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Hybrid Deep Learning Approach for Nonfunctional software Requirements Classifications 3

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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3rd International Conference on Communication, Computing and Digital systems, C-CODE 2023

作者： Rahman, Kiramat Ghani, Anwar Ahmad, Rashid Sajjad, Syed Haseeb International Islamic University Dept. of Software Engineering Islamabad Pakistan Big Data Research Center Jeju National University Jeju Korea Republic of Inernational Islamic University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts. © 2023 IEEE.

关键词： Requirements engineering

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RNN-based Model for an Optimal COVID-19 Cases Detection using Clinical Reports 9

RNN-based Model for an Optimal COVID-19 Cases Detection usin...

引用

9th International Conference on Optimization and Applications, ICOA 2023

作者： Khanday, Akib Bouktif, Salah Ouni, Ali Uae University Cit Dept. Computer Science and Software Engineering Al Ain United Arab Emirates University of Quebec Ets Montreal MontrealQC Canada

ISBN: (纸本)9798350312546

The global impact of COVID-19 has been significant due to its rapid spread and high transmission rate. A large number of individuals have been exposed to this dangerous virus, emphasizing the importance of early detection to potentially save numerous lives. This research paper introduces an enhanced method for COVID-19 detection, utilizing a Recurrent Neural Network (RNN) and leveraging early clinical reports. Specifically, a Long Short Term Memory (LSTM) model is trained using data obtained from the metadata of the available dataset published by the World Health organisation, specifically focusing on extracting clinical reports and labels. Several pre-processing techniques and word embedding are applied to train an LSTM-based classifier that accurately identifies positive COVID-19 cases. The implementation of this proposed approach yielded superior results compared to traditional machine learning algorithms. The model achieved a testing accuracy of 87%, and future works include expanding the dataset to enhance the efficiency of the model. © 2023 IEEE.

关键词： COVID-19

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Enhancing Connectivity in Remote Regions using Adaptive and Resilient UAV-Based Wireless Networks

Enhancing Connectivity in Remote Regions using Adaptive and ...

引用

IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Andrés García–Lopez Diego Ramos–Ramos Santiago García–Gil José Gómez–delaHiz Juan Manuel Murillo Jaime Galán–Jiménez Dept. of Computer Systems and Telematics Engineering University of Extremadura Spain

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

The annual growth of Internet adoption still leaves around 2 billion people in remote areas without connectivity, cutting them off from vital services such as healthcare and distance education. This paper proposes an energy-efficient solution leveraging Unmanned Aerial Vehicles (UAVs) based network architectures to dynamically adapt to hardware failures or battery depletion. By incorporating the software-Defined Networking (SDN) paradigm for real-time network management, this approach ensures swift adaptability to changing environmental conditions. Consequently, the UAV-based network can deliver Internet of Things (IoT) applications to users, significantly improving the quality of life for rural populations. Simulations conducted in realistic scenarios demonstrate a success rate of over 90% in handling user requests across various situations.

关键词： Adaptation models Wireless networks Autonomous aerial vehicles Energy efficiency Real-time systems Hardware Batteries Internet of Things Vehicle dynamics software defined networking

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via Machine Learning Techniques

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

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Emerging Technology (INCET), International Conference for

作者： Amriti Gupta Kunal Nayyar Harshit Chopra Neha Gahlan Divyashikha Sethia Dept. of Software Engineering Delhi Technological University Dept. of Electronics & Communication Engineering Delhi Technological University Dept. of Computer Engineering Delhi Technological University

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Emotions, intricate mental states shaped by neu-rophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. The Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. This paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. This research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. The exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested.

关键词： Emotion recognition Accuracy Sleep Cognitive processes Machine learning Data collection Brain modeling

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A Deep Averaged Reinforcement Learning Approach for the Traveling Salesman Problem

A Deep Averaged Reinforcement Learning Approach for the Trav...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Parasteh, Sirvan Khorram, Amin Mouhoub, Malek Sadaoui, Samira University of Regina Dept. of Computer Science Regina Canada University of Regina Dept. of Industrial Systems Engineering Regina Canada

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

This work presents a deep averaged reinforcement-learning approach to learn improvement heuristics for route planning. The proposed method is tested on the Traveling Salesman Problem (TSP). While learning improvement heuristics using machine learning models are prosperous, these methods suffer from low generalization and forgetfulness of the agents during the training process. We have applied the stochastic weight averaging method during the training phase to solve these issues, which smothers the training convergence and prevents the forgetting of optimized learned policies, and consequently provides better results. The agent can learn the optimized policy while holding a moving average of the previously learned policies during the training epochs. In order to assess the performance of our proposed approach, we conducted comparative experiments considering other known methods from the literature. The results demonstrate our proposed method's superiority in training trends and optimization. © 2022 IEEE.

关键词： Reinforcement learning

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A Comprehensive Threat Model for Autonomous Vehicle Communication Security 6

A Comprehensive Threat Model for Autonomous Vehicle Communic...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Meegammana, Niranjan W. Fernando, Harinda Shilpa Sayura Foundation Cyber Security Research Lab Kandy Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The rapid advancement of autonomous vehicles (AVs) promises to enhance transportation efficiency, reliability, and safety. However, these systems, particularly multi-channel vehicular communications, are vulnerable to cyber attacks that threaten authenticity, authorization, confidentiality, integrity, availability, and non-repudiation. The 2014 Chrysler Jeep hack highlights the urgent need for robust security measures in vehic-ular communications. This study utilizes the STRIDE, DREAD, and MITRE ATT&CK security frameworks to develop a comprehensive threat model that identifies and assesses critical threats and risks to AV communications. By detailing specific attack vectors and their multidimensional impacts and criticality, the study establishes a baseline of threats for communication security in vehicular networks. It produces visual threat maps, impact graphs of various attacks, and a color-coded threat grid to guide stakeholders in developing effective cyber security measures for protecting AV communications, thereby help stakeholders to improve the reliability and safety of AV navigation for fostering growth in the AV industry. © 2024 IEEE.

关键词： Cyber attacks

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Measurement of Ocular Torsion Using Feature Matching on the Iris and Sclera

Measurement of Ocular Torsion Using Feature Matching on the ...

引用

International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Arthur Bell Richard Green Aluna Everitt Dept. of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

The tracking of torsional eye movements, where the eye rotates about the axis of vision, is important for the diagnosis of certain vestibular disorders, but presents additional challenges when compared to the tracking of linear eye movements. This paper proposes a method for the measurement of ocular torsion that combines techniques from several previous studies, attempting to make the measurement robust to a highly off-axis centre of vision and obscurations of the pupil and iris. The proposed method detects feature points across a region of interest (ROI) including both the iris and the sclera (the white part of the eye surrounding the iris), using the Affine-SIFT (ASIFT) feature detection method, and tracks the feature points across frames using the Lucas-Kanade Optical Flow Method. The proposed method was only able to achieve an average error of 0.42 degrees compared to 0.17 degrees achieved in prior research. However, an additional 23.5% of feature points were able to be tracked compared to feature matching on the iris alone, showing that the proposed method has the potential to provide more accurate tracking than conventional methods. Future improvements proposed to the method include a deep segmentation method to automatically determine the ROI in each frame, and further testing with a calibration step to determine the source of error in the method.

关键词： Iris Image segmentation Accuracy Tracking Feature detection Feature extraction Calibration Pupils Optical flow Testing

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A Comparative Analysis of Data Resampling Techniques for Addressing Class Imbalance in Mosquito Classification

A Comparative Analysis of Data Resampling Techniques for Add...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Reshma Pise Abhishek Singh Chauhan Bharati Ainapure Aditya Shenolkar Leena Deshpande Shivraj Jadhav Computer Engineering Dept. Vishwakarma University Pune India CE (Software Engineering) Dept. Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Mosquitoes, as vectors of life-threatening diseases such as dengue, Zika, and West Nile virus, present a significant public health challenge. Precise and rapid identification of mosquito species is crucial for early detection and prevention of these diseases. Deep learning techniques have proven to be highly effective for automatic visual insect classification tasks. However, the imbalanced distribution of mosquito species within training datasets severely impacts the algorithm’s classification performance. This study investigates data sampling solutions for learning from imbalanced mosquito images dataset. We transfer-learn InceptionV3, a pre-trained convolutional neural network to classify images of two vector mosquito species Aedes Aegypti and Anopheles stephensi. The effectiveness of these data sampling solutions is evaluated and compared in terms of their impact on the performance of CNN classifier.

关键词： Training Visualization Technological innovation Accuracy Sensitivity Prevention and mitigation Vectors Convolutional neural networks Viruses (medical) Diseases

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Using a Multi-Agent System to Better Simultaneous Collaboration software systems 2

Using a Multi-Agent System to Better Simultaneous Collaborat...

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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： Abd Alati Elfargane, Rabeia N. Yousef, Khamisa. A. Saleh, Eiman M. Abd Elati, Naeima N Benghazi University Dept. Computer Science Benghazi Libya University of Tobruk Dept. Computer Science Tobruk Libya Benghazi University Dept. Software Engineering Benghazi Libya Benghazi University Dept. Statistics Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

In each field, there is a need for better collaborative efforts among team members. The software development community is currently concentrating its efforts on developing tools that will make this endeavor easier. As a result, the two major purposes of a collaborative system are to solve the lack of community consciousness (since group members must be aware of one another) and to maintain flooring directive (i.e. provide means to manage access to shared resource in synchronous groupware systems). The goal of this study is to design a dynamic, extendable, and adaptable framework capable of handling challenges in synchronous community consciousness systems in order to solve these concerns. The major purpose of this study is to improve Simultaneous Collaboration software systems utilizing the structure analysis real-Time approach (SART), which is based on a multiagent system (MAS)). © 2022 IEEE.

关键词： Multi agent systems

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Towards Accurate and Certain Molecular Properties Prediction 13

Towards Accurate and Certain Molecular Properties Prediction

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Ham, KyungPyo Yoon, JeongNoh Sael, Lee Ajou University Dept. of Artificial Intelligence Suwon Korea Republic of Ajou University Dept. of Software and Computer Engineering Dept. of Artificial Intelligence Suwon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this review, we survey the fundamental problems and their solvers in molecular property prediction problems: graph neural networks modeling and uncertainty estimation. Molecules are naturally represented with graphs, thus graph neural networks, such as graph convolution networks and several types of message passing networks, are their natural models for accurate predictions. However, graph neural networks and any other deep learning models require a large amount of data. Unfortunately, molecular data are insufficient and skewed. Thus, not just an accurate method but models that return uncertainty estimations, such as Bayesian estimations, deep ensembles, and evidential methods, are required. © 2022 IEEE.

关键词： Graph neural networks

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