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2019 International Conference on Biomedical Innovations and Applications, BIA 2019

作者： Naronglerdrit, Prasitthichai Mporas, Iosif Perikos, Isidoros Paraskevas, Michael Kasetsart University Sriracha Campus Dept. of Computer Engineering Chonburi Thailand University of Hertfordshire Dept. of Engineering Hatfield United Kingdom University of Patras Computer Engineering and Informatics Dept. Patras Greece Computer Technology Institute and Press 'Diophantus Patras Greece University of Peloponnesse Dept. of Electrical and Computer Engineering Patras Greece

ISBN: (纸本)9781728147543

In this paper we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image pre-processing for natural hair removal and image segmentation for extraction of the skin lesion area. The segmented images were processed by a convolutional neural network classifier. The training process was done by using the Keras and TensorFlow python packets with CUDA supported. The best performance was achieved by a convolutional neural network architecture with three convolution layers and the classification accuracy was equal to 76.83%. © 2019 IEEE.

关键词： Image classification

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Security enhancement of big data in cloud application using block-chain

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AIP Conference Proceedings 2023年第1期2587卷

作者： Avein Jabar Al-Asadi J. V. S. Arundathi Omdev Dahiya Abdul Wahab Hashmi Ankur Gupta Malik Bader Alazzam Dept. of Information Technology Technical College of Informatics TCI Sulaimani Polytechnic University Sulaymaniyah Iraq Dept. of Computer Science SR & BGNR Government Arts and Science Degree & PG College (Autonomous) Khammam Telangana India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Advanced Manufacturing and Mechatronics Lab Mechanical Engineering Department Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

Organizations are inundated on a daily basis with vast, difficult-to-manage volumes of structured and unstructured data, which is known as “big data.” Analysis of large amounts of data may provide useful information that can be used to improve decision-making and boost employee morale. This kind of data storage makes it almost hard to change, hack, or otherwise deceive. Every computer in a block-chain network has a copy of the block-transaction chain’s log, which is called a “block-chain.” In previous researches it has been observed that security of big data is a major issue. Block chain has been found suitable mechanism to deal with such issues. Block-chain has also been considered a service in the context of cloud computing, termed Block-chain as a Service (BaaS). By using virtual storage nodes, block-chain technology may create whole new cloud storage services that are very resistant to data tampering. The proposed research has focused on the enhancement of security of big data from SQL injection attack that is transferred over cloud application. Research is making use of block chain technology in order to resolve the issue of security. The major objective of research is to propose block chain based security model for big data that should be more secure and efficient in case of SQL injection attack. Research work has proposed modified algorithm enhances the security of big data in cloud application against.SQL injection, cross script.

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Clustering big data for novel health care system

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AIP Conference Proceedings 2023年第1期2587卷

作者： R. Saravanakumar S. P. Maniraj Anindita Das Barshan Santanu Das Hasibul Hasan Malik Bader Alazzam Dept. of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India Dept. of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamilnadu Chennai India Dept. of Medicine Dhaka Medical College Hospital Dhaka Bangladesh Dept. of Biotechnology Seshadripuram First Grade College Yelahanka New Town Bangalore India Dept. of Applied Nutrition and Food Technology Islamic University Bangladesh Faculty of Computer Science and Informatics Amman Arab University Jordan

In research, the relevance of data science in health care is being explored. It has been proposed that the health-care sector benefit from a Nutrition Prescription System. In the proposed model, the Map Reduce, Enhanced K-Mean clustering technique was used. Actual datasets are represented by rectangular rectangles, which contain the health care centre dataset and frequency dataset generated by the map reduction method. Operations in original data flow are represented by the elliptical boxes. Patients will be able to use this technique to access specific and specialized health care inst.tution. As a consequence, consumers will get better services that are tailored to their specific requirements. Patients would have secure access to planned system because of backup facilities. Because diagnosis has no side effects, the concept of nutrition prescription system is better than that of medicine prescription system. In suggested dietary prescription system, patient selects symptoms. The patient is provided information on his nutritional deficits, requirements and food supply. In addition, the patient is provided additional nutritional data stored in an unstructured dataset. The outcome was derived from the data set. In milliseconds, the report also shows the time spent on traditional and contemporary duties.

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License plate detection and recognition: A study of review

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AIP Conference Proceedings 2023年第1期2834卷

作者： Naaman Omar Subhi R. M. Zeebaree Mohammed A. M. Sadeeq Rizgar R. Zebari Hanan M. Shukur Ahmed Alkhayyat Lailan M. Haji Shakir Fattah Kak Technical College of Administration Information Technology Management Dept. Duhok Polytechnic University Duhok-Iraq Technical College of Engineering Energy Dept. Duhok Polytechnic University Duhok-Iraq Computer Science Department Nawroz University Duhok –Iraq Computer Engineering Dept. Al-Kitab University Kirkuk-Iraq Technical Engineering Dept. Islamic University Najaf Najaf-Iraq Computer Science Dept. University Of Zakho Duhok-Iraq Technical College of Informatics Information Technology Dept. Duhok Polytechnic University Duhok-Iraq

The detection and recognition of automatic license plates (ALPs) is an important task for traffic surveillance and parking management systems, as well as for sustaining the flow of modern civic life. It’s been suggested that there are a variety of ways to detect and recognize ALPs thus far Image processing and machine learning techniques are typically used in these approaches. For object detection and license plate identification, this article reviews most of the approaches. In order to improve the suitability of the input pictures for subsequent processing, many pre-processing approaches including Gaussian filtering and adaptive image contrast augmentation were examined. Deep semantic segmentation networks and deep learning techniques are also utilized to find the license plate areas in the input picture… For example, deep encoder-decoder network architecture and convolutional neural network (CNN) models used to recognize license plate.

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Optimization of Stride Prefetching Mechanism and Dependent Warp Scheduling on GPGPU

Optimization of Stride Prefetching Mechanism and Dependent W...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Tsung-Han Tsou Dun-Jie Chen Sheng-Yang Hung Yu-Hsiang Wang Chung-Ho Chen Inst. of Computer and Communication Engineering Dept. of Electrical Engineering National Cheng-Kung University Taiwan

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

In this paper, we propose a data prefetching scheme, History-Awoken Stride (HAS) prefetching, optimized with a warp scheduler, Prefetched-Then-Executed (PTE), and evaluate the performance on the platform that we developed. Our platform is a single inst.uction, multiple thread (SIMT) GPGPU environment, supporting OpenCL 1.2 runtime and TensorFlow framework with CUDA-on-CL technology. Enormous amount of executing threads in GPU demands critical memory performance. HAS exploits history table of related memory accesses in intra-warp and inter-warp of the same workgroup as well as among workgroups, and uses address strides and warp status to monitor the prefetching progress of the executed warp. PTE precisely issues warps according to prefetching status from HAS. The experimental results of LeNet-5 inference and 11 PolyBench test programs on CAS-GPU show that our mechanism can achieve an average IPC performance improvement of 10.4%, and 7.8% reduction in data cache miss rate. The prefetch accuracy can reach 67.7%, and the proportion of prefetch request arrived at the appropriate time reaches 48.2%.

关键词： Prefetching History Kernel Graphics processing units Benchmark testing Central Processing Unit

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Unbounded regions of high-order Voronoi diagrams of lines and segments in higher dimensions 30

Unbounded regions of high-order Voronoi diagrams of lines an...

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30th International Symposium on Algorithms and Computation, ISAAC 2019

作者： Barequet, Gill Papadopoulou, Evanthia Suderland, Martin Dept. of Computer Science Technion - Israel Inst. of Technology Haifa3200003 Israel Faculty of Informatics Università Della Svizzera Italiana Lugano Switzerland

ISBN: (纸本)9783959771306

We study the behavior at infinity of the farthest and the higher-order Voronoi diagram of n line segments or lines in a d-dimensional Euclidean space. The unbounded parts of these diagrams can be encoded by a Gaussian map on the sphere of directions Sd−1. We show that the combinatorial complexity of the Gaussian map for the order-k Voronoi diagram of n line segments or lines is O(min{k, n−k}nd−1), which is tight for n−k = O(1). All the d-dimensional cells of the farthest Voronoi diagram are unbounded, its (d−1)-skeleton is connected, and it does not have tunnels. A d-cell of the Voronoi diagram is called a tunnel if the set of its unbounded directions, represented as points on its Gaussian map, is not connected. In a three-dimensional space, the farthest Voronoi diagram of lines has exactly n2−n three-dimensional cells, when n ≥ 2. The Gaussian map of the farthest Voronoi diagram of line segments or lines can be constructed in O(nd−1α(n)) time, while if d = 3, the time drops to worst-case optimal O(n2). © Gill Barequet, Evanthia Papadopoulou, and Martin Suderland;licensed under Creative Commons License CC-BY

关键词： Gaussian distribution

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Heart Attack Classification System using Neural Network Trained with Particle Swarm Optimization

arXiv

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arXiv 2022年

作者： Amin, Askandar H. Ahmed, Botan K. Maaroof, Bestan B. Rashid, Tarik A. Technical College of Informatics Sulaimani Polytechnic University KRG Sulaimani Iraq Administration and Economics University of Sulaimani KRG Sulaimani Iraq IT Department College of Commerce University of Sulaimani KRG Sulaimani Iraq School of Information Technology Fanshawe College London Canada Computer Science and Engineering Dept. University of Kurdistan Hewler KRG Erbil Iraq

The prior detection of a heart attack could lead to the saving of one’s life. Putting specific criteria into a system that provides an early warning of an imminent attack will be advantageous to a better prevention plan for an upcoming heart attack. Some studies have been conducted for this purpose, but yet the goal has not been reached to prevent a patient from getting such a disease. In this paper, Neural Network trained with Particle Swarm Optimization (PSONN) is used to analyze the input criteria and enhance heart attack anticipation. A real and novel dataset that has been recorded on the disease is used. After preprocessing the data, the features are fed into the system. As a result, the outcomes from PSONN have been evaluated against.those from other algorithms. Decision Tree, Random Forest, Neural network trained with Backpropagation (BPNN), and Naive Bayes were among those employed. Then the results of 100%, 99.2424%, 99.2323%, 81.3131%, and 66.4141% are produced concerning the mentioned algorithms, which show that PSONN has recorded the highest accuracy rate among all other tested algorithms. © 2022, CC BY.

关键词： Swarm intelligence

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Generative Adversarial Networks Based Cognitive Feedback Analytics System for Integrated Cyber-Physical System and Industrial Iot Networks

Research Square

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Research Square 2021年

作者： Upreti, Kamal Syed, Mohammad Haider Alam, Mohammad Shabbir Alhudhaif, Adi Shuaib, Mohammed Sharma, Arvind K. Department of Information & Technology Inderprastha Engineering College Ghaziabad India College of Computing and Informatics Saudi Electronic University Saudi Arabia Department of Computer Science College of computer science and Information Technology Jazan University Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 al-Kharj11942 Saudi Arabia College of Computer Science & IT Jazan University Saudi Arabia Dept. of CSI University of Kota Rajasthan India

In the modern era of technologies, the internet grows in the advancement of our day-to-day life like automation devices. The devices to set up industries with integrated cyber-physical systems and industrial IoT applications. Generative adversarial networks (GAN) can generate Cognitive feedback analysis with various data for both generator and discriminator in a supervised model. Neural networks are used for artificial intelligence algorithms, but in adversarial networks, feedback analytics is analyzed with the significance of data. The modern age of intelligent manufacturing will indeed be ushered in by Cyber-Physical Production Systems (CPPS). However, because of the connections between the virtual and physical worlds, CPPS would be subject to cross-domain assaults. Against.Denial-of-Service (DoS) threats, this paper concentrates on complex performance feedback management of Cyber-Physical Systems (CPS). To begin, a swapping system modelling approach for the complex response feedback CPS is provided by analyzing the distinct effects of DoS assaults on the sensor-controller (SC) and controller-to-actuator (C-A) channels, accordingly. Given the difference in bandwidth between the dual channels and the accused's energy cap, it is reasonable to conclude that an offender can only jam a single communication stream at a point and also that the possible number of successive DoS attacks is limited. Second, using a packet-based transfer scheme, a nested switching paradigm is built on the foundation of the switching mechanism, considering both the spatial heterogeneity and the temporal durability of DoS attacks. The probability of discriminator gets analyzed feedback data to check whether actual data or fake data is sampled, and it is generated. Cognitive feedback supports genetic algorithms to sample the feedback data in a system for advanced technologies. © 2021, CC BY.

关键词： Cyber Physical System

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The Rendezvous Between Extreme Value Theory and Next-generation Networks

arXiv

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arXiv 2024年

作者： Sagar, Srinivas Subhash, Athira Liu, Chen-Feng Elzanaty, Ahmed Al-Badarneh, Yazan H. Kalyani, Sheetal Alouini, Mohamed-Slim Bennis, Mehdi Hanzo, Lajos Dept. of Electrical Engg. IIT Madras India Department of Informatics New Jersey Institute of Technology NewarkNJ07102 United States University of Surrey GuildfordGU2 7XH United Kingdom Department of Electrical Engineering The University of Jordan Amman11942 Jordan Makkah Province Thuwal Saudi Arabia Centre for Wireless Communications University of Oulu Oulu90014 Finland School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Promising technologies such as massive multiple-input and multiple-output, reconfigurable intelligent reflecting surfaces, non-terrestrial networks, millimetre wave communication, ultra-reliable low-latency communication are envisioned as the enablers for next-generation (NG) networks. In contrast to conventional communication systems meeting specific average performance requirements, NG systems are expected to meet quality-of-service requirements in extreme scenarios as well. In this regard, extreme value theory (EVT) provides a powerful framework for the design of communication systems. In this paper, we provide a comprehensive survey of advances in communication that utilize EVT to characterize the extreme order statistics of interest. We first give an overview of the history of EVT and then elaborate on the fundamental theorems. Subsequently, we discuss different problems of interest in NG communication systems and how EVT can be utilized for their analysis. We finally point out the open challenges and future directions of EVT in NG communication systems. Copyright © 2024, The Authors. All rights reserved.

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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning

Constructing a Cryptocurrency-Price Prediction Model Using D...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Toai Kim Tran Thanh Thi Tuyet Le Thinh Tien Bui Vang Quang Dang Roman Senkerik Faculty of Economics Ho Chi Minh City University of Technology and Education No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Vietnam Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. Listopadu 2172/15 Ostrava-Poruba Czech Repulic Faculty of Applied Informatics Tomas Bata University in Zlin T. G. Masaryka 5555 Zlin Czech Republic

ISBN: (纸本)9781665491075

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies.

关键词： Deep learning Recurrent neural networks Bitcoin Predictive models Prediction algorithms Market research Convolutional neural networks

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