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Using the recurrence plots as indicators for the recognition of Parkinson’s disease through phonemes assessment

Using the recurrence plots as indicators for the recognition...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vasileios Skaramagkas Anastasia Pentari Dimitrios I. Fotiadis Manolis Tsiknakis Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Institute of Computer Science Foundation for Research and Technology Hellas (FORTH) Heraklion Crete Greece Dept. of Biomedical Research Institute of Molecular Biology and Biotechnology (FORTH) Ioannina Greece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece

Parkinson’s disease (PD) is considered to be the second most common neurodegenerative disease which affects the patients’ life throughout the years. As a consequence, its early diagnosis is of major importance for the improvement of life quality, implying that the severe symptoms can be delayed through appropriate clinical intervention and treatment. Among the most important premature symptoms of PD are the voice impairments of articulation, phonation and prosody. The objective of this study is to investigate whether the voice’s dynamic behavior can be used as possible indicator for PD. Thus in this work, we employ the recurrence plots (RPs) which derive from the analysis of the three modulated vowels /a/, /e/ and /o/, which belong to the PC-GITA dataset, and are fed as input images to a 3-channel Convolutional Neural network-based (CNN) architecture, which, finally, differentiates the 50 PD patients from 50 healthy subjects. The experimental results obtained provide evidence that the RP-based approach is a promising tool for the recognition of PD patients through the analysis of voice recordings, with a classification accuracy achieved equal to 87%.

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A CNN Based Approach to Classify the Folk Dances of Odisha

A CNN Based Approach to Classify the Folk Dances of Odisha

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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Samantaray, Swati Patra, Sudhansu Shekhar Mohanty, Amlan Barik, Lalbihari Barik, Akash Brahma, Aditya Narayan Kiit Deemed to Be University School of Humanities Bhubaneswar India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India Iiit Department of Computer Science Bhubaneswar India King Abdulaziz University Faculty of Computing & Information Technology in Rabigh Department of Information Systems Saudi Arabia New Horizon College of Engineering Dept. of Computer Science Bengaluru India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Folk dances are a primordial expression of ecstasy and bliss and serve as a mode of communication. They also facilitate in keeping the people connected to their traditions as well as ancestry. Furthermore, these dances help in preserving cultural unity and their tales can often reveal a lot about the periods these dances have developed through. Mahari, Gotipua and Odissi are such folk dance forms of the eastern state of Odisha. These folk dances are on the verge of extinction owing to the reducing number of practitioners, audience, absence of documentation, and digitalization. The absence of documentation has presented a barrier to the legitimacy of folk dance as an academic field of study. This research paper intends to focus on the classification of the folk dances of Odisha such as Mahari, Gotipua and Odissi which will help them to digitize and preserve. We propose deep learning-based methods for the classification and then it can be preserved. To validate the poses and hand motions, we offer a convolutional neural network model (CNN). © 2022 IEEE.

关键词： Neural network models

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Comparing SWIPT Techniques for Zero-Energy RIS

Comparing SWIPT Techniques for Zero-Energy RIS

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IEEE International Conference on Communications Workshops, ICC

作者： Dimitrios Tyrovolas Vasilis K. Papanikolaou Sotiris A. Tegos Yue Xiao Panagiotis D. Diamantoulakis Sotiris Ioannidis Christos Liaskos George K. Karagiannidis Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering Technical University of Chania Chania Greece School of Information Science and Technology Southwest Jiaotong University Chengdu China Computer Science Engineering Department University of Ioannina Ioannina and Foundation for Research and Technology Hellas (FORTH) Greece Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

Providing enhanced quality of service (QoS) to support demanding applications in the most energy-efficient way has been identified as a primary objective of future 6G networks. Towards this direction, the concept of programmable wireless environments (PWEs) has emerged, wherein extremely high QoS can be ensured by controlling the wireless propagation through reconfigurable intelligent surfaces (RIS). To meet the demands of 6G networks in terms of energy efficiency, in this paper, zero-energy RISs (ZERISs) are introduced, which harvest the required amount of energy for their operation and then contribute to the PWE conversion. More specifically, we investigate the performance of a ZERIS-assisted communication system in terms of joint energy-data rate outage probability, which quantifies the trade-off between the harvested energy and information transmission, for three distinct simultaneous wireless information and power transfer (SWIPT) methods are investigated, namely power splitting, time switching, and element splitting. Finally, simulation results validate the derived analysis, leading to useful insights for the ZERIS-assisted network regarding which energy harvesting method achieves better performance in terms of joint energy-data rate outage probability.

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Impact of Dataset Balancing on Machine Learning-Based Intrusion Detection systems

Impact of Dataset Balancing on Machine Learning-Based Intrus...

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IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)

作者： Muhammad Iqbal Yusri Mohamed Hadi Habaebi Teddy Surya Gunawan Hasmah Mansor Mira Kartiwi Levy Olivia Nur Electrical and Computer Engineering Dept. International Islamic University Malaysia Kuala Lumpur Malaysia Information Systems Department International Islamic University Malaysia Kuala Lumpur Malaysia School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350365283

ISBN: (纸本)9798350365290

Intrusion Detection systems (IDS) are indispensable for cybersecurity, as they safeguard networks from increasingly sophisticated and sophisticated cyberattacks. This paper assesses the influence of dataset balancing on the performance of machine learning-based IDS, thereby addressing the challenge of imbalanced data in detecting network intrusions. We concentrate on three IDS implementations: Tree-based Intelligent IDS, Multi-Tiered Hybrid IDS (MTH-IDS), and Leader Class and Confidence Decision Ensemble (LCCDE). We utilized the Synthetic Minority Over-Sampling Technique (SMOTE) to balance data and implemented feature selection and hyperparameter optimization to improve the model's performance using the CICIDS 2017 dataset. The LCCDE model exhibits the highest performance, as our comparative analysis demonstrates that the combination of SMOTE and feature selection enhances the F1 scores. The results underscore the significance of advanced ensemble techniques and data preprocessing in developing resilient IDS. This research emphasizes the necessity of ongoing optimization and evaluation of IDS models to guarantee effective protection against the development of cyber threats.

关键词： Heuristic algorithms System performance Roads Intrusion detection Feature extraction Real-time systems Delays

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Advanced Machine Learning Techniques for Enhancing Data security in Cloud Computing systems

Advanced Machine Learning Techniques for Enhancing Data Secu...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： M Dhinakaran M Sundhari S. Ambika V. Balaji R.Thalapathi Rajasekaran Department of Electronics and Communication Engineering Galgotias College of Engineering & Technology Greater Noida Uttar Pradesh India Dept. of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India Department of Information Technology E. G. S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of CSE (Cyber Security) Easwari Engineering College Chennai India Dept. of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

In the age of cloud computing, data stored and processed in the cloud must be secure. This study examines how powerful machine learning can secure cloud computing data. Three trials assessed different machine learning models in this setting. Experiment 1 used a Random Forest model and achieved 95% accuracy, 0.92 precision, 0.96 recall, and 0.94 F1 Score. This shows that the model can accurately categorize security threats with a good balance of true and false positives. A Deep Neural network (DNN) improved accuracy to 97% in Experiment 2. Precision, recall, and F1 Score values of 0.94, 0.98, and 0.96 demonstrate the DNN’s ability to discriminate threats from normal activity. The model captures complicated patterns well, making it a powerful cloud security tool. security analysis using reinforcement learning, specifically Q-learning, was introduced in Experiment 3. The model’s 88% detection rate showed its capacity to identify threats, but its 0.05 false positive rate created a tradeoff between true and false positives. With the 0.12 false negative rate, one can infer improvements in threat detection accuracy. These results indicate that state-of-the-art machine learning is capable of protecting cloud data. The Random Forest and Deep Neural network models have very high accuracy balanced with reasonable precision-recall trade-offs, whereas reinforcement learning through Qlearning shows promise but needs modification to improve the model’s performance in terms of both accuracy and false positive rates. While rising threats need constant adaptation and learning, the model should also fulfill security needs. This study helps formulate a secure cloud computing infrastructure that can stand threats of change.

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Trusted Access to 6G Testbeds Through a security Intent-Driven Software-Defined Perimeter Framework

Trusted Access to 6G Testbeds Through a Security Intent-Driv...

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

作者： Carolina Fernández-Martínez Anastasios Bikos Christos Verikoukis Shuaib Siddiqui i2CAT Foundation Barcelona Spain Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Iquadrat Informatica SL Barcelona Spain Dept. of Signal Theory and Communications Universitat Politècnica de Catalunya Barcelona Spain Industrial Systems Institute / Athena Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

Parallel to the evolution of recent trends in the cybersecurity industry and the increase of cyberattacks in the last few years, there is renovated interest on the application of software-defined techniques to enforce best security practices, such as in the Software-Defined Perimeter specification and the Zero Trust architectures. This work proposes a software-defined network segmentation and access control framework aligned with these principles. This solution captures minimal security requirements from testbed operators to establish ad-hoc, isolated connectivity and access control for end-users (experimenters) interacting with exposed endpoints from the exposed services in the cloud testbeds. The elicited architecture, design decision and initial implementation are introduced.

关键词： Access control Industries Conferences Computational modeling computer architecture Market research Ad hoc networks Zero Trust Software defined networking computer crime

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Effective DGA Family Classification Using a Hybrid Shallow and Deep Packet Inspection Technique on P4 Programmable Switches

Effective DGA Family Classification Using a Hybrid Shallow a...

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IEEE International Conference on Communications (ICC)

作者： Ali AlSabeh Kurt Friday Jorge Crichigno Elias Bou-Harb College of Engineering and Computing University of South Carolina (USC) Columbia South Carolina USA Information Systems and Cyber Security Dept. The Cyber Center For Security and Analytics University of Texas at San Antonio (UTSA) San Antonio Texas USA

Domain Generation Algorithms (DGAs) are one of the most effective strategies for malware to obtain a connection with the adversary's Command and Control (C2) server. Moreover, the growing number of DGA families makes it increasingly challenging for defense strategies to promptly identify the DGA family behind a given compromise. State-of-the-art high-dimensional DGA detection models perform poorly in such multiclass classification scenarios because their domain name-based features fail to distinguish between DGA families. To this extent, this paper proposes a novel framework that harnesses the flexibility, per-packet granularity, and Terabits per second (Tbps) processing capabilities of P4 Programmable Data Plane (PDP) switches to swiftly and accurately classify DGA families. In particular, the P4 PDP switch is leveraged to extract a combination of unique network heuristics and domain name features through shallow and Deep Packet Inspection (DPI) with minimal throughput reduction. Such collected features cannot be tracked on commodity hardware without significantly degrading the throughput in high-speed networks, nor on traditional layer 2/3 switches due to their limited and fixed functionalities. We crawled hundreds of Gigabytes (GBs) of malware samples from different sources to obtain instances of 50 DGA families and show that the proposed approach can promptly classify each family with high accuracy. Such a reliable multiclass classification enables the immediate halting of malicious communications while allowing network operators to initiate appropriate mitigation, incident management, and provisioning strategies.

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WP-CamemBERT for next location Prediction

WP-CamemBERT for next location Prediction

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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Salah Eddine Henouda Fatima Zohra Laallam Okba Kazar Abdessamed Sassi Houssam Eddine Aouarib Djihane Houfani Dept. Computer Science (LINATI Laboratory) LINFI laboratory of Biskra Kasdi Merbah University Ouargla ALGERIA Dept. Computer Science LINATI Laboratory Kasdi Merbah University Ouargla ALGERIA Dept. Information Systems and Security College of Information Technology United Arab Emirate University UAE Dept. Computer Science Oum El Bouaghi University Oum El Bouaghi ALGERIA Dept. Computer science LINFI Laboratory Mohamed Khider University Biskra ALGERIA

CamemBERT is a Natural Language Processing (NLP) model. The current paper introduced a combination of Bertwordpiece tokenizer and CamemBERT that we named WP-CamemBERT, to solve the next location prediction problem. Substantially, we sought to investigate the performance of our proposed model WP-CamemBERT compared to famous models in the domain that are: Markov, Long-Short Term Memory (LSTMs), Convolutional Neural network (CNN2D), and Support Vector Machine (SVM) models. In order to build the next location prediction model, we used a dataset of mobile users made available through the CRAWDAD project. The results showed that WP-Camembert gave significant improvement in the prediction accuracy and surpassed the state of the art models that are: Markov, LSTMs, CNN2D, and SVM. Particularly, WP-CamemBERT enhanced the next location prediction accuracy by at least 3.2%.

关键词： Support vector machines Deep learning Predictive models Markov processes Natural language processing Convolutional neural networks Task analysis

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In-dept. review of integration of AI in cloud computing 3

In-depth review of integration of AI in cloud computing

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Mohamed, Nachaat Sridhara Rao, L. Sharma, Manju Sureshbaburajasekaranl Badriasulaimanalfurhood Kumar Shukla, Surendra Universiti Sains Malaysia Rabdan Academy UAE Homeland Security Abu Dhabi United Arab Emirates J.B. Institute of Engineering and Technology Dept. of Information Technology Hyderabad India Lecturer Jazan University Jazan Saudi Arabia NVIDIA Corporation Group Product Manager United States College of Computer and Information Sciences Princess Nourahbint Abdulrahman University Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The successful distribution of computer services, including software, storage needs, analytics, intelligence, and many others, is reflected in cloud computing. Additionally, it provides quicker innovation while simultaneously providing flexible *** learning (DL), a subset of artificial intelligence (AI) and machine learning, is widely seen as a key technology of the Fourth Industrial Revolution (4IR or Industry 4.0). The artificial neural network (ANN)-based technique known as deep learning (DL) has gained popularity in the computing world due to its capacity for learning from data. frequently used in a variety of application fields, including cybersecurity, healthcare, visual recognition, and many more. However, because of the dynamic nature and differences in real-world environments, creating an acceptable DL model is a difficult process. issues and information Additionally, because of a lack of fundamental knowledge, DL techniques become black-box devices that hinder standard level progress.A cloud computing system in an organization offers high data security at a cheap maintenance cost. Business cooperation has increased significantly as a result of cloud technology. Use of AI has enhanced each of those procedures. The use of artificial intelligence in certain industries has a considerable impact on how successful cloud services techniques are, resulting in a consequence, the two innovations' cumulative effect boosts the prosperity of such businesses. The use of smart-device and computer vision models improve the effectiveness of public clouds. It has also demonstrated implementing AI into public cloud strategies may benefit enterprises in a variety of *** essay includes a thorough introduction and an insightful literature review. Three objectives are outlined in a methodological approach that has also been given. © 2023 IEEE.

关键词： Intelligent systems

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Authentic Learning on DevOps security with Labware: Git Hooks To Facilitate Automated security Static Analysis

Authentic Learning on DevOps Security with Labware: Git Hook...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Md Mostafizur Rahman MD Abdul Barek Mst Shapna Akter Abm Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Information Technology University of West Florida Florida USA Department of Intelligent Systems and Robotics University of West Florida Florida USA Center for Cyber Security University of West Florida Florida USA Dept. of Computer Science and Software Engineering Auburn University USA Tuskegee University Alabama USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through prac-tical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities.

关键词： Learning systems DevOps Codes Education Static analysis Dynamic scheduling Software

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