Mohammed Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics & Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Stress is a psychological or emotional strain that occurs due to adverse experiences in human life. This paper showcases the application of deep learning in detecting stress levels in continuous audio signals in the Distress Analysis Interview Corpus Wizard of Oz (DAIC-WOZ) database. The features that have been experimented with are Gammatone Frequency Cepstral Coefficients (GFCC), Log Filter Bank (Log-Filter Bank), Mel Frequency Cepstral Coefficients (MFCC), chroma, and Linear Predictive Coding (LPC). Five deep learning models were evaluated: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Bidirectional LSTM (Bi-LSTM), k-fold CNN with the k value as 5, and a fusion model of CNN, LSTM, and attention. Upon evaluating the performance metrics of all the models, it is concluded that the k-fold CNN model with k as 5 performs well with continuous audio signals. The model has achieved an accuracy of 80% when it is trained on MFCC, GFCC, and Log-F Bank features which are observed to be the optimal features in the stress analysis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Feature extraction

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Effect of Stopwords and Stemming Techniques in Urdu IR

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SN computer science 2023年第5期4卷 547页

作者： Sahu, Siba Sankar Dutta, Debrup Pal, Sukomal Rasheed, Imran Department of Computer Science and Engineering Indian Institute of Technology (BHU) Uttar Pradesh Varanasi 221005 India Department of Computer Science and Engineering International Institute of Information Technology Odisha Bhubaneswar 751003 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur 522502 India

This paper explores and evaluates the effect of different stopword removal and stemming techniques in Urdu IR. The issues are examined from four viewpoints. Is there any performance difference between non-corpus-based and corpus-based stopword removal in Urdu IR? Can corpus-based stopword lists improve performance in Urdu IR? Among the different corpus-based stopword lists, which stopword list gives the best performance in the IR domain? Does language-independent stemmer improve the performance of Urdu IR? Which is the best stemmer for Urdu IR? Whether to use clustering-based (YASS) or fast corpus-based (FCB) or co-occurrence-based (SNS) or graph-based (GRAS) or Trunc-n-based indexing? It was observed that the shorter length of a corpus-based stopword list outperforms a larger length of a non-corpus-based stopword list in the IR domain. Among the different corpus-based stopword lists, Zipf’s law-based stopword list (nidf approach) provides the best performance and improves a mean average precision (MAP) score of 4.9% compared to baseline approaches. During stemming evaluation, we observed that the language-independent stemming techniques improve retrieval performance in Urdu IR. Among the different stemming techniques, the FCB V-1-based stemmer performs best and improves a MAP score of 1.41% compared to the no-stemming approach. The trunc-n-based indexing strategy provides comparable performance to the language-independent stemming approach. In both the stopword removal and stemming strategies, the BB2 retrieval model outperforms other models in the IR domain. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Evaluation information retrieval Stemming Stopword Urdu

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An Efficient Autoscaling Cross-Browser Testing Cloud Platform based on Selenium Grid, Kubernetes and KEDA

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Journal of information science and engineering 2023年第5期39卷 1061-1077页

作者： Lin, Chia-Yu Lee, Shin-Jie Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Computer and Network Center National Cheng Kung University Tainan701 Taiwan

Cross-browser testing not only is one of the most common non-functional testing methods in the field of software testing, but also the testing method that requires large amounts of resources, in terms of hardware and time. Basically, based on Selenium Grid, Kubernetes and KEDA auto-scaler, a cross-browser testing platform can be quickly built. However, through our empirical study of this style of platform, we observed three signifi-cant problems in terms of its reliability and efficiency: the Health-Check problem, the Ses-sion-Queue problem, and the Cooldown problem. This paper suggests solutions to these problems. The experimental result shows a 2.27 times improvement in reliability and a decrease in execution time for 61.5%. Moreover, the overall execution time is also 54.2% less comparing with Selenium's Dynamic Grid. © 2023 Institute of information science. All rights reserved.

关键词： Selenium

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