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Machine Learning for Intrusion Detection: Design and Implementation of an IDS Based on Artificial Neural Network 1

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11th International Conference on Innovations in Bio-Inspired computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020

作者： Wadiai, Younes El Mourabit, Yousef Baslam, Mohammed TIAD Laboratory Sciences and Technology Faculty Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (数字)9783030736033

ISBN: (纸本)9783030736026

Securing the network from intrusions becomes a more challenging task to conduct for system administrators, and the need for a more powerful and efficient intrusion detection system emerges with the continuous development of cyber-attacks exploring various methods and techniques. We propose the usage of artificial intelligence to build a sophisticated Network Intrusion Detection System able to be trained/self-trained to detect malicious network traffic. The aim of this paper is to present a new IDS model based on machine learning approach to detect malicious traffic and protect the network from cyber-attacks. The usage of machine learning will allow better accuracy in detection and faster response time. To implement and measure the performance of our model, we used NSL-KDD dataset, after the preprocessing phase, (cleaning, normalization …), we used the most relevant attributes for the classification process based on CfsSubsetEval technique with BestFirst approach as an attribute selection algorithm for removing the redundant attributes to allow the usage of the most pertinent attributes of the dataset. Based on the comparative evaluation of three algorithms (K-means, AdaBoost and Multilayer Perceptron), the experimental results show that the MLP algorithm provides a high detection rate and reduces false alarm rate. Finally, a set of principles is concluded, which will set path for future research for implementing an efficient and performant IDS. To help researchers in the selection of IDS, several recommendations are provided with future directions for this research. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Adaptive boosting

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Evaluation of LMT and DNN Algorithms in Software Defect Prediction for Open-Source Software 1

引用

International conference on Research in intelligent and computing in Engineering, RICE 2020

作者： Alazawi, Sundos Abdulameer Salam, Mohammed Najim Al Department of Computer Science Sciences Collage Mustansiriyah University Bagdad Iraq Department of Computer Engineering Technology University Baghdad Iraq

ISBN: (数字)9789811575273

ISBN: (纸本)9789811575266

Software defect prediction has achieved considerable salability in the last years. Defect prediction is the first important step in dependability assessment of complex software systems, because it can immediately be impacted on the dependability of those systems, improves performance, and decreases the software cost. Among the most important approaches used primarily in software defect prediction are the algorithms of machine learning classification. In this research, LMT machine learning and deep learning algorithms are presented for the software bug prediction model process on a general dataset obtained by a combination of several datasets for four open-source software and applications. These are Linux kernel, MySQL DBMS, Apache HTTPD web server, and Apache AXIS WS. A new dataset has represented four defect classes: Bohrbug, Mandelbug, Aging-Related Bug, and Unknown bugs. The new classification approach depends on defect location in OSSs. The system behavior is categorized to assess system dependability in the future. Performance measurement is implemented in JAVA using NETBEANS version 8.0.2 for LMT classifier and in Python for Deep Learning classifier. In the results of experiments, fault predictors using the DNN classifier perform better than LMT, where accuracy weighted average for each class using the LMT classifier is 0.849, while accuracy weighted average for each class using DNN is 0.87. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Open source software

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Priority-Based Optimized Scheduling (PBOS) Algorithm for Big Data Applications in Cloud computing Environments 1

引用

1st International Conference on Mathematical Modeling and Computational Science, ICMMCS 2020

作者： Tamilarasi, P. Akila, D. Chennai India Chennai India

ISBN: (数字)9789813343894

ISBN: (纸本)9789813343887

This paper proposes a priority-based optimized scheduling (PBOS) algorithm for cloud-based big data applications. In this algorithm, for each incoming task request, the task size and expected completion time are estimated. At the same time, for each VM on a host, the processing capability and current load are estimated. A priority queue for each task is then created in which the tasks are stored in the ascending order of task size and completion time. Similarly, a priority queue for VMs is also made in which the VMs are stored in the ascending order of current load and processing capacity. Then, the scheduling algorithm intelligently maps each user tasks from task queue to the suitable matching VM from VM queue. The process is continued until both these queue become empty. By experimental results, it is shown that PBOS achieves lesser response time and execution time. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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Algorithms for the Synthesis of Optimal Linear-Quadratic Stationary Controllers 1

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11th World Conference on intelligent systems for Industrial Automation, WCIS 2020

作者： Sevinov, J.U. Mallaev, A.R. Xusanov, S.N. Tashkent State Technical University Tashkent Uzbekistan Karshi Engineering-Economic Institute Karshi Uzbekistan

ISBN: (数字)9783030680046

ISBN: (纸本)9783030680039

Algorithms for the synthesis of optimal linear-quadratic stationary controllers in adaptive control systems for technological objects are presented. To construct the control system, the state parameter space method was used. To determine the optimal control of the problem under consideration in various situations, it is proposed to use the solutions of the algebraic Riccati equation. The given computational schemes made it possible to effectively solve the problems of parametric synthesis of optimal controllers in control systems for industrial technological processes. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Controllers

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Combined Method for Accessibility Evaluation in Tele-Rehabilitation Platforms for Low Vision Users

Combined Method for Accessibility Evaluation in Tele-Rehabil...

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4th International Conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Acosta-Vargas, Patricia Perez-Medina, Jorge-Luis Acosta-Vargas, Gloria Salvador-Acosta, Belen Esparza, Wilmer Jimenes-Vargas, Karina Gonzalez, Mario Univ Amer Intelligent & Interact Syst Lab SI2 Lab Via Nayon Quito Ecuador Pontificia Univ Catolica Ecuador Fac Med Quito Ecuador

ISBN: (纸本)9783030680176;9783030680169

According to the World Health Organization, about 15% of persons who have a disability. Existing web applications are not easily accessible, particularly for people with disabilities. In this research, we present a combined method that consists of applying an automatic inspection with a WAVE tool and manual method. In this study, we employed an examination of the accessibility assessment of ePHoRt, a web platform for hip arthroplasty patient rehabilitation, from the physical therapist's perspective. In this research, we included the application of 12 accessibility barriers for people with a low vision based on the Web Content Accessibility Guidelines (WCAG) 2.1 and 2.2. The results obtained suggest that digital ramps and WCAG have more accessible and inclusive web platforms.

关键词： Accessibility Combined method Evaluation Low vision Tele-rehabilitation Web content accessibility guidelines WCAG 2.1

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Electricity Forecasting Using Machine Learning: A Review 1

引用

2nd International Conference on Machine Learning and Information Processing, ICMLIP 2020

作者： Pawar, Shital Mole, Prajakta Phadtare, Shweta Aghor, Dhanashri Vadtile, Pranali Electronics and Telecommunication Department Vishwakarma Institute of Technology Pune Maharashtra India

ISBN: (数字)9789813348592

ISBN: (纸本)9789813348585

In today’s revolutionary period, electricity is one of the crucial aspects among the industry as well as day to day life of people so efficient use of electricity is very much necessary. To optimize the consumption of electricity, there is a need for electricity forecasting. Making a cost-efficient electricity management system is a true intention behind the prediction of electricity. Various approaches can be implemented like machine learning, deep learning algorithms for forecasting of electricity. Machine learning algorithms are proved very much efficient in different applications related to forecasting. There are various researches in load forecasting using different methods. Analyzed different methods of different authors to get better results in terms of accuracy;cost-efficiency and robustness. Various parameters are considered to achieve higher accuracy like temperature, environmental conditions. To study the performances of different machine learning and deep learning algorithms several types of errors are calculated such as RMSE (root mean square error), MAE (mean absolute error), MAPE (mean absolute percentage error). These errors define the accuracy of particular algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electric power utilization

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Co-creative Social Media Features on Video Platforms, and Their Impact on Customer Relationships

Co-creative Social Media Features on Video Platforms, and Th...

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4th International Conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Matsumae, Akane Zhang, Yumeng Kyushu Univ Fac Design Minami Ku 4-9-1 Shiobaru Fukuoka Fukuoka Japan Kyushu Univ Grad Sch Design Minami Ku 4-9-1 Shiobaru Fukuoka Fukuoka Japan

ISBN: (纸本)9783030680176;9783030680169

The rapid development of internet technology has allowed video platforms to implement co-creative social media functions (SMFs). This study examined the impact of these co-creative SMFs on user relationships by analyzing the relationship between their social characteristics and user acquisition and retention on typical video platforms. The authors conducted structured questionnaires and non-structured interviews to examine the relation between SMFs and user relationships. Based on the results of the questionnaires and interviews, the authors conducted a correlation analysis on the relation between types of SMFs, social characteristics, and user relationships. The results show that the social characteristics of helping others and asking for help are correlated with user acquisition, and the social characteristic of information acquisition is strongly correlated with both user acquisition and user retention on video platforms. The knowledge obtained from this study will contribute to building desirable user relationships on video platforms through social media functions.

关键词： Social media features Social characteristics Customer relationships Co-creative platforms

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A Comparative Analysis of DNA Protein Synthesis for Solving Optimization Problems: A Novel Nature-Inspired Algorithm 1

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11th International Conference on Innovations in Bio-Inspired computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020

作者： Al-Janabi, Samaher Alkaim, Ayad F. University of Babylon Babylon Iraq University of Babylon Babylon Iraq

ISBN: (数字)9783030736033

ISBN: (纸本)9783030736026

In this paper, we present a new algorithm to find the optimal proteins generated through DNA synthesis. The algorithm executes in five stages: in the first stage, it takes a DNA sequences and consider it as the initial populations of lions, determined the main positions of each lion and the main distances among lions and goal point then consider this distance as fitness of that lions, after that sort the lions based on their fitness to preparing it to the second stage. The second stage develops lion optimization algorithm (LOA) by adding four new features on it, each feature performance one task, a replacing the kernel of LOA (i.e., searching machnizam) by spirally searching & Bubble net searching to increase the accuracy, at the same time reduce the execution time to reach of the goal achieve by A Smart feature. The main purpose of the third stage is determining lion active or more yauld where each lion in population need update the positions and fitness after each move in searching space to reach of their goal., this achieved through Yauld feature. The fourth stage applies the Cooperative features to convert the active sequence of DNA (i.e., Yauld lion) into mRNA after that built tRNA from it after splitting it into triplet to start to generate the proteins. Synthesis of all triplet of tRNA to generated final proteins result by new optimization algorithm achieved based on deep composite that satisfies the four rules, this feature called Deep feature and represent the final stage of the algorithm. The new algorithm appears as a pragmatic optimization model, it proves their robust to work with dynamic length of DNA sequence. It increases accuracy and reduces execution times. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DNA

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Lean Manufacturing Production Management Model Under a Change Management Approach to Enhance Production Efficiency of Textile and Clothing SMEs

Lean Manufacturing Production Management Model Under a Chang...

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4th International Conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Heredia-Mercado, Reddy Flores-Pinas, Shaly Chavez, Pedro Raymundo, Carlos Univ Peruana Ciencias Aplicadas Ingn Ind Lima 15023 Peru Univ Peruana Ciencias Aplicadas Direcc Invest Lima 15023 Peru

ISBN: (纸本)9783030680176;9783030680169

The combination of Kaizen technologies and Total Productive Maintenance (TPM) systems is proposed with the introduction of elements of change management, which will enable to improve productive efficiency. Kaizen technologies enable waste reduction throughout the manufacturing process, and this is translated into a well-organized and standardized environment. Meanwhile, the TPM processes will allow to reduce losses related to machine and equipment breakdowns as well as quality and costs in industrial production processes. The company object of this investigation is a small- and medium sized enterprise engaged in the manufacture of blouses that has production issues, which causes a high level of non-fulfilment of orders. As a result from applying the selected tools, failure to deliver the orders decreased from 12% to 5%;hence, production efficiency increased from 88% to 95%.

关键词： Lean manufacturing Textile Smes Change management

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Development of Colorization of Grayscale Images Using CNN-SVM 1

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2nd International Conference on Innovative Technology, Engineering and Sciences, iCITES 2020

作者： Abualola, Abdallah Gunawan, Teddy Surya Kartiwi, Mira Ambikairajah, Eliathamby Habaebi, Mohamed Hadi Electrical and Computer Engineering Department International Islamic University Malaysia Gombak Malaysia School of Electrical Engineering and Telecommunications UNSW Sydney Australia Information Systems Department International Islamic University Malaysia Gombak Malaysia

ISBN: (数字)9783030709174

ISBN: (纸本)9783030709167

Nowadays, there is a growing interest in colorizing many grayscales or black and white images dating back to before the colored camera for historical and aesthetic reasons. Image and video colorization can be applied to historical images, natural images, astronomical photography. This paper proposes a fully automated image colorization using a deep learning algorithm. First, the image dataset was selected for training and testing purposes. A convolutional neural network (CNN) was designed with several layers of convolutional and max pooling. Support Vector Machine (SVM) regression was used at the final stage. The proposed algorithm was implemented using Python with Keras and Tensorflow libraries in Google Colab. Results showed that the proposed system could predict the colored image from the training process’s learning knowledge. A survey was then conducted to validate our findings. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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