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2nd International Conference on Advances in engineering Science and Technology, AEST 2022

作者： Sheet, Omar I. Ibrahim, Laheeb M. University of Mosul College of Computer Sciences & Math Software Engineering Dept. Iraq

ISBN: (纸本)9798350334906

The volume of network traffic data has become so big and complicated as a result of the development in Internet-based services that it is extremely difficult to process using typical data processing techniques. Due to the enormous and complicated nature of network traffic data, fast and effective cybersecurity intrusion detection is a highly difficult task. In order to detect hostile traffic as soon as feasible, a realistic cyber security intrusion detection system should be able to handle a huge volume of network traffic data as quickly as *** paper studies a classification-based intrusion detection algorithms based on Machine Learning for speedy and effective intrusion detection in huge network traffic, including Support Vector Machines (SVM), Random Forests(RF), Decision Trees(DT), Naïve Bayes (NB), Deep Neural Network (DNN), Extreme Gradient Boosting (XG Boost), Nondominated Sorting Genetic Algorithm (NSGA2), as well as Deep Belief Network (DBN) and artificial neural network(AI). It has been applied to ACTUAL TIME The KDD dataset, KDD Cup 99 dataset, NSL-KDD dataset, CICIDS-2017 dataset, CICIDS-2018 dataset, ISCX Dataset, CICAndMal2017 dataset and UNSW-NB15 to compare performance based on rating Intrusion detection systems which are assessed in terms of training time, prediction time, accuracy. © 2022 IEEE.

关键词： Intrusion detection

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An In-dept. Study on Handwritten Signature Authentication Using Unique Writer Traits 1

An In-Depth Study on Handwritten Signature Authentication Us...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Vamsikrishna, M. Murari, Haritha Manikandan, M. Mishra, Nilamadhab Raghavendra Gupta, T. Patel, Rohit Aditya University Dept. of Computer Applications Kakinada India Spark Infotech Inc Sr Software Engineer PhoenixAZ United States VeL Tech. R Dr.S Dept. of Computer Science and Engineering Tamilnadu India VIT Bhopal University Dept. of Computer Science and Engineering Sehore India HITM Telenagana Hyderabad India Calorx Teachers' University Dept of CSE Gujrat India

ISBN: (纸本)9798350356816

Handwriting verification, a subset of signature verification, involves using handwritten text to verify or authorize someone's identification. Although simple handwriting can serve as a signature, it is often modified to suit the signer's preferences. The uniqueness of each person's handwritten signature makes it essential for accurate distinction from others. This study presents a handwritten signature verification system using a writer-dependent approach. While signature verification is widely used for identity verification and security, it is susceptible to forgeries. The writer-dependent approach considers the unique characteristics of an individual's signature, training the system to recognize them and making forgery replication more challenging. An extensive review of the literature on signature verification systems, focusing on the writer-dependent approach, was conducted. A system was then developed and implemented based on this approach, utilizing a dataset of signatures from 12 writers. Results indicate that the writer-dependent approach outperforms other methods in terms of accuracy and robustness. The system demonstrated high precision and recall rates, underscoring its effectiveness in detecting both genuine and forged signatures. This study highlights the writer-dependent approach's efficacy in enhancing the accuracy and security of signature verification systems, with implications for developing more reliable and efficient identity verification systems in today's digital age. © 2024 IEEE.

关键词： Authentication

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Intelligent Model for Optimizing Gantt Chart in the Planning Stage 11

Intelligent Model for Optimizing Gantt Chart in the Planning...

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11th International Conference on Information Technology, ICIT 2023

作者： Bdair, Noor Alzyadat, Wael Alzubi, Shadi Alzaytoonh University of Jordan Faculty of Science and IT Dept of Software Engineering Jordan Alzaytoonh University of Jordan Faculty of Science and IT Dept. of of Computer Science Jordan

ISBN: (纸本)9798350320060

Scheduling tasks under resource constraints is essential for project success, and this research focuses on scheduling tasks efficiently to minimize completion time. To tackle this problem, two optimization techniques are employed: Linear programming (LP) and particle swarm optimization (PSO). PSO significantly reduces the duration, leading to a reduction in project duration. But the LP algorithm does not improve the project duration. Using suitable algorithms to maximize project efficiency and success is essential for successful project management. This research provides valuable information for project managers and decision makers, highlighting the importance of utilizing suitable algorithms to maximize project efficiency and success. © 2023 IEEE.

关键词： Linear programming

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Robust Psoriasis Severity Classification by using Data Augmentation

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Transactions of the Korean Institute of Electrical Engineers 2022年第12期71卷 1841-1847页

作者： Moon, Cho-I Baek, Yoo Sang Choi, Min Hyung Lee, Onseok Dept. of Software Convergence Graduate School Soonchunhyang University Korea Republic of Dept. of Dermatology Guro Hospital Korea University College of Medicine Korea Republic of Dept. of Computer Science and Engineering College of Engineering Design and Computing University of Colorado Denver United States

Psoriasis is a chronic recurrent disease formed by lesions such as erythema and scale. To evaluate the severity of psoriasis, the psoriasis area and severity index (PASI) score have been used in clinical trials and studies. This clinical indicator is subjective, so to overcome these shortcoming, various automatic psoriasis analysis methods based on deep learning have been studied. However, the limited number of data and psoriasis characteristic such as ambiguity of severity deteriorate model performance. One of the simple and powerful methods to overcome these problem is data augmentation. Data augmentation should be used according to data characteristics. Therefore, we analyzed and compared the classification results applied with five data augmentation methods, Geometric transformation, CutMix, Visual Corruptions, AutoAugment, RandAugment, and explored data augmentation method suitable for psoriasis severity classification. We used the EfficientNet B2 for psoriasis severity classification. As a result, when RandAugment or the combination of Geometric transform and Visual Corruptions were used, it showed the best classification performance with an accuracy of 87.5%. In addition, we confirmed the effect of data augmentation for improving model performance and the difference in performance according to single or multiple applications of the data augmentation methods. Through these results, our study can be applied to various studies as a data augmentation method suitable for psoriasis disease image. Copyright © The Korean Institute of Electrical Engineers.

关键词： Deep learning

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Handcrafted and Deep features based Classification of Scoliosis 2

Handcrafted and Deep features based Classification of Scolio...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Fatima, Joddat Mohsan, Mashood M. Qaisar, Muhammad Umair Hamza, Muhammad Tahir, Muhammad Zeshan Zaman, Ghazzal Bahria University Dept. of Computer Science Islamabad Pakistan National University of Sciences & Technology Dept. of Computer & Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9781665498197

The Spinal cord acts as the central transmission line connecting the Brain with all other body organs. Vertebrae are 33 uneven bones stacked over one another that holds the whole skeleton structure. Scoliosis is the three-dimensional spinal deformity which commonly occurs during the growing age and erupts before puberty. It is further classified in two Shapes C and S. Our research work has two stages, in first stage we segment out the vertebral column using Mask-RCNN. The segmented column is used for features extraction and in stage two feature based classification is done for normal, C and S shape of scoliosis using AASCE2019 dataset. A comparative study on multiple image classification networks is also conducted and based on results EfficientNet-B4 is selected for formulation of hybrid feature set. The accuracy achieved using Random forest classifier, for handcrafted and deep features was up to 94.32% and 89.66%. Hybrid feature set formulated with combination of deep and handcrafted features attained accuracy up to 94.45%. © 2022 IEEE.

关键词： Decision trees

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Decision Support System: Analysis and Design Methodology 2

Decision Support System: Analysis and Design Methodology

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Darbi, Huweida A. Saleh, Eiman M. University of Derna Dept. Computer Science Derna Libya Benghazi University Dept. Software Engineering Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

The success of any organization depends on the quality of the decisions made, and good decisions can be made by analyzing all the available data and knowledge of the organization. Decision Support Systems (DSSs) are used to analyze this data and aid in decision-making in the formulation of well-studied decisions based on knowledge. However, the analysis and design of decision support systems (DSS) is a complex process that involves the use of appropriate methodologies, methods, and tools to model decision-making processes. This paper presents an architectural design of DSS framework. The proposed approach helps developers develop decision-making systems through models that detail the analysis and design. © 2022 IEEE.

关键词： Decision support systems

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Reliability-Based Design of Time-Varying Electricity Tariff 31

Reliability-Based Design of Time-Varying Electricity Tariff

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31st International Conference on Electrical engineering, ICEE 2023

作者： Rastegar, Mohammad Sarajpoor, Nima School of Electrical and Computer Engineering Shiraz University Shiraz Iran University of Calgary Dept. of Electrical and Software Eng. Calgary Canada

ISBN: (纸本)9798350312560

Implementing price-based energy management programs to change the consumption pattern of residential consumers has become an indispensable solution to the operational challenges of smart distribution power systems. However, practical methods to design efficient time-varying prices to improve the reliability and operation of distribution systems have remained scarce. This paper proposes a bi-objective optimization problem to design time-differentiated electricity tariff for residential energy management, where a new reliability evaluation method is employed to evaluate the reliability enhancement caused by load shifting. A set of numerical studies and sensitivity analysis are conducted to investigate the effectiveness of the proposed pricing method. © 2023 IEEE.

关键词： Energy management

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Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning 3

Robust Phishing URL Classification Using FastText Character ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Al-Sakib, Abdullah Limon, Zishad Hossain Sakib, Anamul Pranto, Md Nuruzzaman Islam, Md Ariful Sultana, Shayma Ahmed, Md. Redwan Rahman, Shafiur Westcliff University Dept. of Information Technology CA92614 United States Westcliff University Dept. of Computer Science CA92614 United States International American University Dept. of Business Administration CA90010 United States International American University Dept. of Management Information System CA90010 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizations. Being able to identify fraudulent websites is crucial in order to effectively address these potential risks. This study introduces a novel method for detecting phishing URLs by using word and character embeddings to capture complex URL patterns. We used a dataset of 80,000 URLs, including 50,000 legitimate ones and 30,000 phishing instances, and applied thorough preprocessing techniques. We utilized word embeddings in FastText to handle unseen words, with the added advantage of n-gram representations. Additionally, we captured character-level features through dense character embeddings. We trained several machine learning and deep learning models, and one model, the Convolutional Bidirectional LSTM (CBiLSTM), stood out with an accuracy of 99.01% and an F1-score of 99.08%. Furthermore, we made a thorough comparison with the most advanced techniques available, and our findings demonstrated clear superiority over previous research. This study presents an effective approach for classifying phishing URLs, providing a valuable tool to combat fraud and protect against identity theft, thereby helping to minimize the financial and emotional harm experienced by victims. © 2024 IEEE.

关键词： Phishing

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CDP: Selective Influential Nodes Based on Cliques in the Diffusion Process in Social Networks 9

CDP: Selective Influential Nodes Based on Cliques in the Dif...

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9th International Conference on Web Research, ICWR 2023

作者： Yazdanifar, Fatemeh Beni, Hamid Ahmadi Salimi, Nikoo Bouyer, Asgarali Amirkabir University of Technology Dept. of Mathematics and Computer Science Tehran Iran Madani University Dept. Software Engineering Azarbijan Shahid Tabriz Iran University of Genoa Dept. of the Elec. Electronics Electronics and Telecommunication Eng. and Naval Architecture Genoa Italy Azarbaijan Shahid Madani University Dept. of Computer Engineering Tabriz Iran

ISBN: (纸本)9798350399691

Influence maximization techniques emphasize selecting a set of influential nodes in order to maximize influence. Because the algorithms presented in this field ignore the topology of cliques for diffusion, there are two major challenges in influence maximization algorithms: optimal diffusion and computational overhead reduction. As a result, the CDP algorithm is presented in this article to address these issues. This algorithm first selects suitable cliques for diffusion based on their position and strategy in social networks. Furthermore, for each clique, a score is calculated based on the topological criteria of the clique, and suitable cliques are selected for diffusion by applying a threshold limit. The seed nodes are chosen from the cliques in the second step. To avoid the rich club phenomenon, only a few nodes from each clique are chosen as seed candidate nodes. Finally, the seed nodes are chosen based on the node's topology and the strength of the node's level one neighbor. In the experiment section, the CDP algorithm significantly outperforms the best algorithms presented in recent years in terms of influence spread rate and execution time. © 2023 IEEE.

关键词： Diffusion

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Report on the 28th Foundation of software engineering Workshop (FOSE2021)

Report on the 28th Foundation of Software Engineering Worksh...

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作者： Nagura, Masataka Sawada, Atsushi Sekizawa, Toshifusa Dept. of Software Engineering Fuculty of Sciense and Technology Nanzan University Japan Dept. of Computer Science College of Engineering Nihon University Japan

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