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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Amat, Kapileshwor Ray Baitha, Aashutosh Adhikarichhetri, Bhabishya Bohara, Neha Bista, Chirayou Shreenidhi, H.S. University Computer Science and Engineering-IOT Faculty of Engineering and Technology Bangalore India

ISBN: (纸本)9798350393354

In this work, an earthquake prediction system utilizing machine learning (ML) techniques and Internet of Things (IoT) technologies is presented, using accelerometer data from the ADXL335 sensor. In order to analyze seismic patterns, the system records multi-axis accelerations. Various machine learning models are then used for predictive analytics. This technology seeks to predict probable seismic events by combining sensor data with sophisticated algorithms, assisting early warning systems for disaster readiness. The ADXL335 accelerometer is the central component of the Earthquake Prediction System described in this work. It records accelerations on the X, Y, and Z axes and converts them into analogue signals for further processing. These data streams are transmitted for feature extraction by utilizing IoT infrastructure, with an emphasis on seismic patterns that may indicate future earthquake events. To evaluate the accelerometer data and produce predicted insights, the system incorporates a variety of machine learning models, such as decision trees and support vector machines. The goal is to support disaster management plans by enabling early detection and warning of seismic activity through this combination of sensor technology and advanced analytics. A wide variety of machine learning models, such as decision trees, support vector machines, and recurrent neural networks, are used to derive actionable insights. These algorithms produce predictive analytics to support catastrophe management methods by carefully analyzing accelerometer data. The ultimate objective is to enable more proactive disaster mitigation planning by facilitating early detection and alerts of seismic activity. This system, which combines advanced analytics with sensor technology, is a critical step in strengthening disaster management systems. Its capacity to predict seismic events may help minimize the effects of earthquakes on impacted areas, help with evacuation plans, and provide timely a

关键词： Internet of things

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ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN

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Journal of Biomedical Physics and engineering 2025年第1期15卷 77-92页

作者： Rahman, Tawfikur Ahommed, Rasel Deb, Nibedita Das, Utpal Kanti Moniruzzaman, Md. Bhuiyan, Md. Alamgir Sultana, Farzana Kausar, Md. Kamruzzaman Department of Electrical and Electronic Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Agriculture International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Computer Science and Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh

Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities along-side normal signals. Material and Methods: This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on di-verse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting ***: The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analy-sis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection. © Journal of Biomedical Physics and engineering.

关键词： Wavelet transforms

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Perceptual Fairness in Image Restoration 38

Perceptual Fairness in Image Restoration

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ohayon, Guy Elad, Michael Michaeli, Tomer Faculty of Computer Science Technion-Israel Institute of Technology Israel Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology Israel

Fairness in image restoration tasks is the desire to treat different sub-groups of images equally well. Existing definitions of fairness in image restoration are highly restrictive. They consider a reconstruction to be a correct outcome for a group (e.g., women) only if it falls within the group's set of ground truth images (e.g., natural images of women);otherwise, it is considered entirely incorrect. Consequently, such definitions are prone to controversy, as errors in image restoration can manifest in various ways. In this work we offer an alternative approach towards fairness in image restoration, by considering the Group Perceptual Index (GPI), which we define as the statistical distance between the distribution of the group's ground truth images and the distribution of their reconstructions. We assess the fairness of an algorithm by comparing the GPI of different groups, and say that it achieves perfect Perceptual Fairness (PF) if the GPIs of all groups are identical. We motivate and theoretically study our new notion of fairness, draw its connection to previous ones, and demonstrate its utility on state-of-the-art face image restoration algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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A Case Study for Language-Free Keyword Extraction with Statistical and Graph-Based Features 6

A Case Study for Language-Free Keyword Extraction with Stati...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Kiliç, Hüma Abibullayeva, Aiman Cetin, Aydin Computer Engineering Department Faculty of Technology Gazi University Ankara Turkey Computer Engineering Department Faculty of Engineering Akhmet Yassawi University Turkistan Kazakhstan

ISBN: (纸本)9798350373875

For text mining applications, keyword extraction is a popular problem to solve. The text mining applications as indexing, summarization and topic tracking uses keyword extraction models as baseline. There are many sequence labeling models proposed in the literature. The performance results, however, did not meet expectations. In this paper, we use many selective statistical and graph-based features to solve the keyword extraction problem. We calculated many selective features for each considerable word in input text. We used Random Forest token classification model. In addition to widely used 500N-KPCrowd dataset in the literature, we trained and tested publicly available KazakhNews and RussianNews datasets to extend language domain. Moreover, we collected two news dataset in Chinese and Arabic languages namely ChineseNews and ArabianNews. We also used a Turkish abstract dataset DergiParkTR for comparison. The performance of the graph-based and statistical features were tested with Random forest algorithm. We have obtained 0.680, 0.706 and 0.896 Fl-scores for DergiParkTR, 500N-KPCrowd and RussianNews datasets respectively. We attained the best performance of 0.978 for KazakhNews using graph-based features and 0.999 for ChineseNews datasets with all statistical features. © 2024 IEEE.

关键词： Graphic methods

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A Review of Evaluation Metrics Algorithmic Used in Scholarly Research Recommender System 4

A Review of Evaluation Metrics Algorithmic Used in Scholarly...

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4th International Multidisciplinary Information technology and engineering Conference, IMITEC 2024

作者： Ovono, Gerald Zuva, Travos Otunniyi, Temidayo Faculty of Applied and Computer Sciences Department of Computer South Africa VaalUniversity of Technology Faculty of Engineering and Technology Department of Process Control and Computer Systems South Africa

ISBN: (纸本)9798350387988

The exponential growth of academic publications has made scholarly research recommender systems indispensable tools for researchers. These systems rely on diverse evaluation metrics to assess their effectiveness and relevance. This paper provides a comprehensive review and analysis of these metrics, categorizing them into accuracy, ranking, diversity, user-centric, coverage, temporal, and computational metrics. By employing a mixed-methods approach, combining systematic literature review and empirical analysis, we identify the prevalence and application of these metrics across various scholarly domains such as literature, conferences, and reviewers. Our findings highlight the widespread use of traditional metrics like precision, recall, and Fl-score, while emphasizing the growing importance of novel metrics such as unexpectedness and serendipity, especially in literature and conference recommendations. We also underscore the domain-specific nature of metric selection, with user-centric evaluations more prominent in literature recommendations, and ranking-focused metrics dominant in conference and reviewer recommendations. Furthermore, the study reveals a significant gap in the use of temporal and computational metrics, suggesting key areas for future research. This review offers valuable insights for researchers and practitioners, guiding them in metric selection and highlighting potential directions for enhancing the effectiveness, adaptability, and user satisfaction of scholarly recommender systems. © 2024 IEEE.

关键词： Algorithms Evaluation Metrics Novelty Recommender Systems Scholarly Research Serendipity

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Bidirectional attentional mechanism for Arabic chatbot

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International Journal of Information technology (Singapore) 2024年第5期16卷 3109-3120页

作者： Youness, Farida Elshenawy, Ayman Makour, Mohamed Ashraf Systems and Computer Engineering Department Faculty of Engineering Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

A chatbot is an intelligent agent that developed based on Natural language processing (NLP) to interact with people in a natural language. The development of multiple deep NLP models has allowed for the creation of a vector representation of words and sentences that captures meaning in context. In this paper, a generative Arabic chatbot is trained with the Sequence to Sequence (Seq2Seq) model based on encoder-decoder architecture. It utilizes both the bi-directional encoder types of Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM). The chatbot was trained using preprocessed dataset of 12 k conversations that were translated from English to Arabic. An attention layer was provided to the RNNs to focus on the important data between the input and output sequences. The obtained results showed that the proposed chatbot performed well with long sentences and elicited positive responses. In addition to that BiLSTM with attention performs better than BiGRU without attention. To assess the model, the Bilingual Evaluation Understudy (BLEU) Score is employed. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Arabic chatbot Attention mechanism Deep learning RNN

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Impact of Different Integration Times on Distributions of Rain Rates for Predictions of Rain Attenuation

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Progress in Electromagnetics Research C 2025年 154卷 229-238页

作者： Hassan, Mohammad Rofiqul Rafiqul, Islam Md. Habaebi, Mohamed Hadi Suriza, Ahmad Zabidi Badron, Khairayu Budalal, Asma Ali Mahfuz, Md. Mahmudul Hasan Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur53100 Malaysia College of Electrical and Electronics Technology Benghazi Libya Department of Electrical and Computer Engineering Concordia University Canada

All wireless communication systems are moving towards higher and higher frequencies day by day which are severely attenuated by rains in outdoor environment. To design a reliable RF system, an accurate prediction method of rain attenuation is established and used globally based on local rain intensity measurement. Required rain intensity used for attenuation prediction is generally measured at a point with 1-min integration time or converted from higher integration time to 1-min. Recent measurements of rain intensity with a 10-second integration time indicate that intensity is not uniform over a 1-minute duration. Consequently, the statistics of rain intensity distribution and attenuation predictions are influenced by measurements with integration times shorter than 1 minute. It has been established that an integration time of 0.01% provides the optimal fit for actual rain rate data. This paper presents the rain intensity distributions from data measured with 2-min, 1-min, 30-sec, 20-sec, and 10-sec integration times, and it has impact on rain rate distributions as well as rain attenuation predictions. © 2025, Electromagnetics Academy. All rights reserved.

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SKGDC: Effective Segmentation Based Deep Learning Methodology for Banana Leaf, Fruit, and Stem Disease Prediction

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SN computer Science 2024年第6期5卷 698页

作者： Shetty, Sahana Mahesh, T.R. Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India

In agriculture, detecting plant diseases is crucial for optimal plant growth. Initially, input images are collected from three datasets: banana leaf spot diseases (BananaLSD) dataset, banana leaf dataset, and PSFD-Musa dataset. These collected images undergo filtering using the upgraded self-guided filtering (Up-SGF) model, chosen for its ability to preserve sharp edges effectively with strong smoothing, enhancing edge perception. Additionally, the improved Wiener convolution filter (IWCF) is applied to enhance image quality by reducing noise while preserving features. Following pre-processing, the images are segmented using spatial kernelized gravity-based density clustering (SKGDC) to identify disease-affected regions. Kaze, Blob, and histogram of oriented gradient (HOG) methods are utilized to extract relevant features: Kaze for scale-invariant key points, Blob for identifying and characterizing areas with shared color or intensity characteristics, and HOG for detecting edges and contours. These extracted features are then fused together using convolutional pyramid fusion (CPF). Finally, the hybrid channel attention-based YOLOv8 (HCA-YOLOv8) model is employed to predict diseases in banana fruit, stems, and leaves. Channel attention is incorporated into the backbone network architecture, and an improved deformable convolution replaces the normal convolution. Hyper parameters of the network model can be optimized using the improved chimp optimization (ICO) algorithm. Simulation results are obtained using the Python programming language. The proposed model attained 98.12% of accuracy in BananaLSD dataset, 99% of accuracy in banana leaf dataset, and 98.5% of accuracy in PSFD-Musa dataset. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Banana leaf disease Chimp optimization Clustering Pyramid fusion Self-guided filtering Wiener filter YOLOv8

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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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Multispectral point cloud superpoint segmentation

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Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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