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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Fiky, Ahmed Hashem El Elshenawy, Ayman Madkour, Mohamed Ashraf Al-Azhar University Systems and Computer Engineering Faculty of Engineering Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

ISBN: (纸本)9781665412438

Nowadays, smartphones became an integral part of human life due to the great necessity for their daily activities. Most smartphone users are downloading and installing mobile apps without worrying about security. Therefore, smartphones are a winning goal for malware, mainly with Android devices. So, it was necessary to provide an intelligent model for detecting malware applications before installing them on android smartphones. In this paper, an intelligent model using machine learning algorithms is proposed for detecting the malware applications in smartphones based on the static malware analysis technique. The contents of an android application features are analyzed, such as permissions, intents, system commands, and API-calls that were extracted from the application manifest file and source code. The proposed model is applied on two separate datasets DREBIN and MALGENOME. The experimental results are compared with some other similar researches, and their results were analyzed. The results indicate that the performance of the proposed model outperforms other similar models for some selected classifiers and can reliably detect both malware and benign Android applications with high accuracy. © 2021 IEEE.

关键词： Android malware

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Strategies for Semi-Automated Registration of Historic Aerial Photographs Utilizing Street and Roof Segmentations as Durable Landmarks 34

Strategies for Semi-Automated Registration of Historic Aeria...

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34th European Modeling and Simulation Symposium, EMSS 2022

作者： Zwettler, Gerald A. Ono, Yuta Stradner, Michael Praschl, Christoph Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Graduate School of Software and Information Science Iwate Prefectural University Japan Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria

Historical and current aerial photographs are only of great value if the geolocation or address of the photographed areas is also available. In Western Europe, especially Austria, Germany and Czech Republic, there is a market for the sale of aerial photographs of one’s own private residential building. Automated geolocation is a feasible way to enable the sales agents to assign the addresses for the sale more quickly. In the course of this research work, a process chain is modeled that allows the assignment of aerial photographs to residential addresses using machine vision. After model-based rectifying the aerial images to compensate for perspective distortions, larger image blocks get assembled using image stitching. The assignment to a 2D reference map, such as satellite imagery via Google Maps, is done by applying a U-Net CNN after extracting durable image features such as roads or buildings. The mapping of aerial imagery to two-dimensional cartography is either automated via registration approaches or based on manually placed corresponding landmarks and homography. Test runs on imagery between the years 1969 and 2020 show that the labor-intensive process of geolocation of aerial imagery can be solved by the proposed process model in a hybrid way. © 2022 The Authors.

关键词： Antennas

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Robust Model Integral Predictive Control Design for Reference Tracking DC Servomechanism 10

Robust Model Integral Predictive Control Design for Referenc...

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10th IEEE International Conference on Intelligent Computing and information systems, ICICIS 2021

作者： Khalil, Khalil Mohamed Elshenawy, Ayman Al-Azhar University Faculty of Engineering Systems and Computers Engineering Department Cairo Egypt Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo Egypt

ISBN: (纸本)9781665440769

Model Predictive Control (MPC) has been very successful in the control industries due to its ability to handle uncertainty in physical systems with some constraints. Due to its higher power density, DC servomechanism is used in control systems of several industrial applications. However, the DC servomechanisms suffer severe throttling loss, leading to low effectiveness, and the uncertainty parameters and distortions caused by the changing system environment harm their performance. In this paper, a Robust Integral MPC (RIMPC) model is designed to enhance the efficiency of the DC servomechanism control system. Uncertainties and nonlinearity of the parameters are taken into consideration. In RIMPC, MPC is incorporated into the integral controller synthesis through an optimization problem subjected to some Linear Matrix Inequality (LMI) constraints to generate the control law of the servomechanism dynamics. The performance of the proposed system is evaluated in two cases: with no disturbances and when disturbances are affecting the system. The obtained results are analyzed and compared with other controllers' design, such as Proportional Integral Derivative (PID) controller and the Monte Carlo (MC) controller. The obtained results prove that the proposed system is more effective in cost function and transient performance (rise time, peak time, maximum overshoot, and settling-time), indicating a much better reference tracking accuracy and proposed method robustness. © 2021 IEEE.

关键词： Model predictive control

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An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence

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Computers, Materials & Continua 2022年第5期71卷 2721-2738页

作者： Aitizaz Ali Muhammad Fermi Pasha Ong Huey Fang Jehad Ali Mohammed A.Al.Zain Mehedi Masud Department of Software Systems and Cyber-Security School of ITMonash University47500Subang JayaMalaysia Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499Korea Department of Information Technology College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia

Personal health records and electronic health records are considered as the most sensitive information in the healthcare *** solutions have been provided for implementing the digital health system using blockchain,but there are several challenges,such as secure access control and privacy is one of the prominent ***,we propose a novel framework and implemented an attribute-based access control system using ***,we have also integrated artificial intelligence(AI)based approach to identify the behavior and activity for security *** current methods only focus on the related clinical records received from a medical ***,existing methods are too inflexible to resourcefully sustenance metadata changes.A secure patient data access framework is proposed in this research,integrating blockchain,trust chain,and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare *** have used a neural network and classifier to categorize the user access to our proposed *** proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare *** results surpass the existing solutions by collecting attributes such as the number of transactions,number of nodes,transaction delay,block creation,and signature verification time.

关键词： Personal health records blockchain technology artificial intelligence access control attributes health care system

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Yolo Based License Plate Detection Of Triple Riders And Violators

Yolo Based License Plate Detection Of Triple Riders And Viol...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： R G Nandhakumar K. Nirmala Devi N. Krishnamoorthy S. Shanthi V R Pranesh S Nikhalyaa Department of Computer Science and Engineering Kongu Enginering College Perundurai Erode Tamilnadu India Department of Software Systems and Engineering School of Information and Engineering Vellore Institutte of Technology Vellore India

Road safety is of prime importance as road accidents are among the biggest causes of deaths in the country. Road Accidents are majorly due to violators and lawbreakers of road safety rules like not wearing helmets, triple riding etc. Even though there are many smart systems to monitor these violations, it is complicated to keep a track of the data and view it efficiently anywhere. Also monitoring the permit details of other state vehicles is hard. A method was proposed to address this issue. based on Deep learning and Optical Character Recognition (OCR). Here we detect the riders not wearing helmet and triple riders using object detection by comparing YOLOv2 and YOLOv3. This simplifies the task of traffic police who can’t continuously and efficiently monitor all the violators. We gathered our data from three different sources. Kaggle was used to collect the data set. A number of items can be recognized by the real-time object recognition system YOLO in a single frame. It recognizes object more precisely and faster than other recognition systems. It can predict up to 9000 classes and even unseen classes. It divides the image into m*m matrix and each grid detects objects within itself. The trained model is tested using a test dataset set after training in order to measure the performance. For Number plate detection we use OCR based character recognition. The recognized license plate images of violators are captured and stored in a database which is then sent to the concerned department. The stored data can be easily accessed using the developed mobile app. Metrics considered for evaluation is Accuracy. The average is utilized to assess object detection models like R-CNN and YOLO. The average calculates a score by comparing the detected box to the ground-truth bounding box. The better the score, the more precise the model’s detections are. When compared to YOLOv2, YOLOv3 exhibits strong performance in terms of accuracy with 95.5%. Keywords—Deep learning; Object detecti

关键词： Head Road accidents Databases Computational modeling Object detection Road safety Safety

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Development of a Load Distribution Model for information Processing Centers in Automated Power Supply Control systems

Development of a Load Distribution Model for Information Pro...

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： Aung Kyaw Myo Alexander M. Bain Evgeni M. Portnov Institute of Systems and Software Engineering and Information Technology National Research University of Electronic Technology (MIET) Zelenograd Moscow Russia

Nowadays computing power and the bandwidth of communication channels of automated power supply control systems are widely used. Ireeational use of this leads to an overload of the processing center that included emergency information flows while simultaneously receiving them from several control units at once. To solve this problem, methods and algorithms for distributing information processing centers’s load can be used. In this paper a mathematical model of the query processing process has been developed by using a cache-aware algorithm for balancing the information processing centers’s load in distributed computing systems. The modified algorithm for consistent hashing are proposed, which provided better uniformity of the load of information processing centers, as well as prevent possible overloads.

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FRAUDability: Estimating Users’ Susceptibility to Financial Fraud Using Adversarial Machine Learning

arXiv

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arXiv 2023年

作者： Doytshman, Chen Momiyama, Satoru Singh, Inderjeet Elovici, Yuval Shabtai, Asaf Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel NEC Corporation Japan

In recent years, financial fraud detection systems have become very efficient at detecting fraud, which is a major threat faced by e-commerce platforms. Such systems often include machine learning-based algorithms aimed at detecting and reporting fraudulent activity. In this paper, we examine the application of adversarial learning based ranking techniques in the fraud detection domain and propose FRAUDability, a method for the estimation of a financial fraud detection system’s performance for every user. We are motivated by the assumption that"not all users are created equal" – while some users are well protected by fraud detection algorithms, others tend to pose a challenge to such systems. The proposed method produces scores, namely "fraudability scores", which are numerical estimations of a fraud detection system’s ability to detect financial fraud for a specific user, given his/her unique activity in the financial system. Our fraudability scores enable those tasked with defending users in a financial platform to focus their attention and resources on users with high fraudability scores to better protect them. We validate our method using a real e-commerce platform’s dataset and demonstrate the application of fraudability scores from the attacker’s perspective, on the platform, and more specifically, on the fraud detection systems used by the e-commerce enterprise. We show that the scores can also help attackers increase their financial profit by 54%, by engaging solely with users with high fraudability scores, avoiding those users whose spending habits enable more accurate fraud detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Performance Evaluation of Clustering Algorithms for Enhancing Test Case Prioritization in Regression Testing

Performance Evaluation of Clustering Algorithms for Enhancin...

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Advanced Research in Computing (ICARC), International Conference on

作者： S. Prabath Puviskar W.V.S.K. Wasalthilaka B.T.G.S Kumara Department of Computing & Information Systems Sabaragamuwa University of Sri Lanka Sri Lanka Department of Software Engineering Sabaragamuwa University of Sri Lanka Sri Lanka

Regression testing plays a crucial role in maintain software quality as applications evolving and getting more complex. Selection of a regression testing technique significantly influences overall software quality. In initial stages, this involves picking relevant test cases and remove unnecessary redundancies during minimization phase. Finally, prioritization of test cases phases most important ones is executed first which continue process iteratively or whole testing carried out which effect time and cost intensively. Test Case Prioritization method effective way to address the issue. In this study, we proposed approach to enhance Test Case Prioritization by integrating fault based methods and time of execution analysis. The study also evaluates the efficiency in proposed methodology through, employing APFD metric. The study aims to improve the precision and effectiveness of test case prioritization methods, advancing overall quality and reliability in dynamic software development environments. Notably, AHC outperforms in comparisons, showcasing its efficacy in improving outcomes.

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Automatic Multi-Class Non-Functional software Requirements Classification Using Machine Learning Algorithms

Automatic Multi-Class Non-Functional Software Requirements C...

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International Research Conference on Smart Computing and systems engineering (SCSE)

作者： M. A. F. Saroth P. M. A. K. Wijerathne B. T. G. S. Kumara Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Department of Software Engineering Faculty of Computing Sabaragamuwa University of Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

A successful software development process depends on software requirements. Those requirements are often classified into two categories: functional requirements (FR) and non-functional requirements (NFR). In software development, NFRs play a crucial role, but they are often forgotten by the developers. Therefore, it causes adverse consequences. As a result, an NFR classification method is required to assist software developers in classifying NFR from requirement documents. The majority of research in this area focuses on categorizing requirements as FR or NFR. However, NFR can be divided into more than two categories. As a result, a multi-label categorization strategy was required. This study presented an approach for categorizing NFR based on product, process, and external factors. To categorize the NFR, the authors developed four machine learning-based algorithms entitled Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K Nearest Neighbor Classifier (KNN) classifier, as well as one deep learning method named Long Short-Term Memory (LSTM) model. Ultimately, the authors examine the models and conclude that the LSTM model which gained an accuracy of 99.69% is appropriate for this type of classification.

关键词： Support vector machines Deep learning Logistic regression Machine learning algorithms Computational modeling software Classification algorithms

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Interruptions detection in video conferences

arXiv

引用

arXiv 2023年

作者： Horowitz, Shmuel Kagan, Dima Alpert, Galit Fuhrmann Fire, Michael The Data4Good Lab Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

In recent years, video conferencing (VC) popularity has skyrocketed for a wide range of activities. As a result, the number of VC users surged sharply. The sharp increase in VC usage has been accompanied by various newly emerging privacy and security challenges. VC meetings became a target for various security attacks, such as Zoombombing. Other VC-related challenges also emerged. For example, during COVID lockdowns, educators had to teach in online environments struggling with keeping students engaged for extended periods In parallel, the amount of available VC videos has grown exponentially. Thus, users and companies are limited in finding abnormal segments in VC meetings within the converging volumes of data. Such abnormal events that affect most meeting participants may be indicators of interesting points in time, including security attacks or other changes in meeting climate, like someone joining a meeting or sharing a dramatic content. Here, we present a novel algorithm for detecting abnormal events in VC data. We curated VC publicly available recordings, including meetings with interruptions. We analyzed the videos using our algorithm, extracting time windows where abnormal occurrences were detected. Our algorithm is a pipeline that combines multiple methods in several steps to detect users' faces in each video frame, track face locations during the meeting and generate vector representations of a facial expression for each face in each frame. Vector representations are used to monitor changes in facial expressions throughout the meeting for each participant. The overall change in meeting climate is quantified using those parameters across all participants, and translating them into event anomaly detection. This is the first open pipeline for automatically detecting anomaly events in VC meetings. Our model detects abnormal events with 92.3% precision over the collected dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Video conferencing

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