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2nd International Conference on information technology Research and Innovation, ICITRI 2023

作者： Yanto, Musli Yunus, Yuhandri Tajuddin, Muhammad Universitas Putra Indonesia YPTK Information Technology Faculty of Computer Science Padang Indonesia Universitas Bumi Gora Faculty of Computer Science Mataram Indonesia

ISBN: (纸本)9798350324945

Detection of gallstone disease on medical images plays a very active role in early screening. However, medical imaging still cannot provide optimal diagnostic results. This is because medical images have deficiencies in the identification process. With these deficiencies, it is necessary to process image processing to overcome existing problems. This study aims to identify gallstone disease using an image segmentation process. The segmentation process is carried out by adopting the performance of the Multistage Segmentation Algorithm (MSA) based on an unsupervised approach and image morphology techniques. The performance of this MSA is expected to obtain the results of identifying images of objects containing gallstones. The data set for this study consisted of 1678 images derived from the examination results of 17 gallstone patients based on medical imagery resonance cholangiopancreatography (MRCP). Based on the experiments conducted, MSA can present precise and accurate identification results. These results were obtained based on the performance of the Cluster-Based Segmentation (CBS) process and morphological image operations can to detected gallstone objects. The overall results of the segmentation process provide fairly good test results with an accuracy value of 90% With these results, this study contributes to optimizing the process of diagnosing gallstone disease patients. © 2023 IEEE.

关键词： Medical imaging

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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Text-Independent Algorithm for Source Printer Identification Based on Ensemble Learning

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computers, Materials & Continua 2022年第10期73卷 1417-1436页

作者： Naglaa F.El Abady Mohamed Taha Hala H.Zayed Department of Computer Science Faculty of Computers and Artificial IntelligenceBenha University13518Egypt School of Information Technology and Computer Science(ITCS) Nile University12677Egypt

Because of the widespread availability of low-cost printers and scanners,document forgery has become extremely *** or signatures are used to protect important papers such as certificates,passports,and identification *** the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s *** printer identification(SPI)has become increasingly popular for identifying frauds in printed *** paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes.A dataset of 1200 papers from 20 distinct(13)laser and(7)inkjet printers achieved significant identification results.A proposed algorithm based on global features such as the Histogram of Oriented Gradient(HOG)and local features such as Local Binary Pattern(LBP)descriptors has been proposed for printer *** classification,Decision Trees(DT),k-Nearest Neighbors(k-NN),Random Forests,Aggregate bootstrapping(bagging),Adaptive-boosting(boosting),Support Vector Machine(SVM),and mixtures of these classifiers have been *** proposed algorithm can accurately classify the questioned documents into their appropriate printer *** adaptive boosting classifier attained a 96%*** proposed algorithm is compared to four recently published algorithms that used the same dataset and gives better classification accuracy.

关键词： Document forensics source printer identification(SPI) HOG LBP principal component analysis(PCA) bagging AdaBoost

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Abnormal Events Detection in Surveillance Environments using Machine Learning

Abnormal Events Detection in Surveillance Environments using...

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2024 International Conference on science, technology, Engineering and Management, ICSTEM 2024

作者： Priya, D. Divya Theam, Lim Jit Vinisha, J. Krishnan, Kesavan Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Department of Computer Science Engineering MLR Institue of Technology Hyderabad India Kampar Perak Malaysia Department of Information Technology St.Joseph's Institute of Technology Tamilnadu India Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350376913

The ability of intelligent CCTV systems to detect unusual events, detect human behaviour and recognize objects is essential. This system enables the detection of anomalous events in the environment, anomalous human behaviour and the level of alertness in the environment. The system uses machine learning and machine vision capabilities to detect and identify specific anomalies in CCTV video feeds. This system often uses frames through frame processing, and supervised learning is often used as a training method. However, unsupervised and semi-supervised learning replace supervised learning in the system learning process due to the large variety of anomalies and the impossibility of predicting and preparing for all of them. This system can decrease or do away with the amount of labour-intensive human work needed to manually spot anomalies in the live CCTV feed and provide alerts. The initial monitoring of unusual events and low-quality archiving of typical scenarios also increases the efficiency of the system’s storage. In addition, this system provides an extension for developing a distributed anomaly classification system so that only abnormal events are directed to different unique systems for categorization. Regular recordings, on the other hand, will be archived with low quality. Furthermore, this approach offers a distributed anomaly classification system development path. © 2024 IEEE.

关键词： Anomaly detection

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The Impact of Domain Name Server(DNS)over Hypertext Transfer Protocol Secure(HTTPS)on Cyber Security:Limitations,Challenges,and Detection Techniques

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computers, Materials & Continua 2024年第9期80卷 4513-4542页

作者： Muhammad Dawood Shanshan Tu Chuangbai Xiao Muhammad Haris Hisham Alasmary Muhammad Waqas Sadaqat Ur Rehman Faculty of Information Technology Beijing University of TechnologyBeijing100124China National Center of Robotics and Automation University of Engineering and TechnologyPeshawar25000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Engineering Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUnited Kingdom School of Engineering Edith Cowan UniversityPerthWA 6027Australia School of Computer Science and Engineering University of SalfordManchesterM54BRUnited Kingdom

The DNS over HTTPS(Hypertext Transfer Protocol Secure)(DoH)is a new technology that encrypts DNS traffic,enhancing the privacy and security of ***,the adoption of DoH is still facing several research challenges,such as ensuring security,compatibility,standardization,performance,privacy,and increasing user *** significantly impacts network security,including better end-user privacy and security,challenges for network security professionals,increasing usage of encrypted malware communication,and difficulty adapting DNS-based security ***,it is important to understand the impact of DoH on network security and develop newprivacy-preserving techniques to allowthe analysis of DoH traffic without compromising user *** paper provides an in-depth analysis of the effects of DoH on *** discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security ***,this paper highlights the need for continued research and development to ensure the effectiveness of DoH as a tool for improving privacy and security.

关键词： DNS DNS over HTTPS cybersecurity machine learning

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Intelligent Eczema management and awareness system for Saudi Arabia System Architecture 3

Intelligent Eczema management and awareness system for Saudi...

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3rd International Conference on Computing and information technology, ICCIT 2023

作者： Alotaibi, Mohammed University of Tabuk Faculty of Computer Science and Information Technology Tabuk47191 Saudi Arabia

ISBN: (纸本)9798350321487

Eczema disease diagnosis and treatment are frequently challenging procedures. Recent development in artificial intelligence (AI) or digital healthcare technology have improved the self-management of severe chronic diseases. for example, skin conditions, insulin, pressure, etc. Numerous research has validated its effectiveness. Additionally, studies have also shown that greater knowledge about any illness enables people to properly control their chronic health conditions, which in turn lowers the problems brought on by disorders such as diabetes, eczema, and hypertension. In this research, a novel dermatitis illness monitoring and treatment system is presented for Saudi Arabian patients. The technology being shown uses AI in conjunction with smart healthcare technology. Employing digital healthcare technologies and AI, it also hopes to arm eczema patients with numerous features about the condition. Comprehensive testing and evaluation of the method that this research proposes will take place in a randomized scientific experiment in Saudi Arabic. © 2023 IEEE.

关键词： mHealth

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Classification of Sleep-Related Disorders Using Machine Learning Techniques 25

Classification of Sleep-Related Disorders Using Machine Lear...

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25th International Arab Conference on information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Al-Safarini, Maram Y. Alamleh, Amneh Zaid, Ali Abu Mansur, Hasan Elrashidi, Ali Alzoubi, Haneen Bani-Bakr, Alaa Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan American University of Madaba Faculty of Business Department of Accounting Madaba Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Several people suffer from irregular sleep patterns, a problem that negatively affects their daily lives. It presents a multitude of challenges, including daytime distress, disturbances in sleep-wake patterns, feelings of unease, and a variety of other complications. Diabetes, obesity, and psychological disorders are among the diseases that may be responsible for this sleep disorder. Therefore, the primary aim of this research is to employ the robust capabilities of machine learning in forecasting sleep disorders. This research classifies three diseases: diabetes, obesity, and psychological disorders, using thirty-two different classifiers belonging to five learning strategies. The research compares and evaluates their predictive performance on four datasets. The comparative analysis includes four evaluation metrics: Accuracy, Precision, ROC Area, and MCC. The research results indicate that the best classifier is SGD, followed by LMT, while NBM is the weakest classifier. In addition, the research results revealed that the best strategy is Functions, followed by Rules, with Bayes being the least effective strategy. While conducting the experiment and analyzing the data, it is observed that the best feature selection method that yielded the most favorable outcome is the CorrelationAttributeEval. Furthermore, it has been found that the classification algorithm known as SGD achieved the highest Accuracy and Precision in categorizing the data. This indicates that the combination of CorrelationAttributeEval as the feature selection approach and SGD as the classification algorithm proved to be the most successful and efficient strategy for this research © 2024 IEEE.

关键词： Sleep research

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Implementing Hadoop and Moodle Integrated Student Support System 5

Implementing Hadoop and Moodle Integrated Student Support Sy...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Aung, Myint Myat Min Htet, Shin Set, Hein Myo Hpone Thant, Aung Htut, Sithu Htet, Kyaw Zin Nwe Myint Thein, Nwe University of Information Technology Data Distribution Management Lab Yangon Myanmar University of Information Technology Data Distribution Management Lab Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Learning management systems (LMS) generate large amounts of real-time data which has the potential to provide valuable insights. Hadoop MapReduce is a scalable framework that is designed to handle such data. This paper presents a system architecture that integrates Hadoop MapReduce into Moodle, a popular LMS, for analyzing academic data. The implementation of the architecture allows users to generate a variety of reports from student attendance logs through a Moodle plugin which utilizes the Hadoop cluster for processing data. Users such as lecturers can then utilize these reports to identify which students do and do not engage in which courses as well as monitor their eligibility for exams. The use of Hadoop MapReduce provides high performance computing (HPC) capabilities that offer distributed parallel processing and economical scalability by means of adding commodity nodes to the cluster. © 2024 IEEE.

关键词： MapReduce

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Sparse Convolutional Neural Network for Localization and Orientation Prediction and Application to Drone Control 27

Sparse Convolutional Neural Network for Localization and Ori...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Rodziewicz-Bielewicz, Jan Korzen, Marcin West Pomeranian University of Technology in Szczecin Faculty of Computer Science and Information Technology Żolnierska 49 Street Szczecin71-210 Poland

ISBN: (纸本)9781643685489

Depth cameras used in visual feedback loops typically produce point clouds or their voxelized images. In the paper, we introduce an original sparse convolutional neural network structure tailored to work with 3D point clouds taken from RGB-D camera. The proposed structure is particularly effective for localization and orientation tasks and operates on the entire scene. We demonstrate its application by using the proposed neural network in a computer vision feedback system to track and control the drone indoors. The visual feedback loop measures the drone's state and uses the information about its position and orientation to control the drone and allow it to fly along a given trajectory. In the experimental part we show the effectiveness of this approach to control the drone position in indoor spaces. We achieve trajectory-following accuracy comparable to that of the drone's inbuilt controller's ability to maintain a fixed position. We compare the proposed solution with a YOLO-based detector and sparse convolutional network to angle prediction operating on cropped and aligned drone point clouds. © 2024 The Authors.

关键词： Drones

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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