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International Symposium on Digital Forensic and Security (ISDFS)

作者： Görkem Gök Aytuğ Boyacı Mustafa Ulaş Department of Software Engineering Faculty of Technology Fırat University Elazığ Türkiye Department of Computer Engineering Air Force Academy National Defence University İstanbul Türkiye Department of Artificial Intelligence and Data Science Engineering Faculty Of Engineering Fırat University Elazığ Türkiye

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

This study is meant to research the evolution of intrusion detection and network monitoring within computer, cloud-based systems, IIoT, and mobile environments. The source has outlined the novel technologies in IDS, from “Fast Learning Network” to hybrid classifiers aiming at protecting cloud computing. The findings highlight innovation IDS technologies that provide flexible and peculiar solutions to ward off potential cyber threats. In addition, it presented with the use of Nagios, NConf, the state of the art in network traffic monitoring, and deep learning as tools for advanced management and network protection. Besides, the research also reviewed concepts of Big Data Analytics and Federated Learning in bettering direct integration and anomaly detection in IIoT. A good comparison of the two would show that recent technological advancements are much needed for network efficiency and its cybersecurity. Based on the findings, the scope of networked system security develops dynamically amid current challenges.

关键词： Deep learning Performance evaluation Cloud computing Technological innovation Federated learning computer hacking Intrusion detection

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Deep Learning based Brain Tumor Classification for MR Images using ResNet50

Deep Learning based Brain Tumor Classification for MR Images...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Ömer Miraç Kökçam Aytuğ Boyaci Muhammed Emre Çolak Department of Software Engineering Faculty of Technology Fırat University Elazığ Türkiye Department of Computer Engineering Air Force Academy National Defence University İstanbul Türkiye Department of Artificial Intelligence and Data Science Engineering Faculty of Engineering Fırat University Elazığ Türkiye

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, “Early Diagnosis Saves Lives”. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis.

关键词： Deep learning Image recognition Brain modeling Internet Security Artificial intelligence Medical diagnostic imaging

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Design of an easy upgradable cost efficient autonomous assistive robot ROSWITHA

Design of an easy upgradable cost efficient autonomous assis...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Sharan, Sudeep Nauth, Peter Umansky, Julian Michalik, Robert Domínguez-Jiménez, Juan José Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Avda. de la Universidad de Cádiz 10 Cádiz Puerto Real11519 Spain

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this paper, a design and development of an assistive robot are proposed that assists to the elderly and handicapped people, such as providing information and care with daily routine supports with the ability to perform various tasks, including the contact safety. Firstly a clear function requirement for such assistive robots was made. Secondly different sub-parts have been mounted to synthesize/unify the functions such as high power, dexterity/competence, mobility, safety and the cost-effectiveness. Additionally, a set of different applications, like the robot moves to other rooms/locations according to the commands given by the user, and the arm performs the task of grabbing and pouring/serving. Based on the application, the assistive prototype robot ROSWITHA (RObot System WITH Autonomy) was developed successfully, that operates using three major systems- the vision system, the arm system and the navigation system. The mechanisms are the camera, LIDAR, ultrasonic sensors, dual arm with gripper and two wheeled mobile vehicle including two castor wheels. Some experiments are performed focusing on assistive tasks using ROSWITHA's sub-systems, indicating that the robot software can be integrated and become extremely useful to improve the living style of the elderly and handicap people in future. © 2022 IEEE.

关键词： Cost effectiveness

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Handwritten Signature Recognition using Parallel CNNs and Transfer Learning for Forensics

Handwritten Signature Recognition using Parallel CNNs and Tr...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Jaouhar Fattahi Feriel Sghaier Mohamed Mejri Ridha Ghayoula Emil Pricop Baha Eddine Lakdher Department of Computer Science and Software Engineering Laval University Quebec Canada Carthage National Engineering School Carthage University Tunis Tunisia Faculty of Engineering University of Moncton Canada Computers & Electronics Department Automatic Control Petroleum-Gas University of Ploieşti Ploieşti Romania Faculty of Science University of Monastir Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Handwritten signatures hold paramount importance in legal, financial, and administrative domains, necessitating the development of robust signature recognition tools for forensic applications. This paper introduces a handwritten signature recognition (HSR) model employing Parallel Convolutional Neural Networks (CNN) tailored for forensic endeavors. Utilizing the parallel processing capabilities of CNN, our proposed approach adeptly analyzes and extracts discriminative features from handwritten signature images to facilitate precise recognition. In addition, we leverage several transfer learning techniques by parallelizing proven pre-trained CNNs. Extensive experimentation validates the efficacy of our approach on a standard dataset, demonstrating high accuracy and resilience in signature recognition tasks. The proposed approach exhibits substantial promise in augmenting forensic investigations by automating signature verification processes, thereby bolstering fraud detection efforts and upholding the integrity of legal documentation.

关键词： Deep learning Handwriting recognition Forensics Face recognition Transfer learning Feature extraction Fraud Convolutional neural networks Standards Resilience

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A Statistical Model to Assess the Team's Productivity in Agile software Teams 4

A Statistical Model to Assess the Team's Productivity in Agi...

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4th IEEE International Conference and Workshop in Obuda on Electrical and Power engineering, CANDO-EPE 2021

作者： Mashmool, Amir Khosravi, Samiyeh Joloudari, Javad Hassannataj Inayat, Irum Gandomani, Taghi Javdani Mosavi, Amir University of Birjand Faculty of Engineering Computer Engineering Department Birjand Iran National University of Computer and Emerging Sciences School of Computing Department of Software Engineering Islamabad Pakistan Shahrekord University Department of Computer Science Shahrekord*** Iran Obuda University Institute of Software Design and Development Budapest1034 Hungary

ISBN: (纸本)9781665420235

Agile methods promise to achieve high productivity and provide high-quality software. Agile software development is the most important approach that has spread through the world of software development over the past decade. software team productivity measurement is essential in agile teams to increase the performance of software development. Due to the prevalence of agile methodologies and increasing competition of software development companies, software team productivity has become one of the crucial challenges for agile software companies and teams. Awareness of the level of team productivity can help them to achieve better estimation results on the time and cost of the projects. However, to measure software productivity, there is no definitive solution or approach whether in traditional and agile software development teams that lead to the occurrence of many problems in achieving a reliable definition of software productivity. Hence, this study aims to propose a statistical model to assess the team's productivity in agile teams. A survey was conducted with forty software companies and measured the impact of six factors of the team on productivity in these companies. The results show that team effectiveness factors including inter-team relationship, quality conformance by the team, team vision, team leader, and requirements handled by the team had a significant impact on the team's productivity. Moreover, the results also state that inter-team relations affect the most on software teams' productivity. Finally, the model fit test showed that 80% of productivity depends on team effectiveness factors. © 2021 IEEE.

关键词： Productivity

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm

Web Application to predict Skin Disease: An Approach Using D...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Eelanila Thayalan Ayingaran Arumugavel Sharujan Ganeshan Makenthiran Dalaxshan Harinda Fernando Kaushalya Rajapakse Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system’s proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife.

关键词： Transfer learning Medical services Skin Pattern recognition Medical diagnostic imaging Standards Robots

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Mudahnya BM: A Context-Aware Mobile Cloud Learning Application Using Semantic-Based Approach

Mudahnya BM: A Context-Aware Mobile Cloud Learning Applicati...

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7th International Conference on Advances in Visual Informatics, IVIC 2021

作者： Muhammad, Sufri Admodisastro, Novia Osman, Hafeez Mohd Ali, Norhayati Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Seri Kembangan Selangor43400 Malaysia

ISBN: (纸本)9783030902346

A great potential of various learning environment in mobile learning application can clearly be seen in current pandemic situation. The accessibility of the learning resources needs to be available from anywhere anytime despite having strong or poor internet connection. It has motivated researchers to imply context-aware capability in improving the accessibility of the learning resources. This paper presents a development process of Mudahnya BM mobile application that follows a fundamental concept of Mobile Cloud Learning (MCL). Mudahnya BM is an application to learn basic Malay language for learner 7–12 years old. The injection of extrinsic and intrinsic context-aware help the application to improve the reasoning process for finding available learning resources from service providers. Semantic-based approach is applied in the reasoning process. This study involved the end users for evaluation purposes. 33 randomized scenarios have been tested using One-Sample Wilcoxon Signed Rank test. The result shows a positive impact to the population. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Extended Properties of Nonlinear Power System Models that are Symmetrically Configured in the Narrow Sense

SSRN

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SSRN 2024年

作者： Segev, Elior Zhuk, Vladimir Ginzburg-Ganz, Elinor Belikov, Juri Levron, Yoash Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akademia tee 15a Tallinn12618 Estonia

Three-phase circuits that are Symmetrically Configured in the Narrow Sense (SCNS) have been defined in previous works as circuits which dq0 dynamic model does not depend on the transformation's reference angle. This property leads to dynamic models that are linear and time-invariant in the dq0 reference frame, or at least suitable for low-complexity numerical analysis, which is crucial when studying large-scale power systems. The current work is an extension of two previous works on the same topic. We continue here to explore properties of SCNS circuits, and provide two crucial extensions: first we explore circuits that are not passive, such as generators and inverters, and second we extend the mathematical formulation to apply to circuits that have more then one frame of reference. Our primary objective is to further enrich and extend the theoretical and practical tools introduced in previous works. The theoretical tools presented here are followed by a detailed case-study. © 2024, The Authors. All rights reserved.

关键词： Dynamic models

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Flexibility and Privacy: A Multi-Head Federated Continual Learning Framework for Dynamic Edge Environments

Flexibility and Privacy: A Multi-Head Federated Continual Le...

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International Symposium on Computing and Networking (CANDAR)

作者： Chunlu Chen Kevin I-Kai Wang Peng Li Kouichi Sakurai Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

Federated Learning (FL) and the Internet of Things (IoT) have revolutionized data processing and analysis, overcoming the traditional limitations of cloud computing. However, traditional machine learning strategies lead to increased costs and catastrophic forgetting due to model retraining with new datasets. Continual learning has been proposed to counter this, enabling models to adapt to new data while preserving previous knowledge, which is beneficial for dynamic edge environments. Despite these advantages, the retention of previous knowledge during the continual learning process may lead to the information leakage. To address the inherent challenges of multitask scenarios, we present a Federated Continual Learning (FCL) framework that integrates the privacy-preserving benefits of Federated Learning (FL) into a continual learning system, ensuring both continual learning and privacy preservation in edge computing data processing and analysis. Specifically, our architecture introduces dedicated fully-connected layers for each task. This architecture ensures that distinctive features pertinent to each task are not only captured but also preserved throughout the model’s lifespan. Within our framework, data is processed via task-specific layers. Subsequently, the final label is determined by associating it with the paramount prediction value, thus capitalizing on the model’s comprehensive knowledge reservoir to bolster prediction accuracy. We subjected our FCL framework to rigorous validation using two benchmark datasets: MNIST and CFAR-10. Experimental outcomes unequivocally substantiate the efficacy of our proposed methodology.

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Identification of selection criteria for blockchain platforms

Identification of selection criteria for blockchain platform...

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International Association of Institutes of Navigation World Congress (IAIN)

作者： Shahr Banoo Muradi Yin Kia Chiam Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

Blockchain technology is widely used to develop software systems in different industries such as finance, healthcare, supply chain management, data management, Internet of Things (IoT). To adopt blockchain, some critical challenges such as performance, interoperability, availability, security attacks, sustainability, and latency need to be taken into consideration. If the blockchain platform selected is not appropriate, the functionality and quality of the blockchain-based systems will be affected. Additionally, numerous blockchain platforms with unique properties and specifications are available in the market, and the task of selecting the right platform that best meets the project needs is getting complicated. It is crucial to define significant criteria that can help to shortlist the blockchain platforms for further comparison and selection using multicriteria decision-making methods. This research proposes a set of selection criteria that cover both features and quality attributes of the blockchain platforms. A survey was conducted to get the opinions of blockchain practitioners on the suitability of the selection criteria for the evaluation and selection of blockchain platforms.

关键词： Supply chain management Decision making Medical services software systems Blockchains Internet of Things Security

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