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Railway Track Fault Detection Using Mel Frequency Cepstral Coefficient from Acoustic Data using Chi Square

Chinese Journal of Electronics 2023年

作者： Furqan Rustam Abid Ishaq Muhammad Shadab Alam Hashmi Hafeez-Ur-Rehman Siddiqui Imran Ashraf School of Computer Science University College Dublin Faculty of Computer Science and Information Technology Khawaja Fareed University of Engineering and Information Technology Department of Information and Communication Engineering Yeungnam University

Spatial, temporal, and weather elements like ballast, loose nuts, misalignment, and cracks due to rain, snow, and earthquakes may lead to railway accidents and cause human and financial loss. Manual inspection is erroneous, labor-intensive, and *** automatic inspection provides a fast, reliable, and unbiased solution in this regard, however, ensuring high accuracy for fault detection is challenging due to the lack of public datasets, noisy data, high computer processing requirements, and inefficient models. This study presents an approach that uses Mel frequency cepstral coefficient features from the acoustic data. The dataset gathered using a customized railway cart from our previous research is used for experiments. The focus of the study is to increase the fault detection performance using selective features from the acoustic data. This study employs Chisquare(Chi2) for the selection of important features and involves performance analysis of machine learning and deep learning models using selected features. Experimental results suggest that using 60 features, 40 original features, and 20 Chi2 features, produces optimal results both regarding accuracy and computational complexity. A 100%accuracy can be obtained using the proposed approach with machine learning models. Moreover, this performance is significantly better than existing approaches.

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

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computers, Materials & Continua 2023年第7期76卷 573-589页

作者： Halah Hasan Mahmoud Marwan Kadhim Mohammed Al-Shammari Gehad Abdullah Amran Elsayed Tag eldin Ala R.Alareqi Nivin A.Ghamry Ehaa A.Lnajjar Esmail Almosharea Computer Center University of BaghdadBaghdad6751Iraq Department of Management Science and Engineering Dalian University of TechnologyLiaoningDalian116024China Department of Information Technology Faculty of Computer Sciences and Information Technology AL Razi UniversitySana’aYemen Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Faculty of Engineering Mechanics Dalian University of TechnologyLiaoningDalian116024China Faculty of Computers and Artificial Intelligence Cairo UniversityGizaEgypt Department of Business Administration Dalian University of TechnologyLiaoningDalian116024China College of Software Engineering Dalian University of TechnologyChina

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial *** of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine *** study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity *** Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity *** research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical *** results suggest that detecting the submarine early increases the likelihood of averting a *** dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access *** communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the *** swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its ***,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other *** dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

关键词： Unmanned vehicles unmanned aerial vehicles unmanned underwater vehicles high altitude unmanned aerial vehicles anti-submarine warfare re-fragmentation dragonfly algorithm

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An Electricity Usage Monitoring System Using Internet of Things (IoT) for Small Business Stalls 1

An Electricity Usage Monitoring System Using Internet of Thi...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Aisuwarya, Ratna Yolanda, Desta Talmera, Annisa Triana Universitas Andalas Faculty of Information Technology Computer Engineering Department Padang Indonesia

ISBN: (纸本)9798350306484

In this proposed research, an IoT -based electrical power monitoring system is developed to monitor small business stalls by adding voltage and output sensors. The system displays electrical parameters and estimated costs on a web interface and provides notifications through an instant messaging service for the stall owners. The study aims to display the results of monitoring electricity usage to users via a web server and provide notifications if there is excess usage based on the rental rules for the stall tenants. Based on the implementation design and system testing, the system can collect data on the use of electrical power at the electrical terminal of a stall using the current sensor SCT013 and the voltage sensor ZMPT101B with an average error of 0.69% - 1.41 %. The system also successfully performs real-time power and cost calculations and shows the information on the website. Additionally, a notification in the form of an excess usage warning is sent via Telegram Bot if there is an excess of 1105 watts of electricity. © 2023 IEEE.

关键词： Microcontrollers

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Analyzing processing time and load factor: 5-node mix network with ElGamal encryption and XOR shuffling

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International Journal of information technology (Singapore) 2024年第7期16卷 4589-4597页

作者： Naikoo, Fayaz Ahmad Ahmad, Khaleel Ahmad, Khairol Amali Bin Department of Computer Science and Information Technology Maulana Azad National Urdu University Faculty of Engineering National Defence University of Malaysia

To provide anonymous communication, this paper proposes the implementation of a 5-node mix network using ElGamal encryption and XOR Shuffling. An algorithm for a 5-node mix network with XOR shuffling has been presented. The study carefully examines the processing time at each node and determines the load factor linked to each node using MATLAB simulation. By utilizing a shuffling technique to break the connection between nodes, the mix network successfully achieves communication anonymity. The effectiveness of this approach in maintaining anonymity and controlling communication load is persuasively shown by the data that are presented. The knowledge of mix networks and their function in protecting user privacy in digital communication systems is enhanced by this research. This study establishes a platform for future research and optimization of mix network deployments by offering empirical proof of its efficacy. These developments could improve digital communication networks’ security and privacy in a number of situations. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anonymous communication ElGamal encryption Mix network XOR shuffling

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Forecasting and managing urban futures: machine learning models and optimization of urban expansion

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Asian Journal of Civil engineering 2024年第6期25卷 4673-4682页

作者： Abid, Mohammed Talib Aljarrah, Njood Shraa, Tamer Alghananim, Haneen Marouf Department of Architecture College of Engineering Al-Nahrain University Baghdah Iraq Computer Sciences Faculty of Information Technology and Computer Sciences Ajloun National University Ajloun Jordan Faculty of Science and Information Technology Cyber Security Irbid National University Irbid Jordan Faculty of Engineering Architecture Department Ain Shams University Cairo Egypt

This paper presents a novel method for predicting and controlling urban growth by combining convolutional neural networks (CNN) with Spider Monkey Optimization (SMO) to efficiently use their combined capabilities. This strategy utilizes sophisticated machine learning algorithms to forecast urban development patterns, while optimization technologies enhance these forecasts by including sustainable urban design ideas. Integration plays a crucial role in tackling the difficulties presented by fast urbanization, including issues related to land use, resource administration, and environmental sustainability in the fields of civil and architectural engineering. The study employs large datasets to assess the efficacy of CNN models in detecting urban expansion patterns and enhances these models with optimization scenarios encompassing land use, traffic flow, electricity efficiency, water resources, and waste management, all with the goal of enhancing urban sustainability. The findings demonstrate a significant improvement in the precision of predictions and the ability to maintain positive results, highlighting the crucial role of integrating machine learning and optimization in urban planning methodologies. This method not only provides urban planners and politicians with strong and effective tools for making decisions but also creates opportunities for implementing creative and sustainable solutions for urban growth. The results provide a substantial contribution to the existing urban planning literature by demonstrating the actual use of these technologies in regulating urban expansion. This will stimulate more study in this rapidly evolving subject. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks Machine learning Optimization techniques Spider monkey optimization Sustainable urban planning Urban expansion

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Indoor Positioning System Based on BSSID on Office Wi-Fi Network

Indoor Positioning System Based on BSSID on Office Wi-Fi Net...

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2023 International Conference on computer Science, information technology and engineering, ICCoSITE 2023

作者： Aisuwarya, Ratna Ferdian, Rian Yulianti, Indah Hestina Universitas Andalas Faculty of Information Technology Computer Engineering Department Padang Indonesia

ISBN: (纸本)9798350320954

Indoor positioning system determine the position of objects in a closed room or story building. This system can determine not only the position but also the orientation and direction of a person's movement. This research uses Wi-Fi (Wireless Fidelity) a network technology that utilizes wireless technology and can work at frequencies of 2.4 GHz and 5.8 GHz. The aims to produce a system that can monitor the presence of employees. This makes the supervisor's work more effective because it can unify based on the information displayed on the android application. Based on observation and testing that has been done, the proposed system can display BSSID as MAC address and SSID from user data by authentication by admin. The system can monitor the user's position in the faculty office area with the application of the K-Nearest Neighbor (KNN) algorithm and the calculation of Received Signal Strength Indication (RSSI) and using the Fingerprinting method with an average Euclidean distance accuracy of 2.37 meters and able to display the user's position with a 100% success percentage. Then, the system is able to read the value of RSSI with 2.08% error. © 2023 IEEE.

关键词： Indoor positioning systems

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Optimal allocation and sizing of shunt active power filter in radial distribution systems using particle swarm optimization 20

Optimal allocation and sizing of shunt active power filter i...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Primorac, Mario Klaic, Zvonimir Znidarec, Matej Adric, Adric Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9781665463973

The reduction of harmonics in terms of improving power quality is usually achieved using passive or active filters. Due to their advantages over passive filters, active power filters (APF) are receiving increased attention from researchers focusing on optimal sizing and allocation of APFs in the network with the aim of reducing total harmonic distortion. This paper proposes methodology for determining optimal size, location and settings of APF while observing total harmonic distortion of voltage (THDV). It emphasizes the importance of controlling individual harmonic distortion (IHD). Furthermore, due to the different nature of the injection of harmonics into the radial distribution system (RDS), proposed methodology takes into account the load variation as well as changes in generation of the PV systems as distributed generation (DG). Optimal settings of APF is determined using particle swarm optimization (PSO) method. Evaluation of the proposed method is performed on three different study cases. Results show that variation of generation and consumption have significant impact on sizing, allocation and settings of the APFs. Furthermore, results pointed out that in apart from observing THDV, it is also necessary to observe IHD. © 2023 IEEE.

关键词： Power quality

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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