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Data-Driven Decision-Making in IT Projects: A Regression Application in software Estimation

Data-Driven Decision-Making in IT Projects: A Regression App...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Olla Bulkrock Ahmed BaniMustafa Abdallah Qusef Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

IT-related projects are among the most challenging to manage due to their inherent complexity, uncertainty, and the intangibility of their outcomes. However, a data-driven decision-making approach can help tackle these challenges and increase the chance of IT project success. Machine learning techniques can be used to predict uncertain aspects of IT projects, such as the estimation of time, cost, and effort, when applied to historical data collected from previous or similar projects. This approach can allow the project manager to make informed decisions based on facts rather than human judgment or hunch. In this work, we review the potential of data-driven approaches in the management of IT projects, and we demonstrate a use case that applies three regression algorithms, including polynomial regression, linear regression, and partial least squared (PLS), to predict software effort estimation based on the NASA93 public dataset. The best results were achieved using polynomial regression which scored 82% in R 2 , 81% in MAPE, 152 in MAE, 276 in RMSE and 76387 in MSE metrics.

关键词： Measurement Analytical models Reviews Computational modeling Decision making software algorithms Estimation Project management Polynomials software

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A Deep Learning-Based Spectral Efficiency Maximization in Multiple Users Multiple STAR-RISs Massive MIMO-NOMA Networks

A Deep Learning-Based Spectral Efficiency Maximization in Mu...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Science and Engineering International University - Vietnam National University HCM City Vietnam Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we investigate a deep learning-based spectral efficiency maximization in multiple users multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks, where multiple STAR-RISs are deployed to assist a base station (BS) in communicating with multiple near and far users simultaneously. To improve spectral efficiency (SE), we formulate a joint optimization of the precoding matrix, the phase shift of reflection and transmission of the STAR-RIS problem subject to the individual quality of services (QoS) of each user, maximum power budget at BS and the discrete phase shift. The formulated problem belongs to the class of mixed-integer non-convex optimization problem, which is difficult to solve optimally. We first relax the formulated problem and then transform it into an equivalent non-convex one but with a more tractable form. We then apply the inner approximation method to approximate the non-convex parts iteratively. Towards real-time optimization, we propose a novel deep learning framework with a convolutional neural network (CNN) model to predict the downlink SE based on users’ position information and channel gains. Numerical results show that the proposed approach improves the SE compared to the massive MIMO systems with conventional RIS. Besides, the DL framework for the proposed scheme predicts the optimal SE within a short time compared to the conventional approach.

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Smart environmental solution for modern urbanization using mobile application. 3

Smart environmental solution for modern urbanization using m...

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3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Fahim, Wasik Ahmmed Sarkar, Puza Rani Tonmoy, Shakib Salim Pranto, Atiqur Rahman Islam, Md. Ashiqul Khatun, Tania Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9780738111834

Nowadays modern civilization having a huge development in technology but we do not get obviate pollution due to the huge amount of population. Behind this, the most of the problem is continuously throwing the garbage's outside and have become stuck up anywhere. There are tons of applications supported the environment and pollution but don't have any effective solution. This paper attempts to develop a flutter based Mobile Application with Google maps API to help the users to reduce the pollution in their locality. User can mark the locations of garbage and can also mail or call the closest authority. In order that the authority can clean or take necessary steps counting on the user's report. For increasing people's awareness, it shows real-time air quality and pollution level supported on web scrapping data from different websites and IoT based systems. During this modern technology era, mobile applications are the foremost popular. So this application may be a very effective to reduce pollution by public awareness or by making communication between user and the authority. © 2021 IEEE.

关键词： Internet of things

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Exploiting Impact of Eavesdropping Attacks on Secrecy Performance in WPT-based Secure Multi-hop Transmission

Exploiting Impact of Eavesdropping Attacks on Secrecy Perfor...

引用

International Conference on Ubiquitous and Future Networks, ICUFN

作者： Kyusung Shim Beongku An School of Computer Engineering & Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we study the impact of various eaves-dropping attacks on the secrecy performance in wireless power transfer (WPT)-based secure multi-hop transmission. Since each node has a limited power supply, each node harvests energy from the power beacon before data transmission. After then, each node fully consumes the harvested energy for data transmission. Meanwhile, according to the nature of the wireless medium, eavesdroppers can overhear the confidential message at each hop. In order to exploit the relationship between network parameters and secrecy performance, we derive the closed-form expression for secrecy outage probability (SOP) with different eavesdropping attacks, respectively. From the numerical results, we show that the Monte-Carlo simulation and analysis results are tightly matched. Additionally, we evaluate and discuss the impact of the power beacon’s transmit power, the number of eavesdroppers, and time switching ratio on the secrecy performance.

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Deep Learning-based Spectral Efficiency Maximization in Massive MIMO-NOMA Systems with STAR-RIS

Deep Learning-based Spectral Efficiency Maximization in Mass...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Yushintia Pramitarini Kyusung Shim Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Science and Engineering International University - Vietnam National University HCM City Vietnam School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

This paper studies a deep learning-based framework for spectral efficiency maximization problem in massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) systems with simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). We formulate the spectral efficiency maximization with a joint design of power allocation of the users, phase shift matrix of transmission and reflection element at the STAR-RIS. Since the problem is non-convex and power allocation of the users and reflector/transmitter elements at a STAR-RIS are coupled, it is very challenging to solve optimally. We propose a low-complexity iterative algorithm based on the inner approximation (IA) method to solve this problem with guaranteed convergence at a relatively optimal level. For real-time optimization, we design a deep learning (DL) framework to predict the optimal solution of power allocation of users, phase shift matrix of transmission and reflection elements at the STAR-RIS according to distances and channel gains from the base station (BS) to STAR-RIS and from STAR-RIS to users. Simulation results show that the suggested scheme improves the spectral efficiency (SE) compared to the massive MIMO system with direct link and without STAR-RIS. Besides, the DL framework can predict the optimal solution within a short time under the suggested scheme.

关键词： Deep learning Base stations Spectral efficiency Simulation Massive MIMO Reflection Real-time systems

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Enhancing DNA Sequence Classification Through Machine Learning: A Comparative Study on Human and Chimpanzee Genomic Data

Enhancing DNA Sequence Classification Through Machine Learni...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Kushagra Pandya Heli Hathi Ravikumar R N Sivakumar Nadarajan Manoj Praveen Vinod Kumar Pal Associate Software Engineer - AI TSS Consultancy Pvt. Ltd. Rajkot India Dept. of Computer Engineering Marwadi University Rajkot India Dept. of Computer Science and Engineering Alliance University Bangalore India Dept. of AI & DS Velalar College of Engineering and Technology Erode India Dept. of Computer Science Marwadi University Rajkot India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

DNA sequence classification is a major challenge in biological processing of data. The classification of DNA sequences is an important study field in bioinformatics since it allows researchers to perform genomic analysis. Through this research, we tried to analyze the performance of various machine learning (ML) algorithms in identifying DNA genes in humans and chimpanzees to find the most effective algorithm for classification of DNA sequences. The results of this research outperformed earlier studies, which had an accuracy around 98%. The dataset was preprocessed using k-mer counting, which assisted in finding key characteristics for the model's training. Naïve Bayes stood out, with an outstanding accuracy of 98.4% for human DNA and 91.4% for chimpanzee DNA. After improving the parameters, Naïve Bayes continued to improve, with human DNA accuracy increasing to 99% and chimpanzee DNA accuracy to 92% in all categories followed by Logistic Regression at 93.9% for human DNA sequences and Ridge Classifier at 90.2% for chimpanzee DNA sequences. K-Nearest Neighbors, Ridge Classifier, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extra Trees, XGBoost, and Naïve Bayes were among the algorithms tested, with accuracy, F1 score, precision, and recall used in assessing their performance. These findings highlight the necessity of selecting the appropriate models and tweaking their parameters to improve performance.

关键词： Logistic regression Accuracy Machine learning algorithms Computational modeling Genomics Classification algorithms Bayes methods Bioinformatics Random forests Tuning

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Sinhala Sign Language Learning System for Hearing Impaired Community

Sinhala Sign Language Learning System for Hearing Impaired C...

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2023 4th International Informatics and software engineering Conference (IISEC)

作者： Idushan K. A. S Dilshan P. G. T Jayasundera P. S Madhushan K. L. B Jenny Krishara Dinuka Wijendra Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-to-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users.

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Exploiting Secure Multihop Transmission in Underlying Cognitive Radio Networks: Analysis and Deep Learning Approaches

Exploiting Secure Multihop Transmission in Underlying Cognit...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

This paper studies the impact of the eavesdropping attacks on secure multihop transmission in cognitive radio networks. When secondary users transmit confidential information, the eavesdroppers overhear this information collaboratively and independently, respectively. We derive the exact closed-form expression for secrecy outage probability (SOP) with different eavesdropping attacks. Additionally, we propose the deep neural network (DNN)-based framework to capture the relation between input and output. The numerical results show that a colluding attack can wiretap more information compared to a non-colluding attack. From the Monte-Carlo simulation, the closed-form expression and DNN-based framework are verified. The effects of the interference threshold, the number of hops, the number of eavesdroppers, and the secrecy target data rate are investigated, respectively.

关键词： Deep learning Analytical models Closed-form solutions System performance Neural networks Spread spectrum communication Probability

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Intrusion Detection System (IDS) of Multiclassification IoT by using Pipelining and an Efficient Machine Learning

Intrusion Detection System (IDS) of Multiclassification IoT ...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Layth Rafea Hazim Abdulrahman Ahmed Jasim Oguz Ata Muhammad Ilyas College of Computer Science and Mathematics Tikrit University Tikrit Iraq Dept. of Electrical and Computer Engineering Altinbas University Istanbul Turkey College of Engineering Al-Iraqia University Baghdad Iraq Dept. of Software Engineering Altinbas University Istanbul Turkey Dept. of Computer Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350316926

ISBN: (纸本)9798350316933

The Internet of Things (IoT) has quickly advanced and been incorporated into many different fields. With the use of IoT technology, gadgets can receive, process, and send data automatically. IoT has been rapidly accepted in many important fields since it makes life easier and increases service quality, yet it still faces significant privacy and security problems. An Intrusion Detection System (IDS) could be implemented as a security feature to protect IoT networks from a variety of cyberattacks. This study suggests using IDS to defend against a wide range of cyberattacks on IoT systems. The suggested approach makes use of the Multi-layer Perceptron (MLP) as well as Extra Trees (ExT) as efficient algorithms of classification. Also, the study uses the pipeline to put together several cross-validated phases while selecting various parameters to increase the detection rate. One dataset is utilized for evaluating and analyzing the performance outcomes so as to validate the efficiency of the suggested IDS approach. The evaluation findings show that the suggested IDS methods may greatly increase detection performance results concerning accuracy rate, precision, F1-score, and recall while also improving detection efficiency.

关键词： Measurement Privacy Correlation Redundancy Intrusion detection Machine learning Security

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Kamen Shah Maria Pantoja Dept. Computer Science and Software Engineering California Polytechnic State University San Luis Obispo USA

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals.

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