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Information technology, Applied Mathematics and Statistics (ICITAMS), International Conference on

作者： Suphian Mohammed Tariq Intisar Shadeed Al-Mejibli Computer Engineering Informatics Institute for Postgraduate Iraqi Commission for Computers & Informatics College Of Engineering Al-Iraqia University Baghdad Iraq Biomedical Informatics College University of Information Technology and Communications Baghdad Iraq

ISBN: (数字)9798350335118

ISBN: (纸本)9798350335125

Specifying the location of wireless sensor devices is vital in many applications such as target tracking, remote environmental monitoring, and more. Time of arrival (TOA), the received signal strength indicator (RSSI) and range information are applied to locate the wireless sensor network (WSN) node’s location. Still, they suffer inaccuracy because of environmental factors in the 2D region. A ranging method has been suggested, merging the TOA information domain with RSSI to accurately determine the distance between the unknown node and the anchor node. This approach relies on utilizing RSSI and TOA ranging techniques in the near and far endings. We combine received signal strength indication and arrival time in parallel style as major elements. The findings of the two methods are then combined with the error percentage to determine the final location using adaptive weight errors. The proposed range method is promoted in this work to enhance the accuracy of the positioning. The proposal has been implemented by MATLAB simulation and the results show improving positioning accuracy compared to the conventional TOA and RSSI ranging method. The mean square error in the proposed method is 0.5044, whereas it was 0.7032 and 0.8102 in RSSI, respectively. This indicates that the suggested approach is more efficient and performs better for localization than the other two methods.

关键词： Location awareness Wireless communication Wireless sensor networks Target tracking Simulation Merging Mean square error methods

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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2023 International Seminar on Application for technology of Information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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A Novel Framework for Preserving Patient Health Record in IoT Healthcare

A Novel Framework for Preserving Patient Health Record in Io...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Mohammad Riyaz Belgaum Priyanka Pothureddy Sai Suraksha Patnam Parimala Deepthi Gadda Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

As artificial intelligence, the Internet of Things (IoT), and next-generation mobile communication continue to evolve at a rapid pace, an increasingly popular application called the Internet of Medical Things (IoT) has arisen to provide ease and practicality in healthcare. Nonetheless, privacy preservation issues are brought up by accessing patient medical information inside the IoT framework, which calls for the creation of safe and private-preserving solutions. To address these issues, we provide in this study an IoT-Framework for preserving patient health record (FFPPHR) based on ELGamal blind signatures. We provide an enhanced privacy protection medical record searching technique (FFPPHR) based on the elliptic curve discrete logarithm problem (ECDLP) and point out security vulnerabilities in the current FFPPHR. Our security research shows that by utilizing the hard problem assumption of ECDLP, the FPMR guarantees identity privacy and accuracy. Furthermore, the use of Blind ECDSA Signatures improves data security. Performance assessments confirm the FFPPHR efficiency, and theoretical findings agree with simulations. The suggested FFPPHR approach yields lower time costs when compared to state-of-the-art techniques, which makes it a potential option for safe and effective medical record searches in IoT contexts.

关键词： Privacy Costs Elliptic curves Data security Internet of Medical Things Search problems Mobile communication

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Securing Electric Vehicles Against CAN Bus Replay Attacks: A Message Authentication Approach

IEEE Communications Standards Magazine

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IEEE Communications Standards Magazine 2024年第4期8卷 88-95页

作者： Yunlong Sun Shancang Li Muddesar Iqbal Fatma Taher University of Electronic Science and Technology of China China School of Computer Science & Informatics Cardiff University UK College of Engineering Prince Sultan University Saudi Arabia College of Technical Innovations Zayed University UAE

Controller Area Network (CAN) bus is a critical communication system of an automobile, which enables various electronic control units (ECU) to communicate to each other and share information. CAN faces security vulnerabilities due to its limited security features and broadcast nature. This article investigated replay attacks on CAN bus and proposed an integrated intrusion prevention systems (IPS) for active defense against CAN bus attacks. Specifically, a secure message authentication scheme was introduced to verify and authenticate and freshness and integrity of message received on CAN bus against replay attack, in which each message were a freshness value and MAC tag were attached with the workload. Experimental results demonstrate the effectiveness of proposed scheme.

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How we can use text classification in the Back-Office environment of a bank as 'business as usual' solution 17

How we can use text classification in the Back-Office enviro...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Krutilla, Zsolt Kovari, Attila Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty buda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary University of Dunaújváros Institute of Engineering Department of Natural Science Dunaújváros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary

ISBN: (纸本)9798350321104

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a 'business as usual' (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models. © 2023 IEEE.

关键词： Information retrieval systems

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S Priyadharshini G. Naveen Sundar D. Narmadha Senthil Kumar Thangavel Adeyemi Abel Ajibesin Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa Department of Mathematics and Computer Science Ashland University USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future.

关键词： Training Emotion recognition Accuracy Computational modeling Speech recognition Speech enhancement Predictive models Feature extraction Data models Convolutional neural networks

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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Comparative analysis of Start-up Success Rate Prediction Using Machine Learning Techniques

Comparative analysis of Start-up Success Rate Prediction Usi...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Mohammad Riyaz Belgaum Ediga Kasi Viswanath Pavukolla Chakradhar Ediga Chenna Kesava Goud Kuruva Chandu Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Software startups create innovative products or services using programming, which can lead to a need for products from potential customers. Researchers suggest that trial and error methodology can increase the success rate of these organizations by evaluating assumptions about client needs before fostering a product. However, programming new businesses typically do not require trial and error. The study aims to predict the success of startup companies in their early development stages by analyzing their success based on initial funding. A study of 106 unsuccessful software start-ups was conducted to predict the success rate. The experimentation progression model (X Pro) was developed to show that effective experimentation requires understanding trial and error, setting objectives, conducting tests, analyzing results, and acting based on new knowledge. The findings provide insights into supporting procedures and approaches that enhance trial- and-error reception in developing new business models. Researchers can become aware of potential factors leading to startup disappointment and take precautions to avoid them. The research focuses on using web-based open sources, including social networking services and the web, to model the task. The study uses machine learning techniques like Random Forest, Gradient Boost Classifier, XG Boost, and Ada Boost Classifier. The analysis provides insights into the types of useful signals discovered on the web and market mechanisms underlying the funding process. This approach offers a more comprehensive approach to predicting startup success.

关键词： Analytical models Accuracy Social networking (online) Machine learning Companies Predictive models Programming

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhandarkar, Vishakha D. Rongali, Aruna Sri Shamout, Mohamed Dawood Pund, Sachin Nama, Gigih Forda Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities India University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Doctoral Program of Environtmental Science University of Lampung Department of Informatics Bandar Lampung Indonesia Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

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Data-Driven Online Resource Allocation for User Experience Improvement in Mobile Edge Clouds

arXiv

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arXiv 2024年

作者： Fu, Liqun Tong, Jingwen Lin, Tongtong Zhang, Jun The School of Informatics Xiamen University Xiamen361005 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

As the cloud is pushed to the edge of the network, resource allocation for user experience improvement in mobile edge clouds (MEC) is increasingly important and faces multiple challenges. This paper studies quality of experience (QoE)oriented resource allocation in MEC while considering user diversity, limited resources, and the complex relationship between allocated resources and user experience. We introduce a closed-loop online resource allocation (CORA) framework to tackle this problem. It learns the objective function of resource allocation from the historical dataset and updates the learned model using the online testing results. Due to the learned objective model is typically non-convex and challenging to solve in real-time, we leverage the Lyapunov optimization to decouple the long-term average constraint and apply the prime-dual method to solve this decoupled resource allocation problem. Thereafter, we put forth a data-driven optimal online queue resource allocation (OOQRA) algorithm and a data-driven robust OQRA (ROQRA) algorithm for homogenous and heterogeneous user cases, respectively. Moreover, we provide a rigorous convergence analysis for the OOQRA algorithm. We conduct extensive experiments to evaluate the proposed algorithms using the synthesis and YouTube datasets. Numerical results validate the theoretical analysis and demonstrate that the user complaint rate is reduced by up to 100% and 18% in the synthesis and YouTube datasets, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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