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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Sathishkumar, G. Siva, P. Anto Maria Joseph, D. Santhosh, R. Reshma, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

ISBN: (纸本)9798350398496

Cloud computing seems to be a scalable and realistic distributed computing platform that delivers resources as a service. One of the major difficulties impeding cloud computing development is data security in the cloud. The security vulnerabilities in cloud computing and other cloud services slow down cloud computing growth and difficulties with data protection and privacy. In this newcontext, cloud service customers must be cautious about data breaches. This article studies a solution to current diverse security challenges that have arisen as a result of the service demonstrating that the offered methods are provably safe and very efficient. The solution comprises a distributed multi-level security scheme for processing the information involved in cloud computing. The proposed scheme evaluates the most significant encryption techniques generally used in various industries like AES (Advanced Encryption Standard), Blowfish, 3DES (Data Encryption Standard), CAST128, and RC4 (Rivest Cipher 4). The proposed model with these encryption techniques ensure minimal energy consumption in cloud computing with maximum speed. Moreover, to increase the security of the data, various cryptographical algorithms are applied, analyzed, and compared in an OpenStack environment. © 2023 IEEE.

关键词： Energy utilization

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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Novel Algorithm for Mobile Robot Path Planning in Constrained Environment

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computers, Materials & Continua 2022年第5期71卷 2697-2719页

作者： Aisha Muhammad Mohammed A.H.Ali Sherzod Turaev Ibrahim Haruna Shanono Fadhl Hujainah Mohd Nashrul Mohd Zubir Muhammad Khairi Faiz Erma Rahayu Mohd Faizal Rawad Abdulghafor Faculty of Manufacturing Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechanical Engineering Faculty of EngineeringUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science and Software Engineering College of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechatronics Engineering Faculty of TechnologyBayero UniversityKano(BUK)700241Nigeria Computer Science and Engineering Department Chalmers and University of Gothenburg41296GothenburgSweden Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science Faculty of Information and Communication TechnologyInternational Islamic University Malaysia53100Kuala LumpurMalaysia

This paper presents a development of a novel path planning algorithm, called Generalized Laser simulator (GLS), for solving the mobilerobot path planning problem in a two-dimensional map with the presence ofconstraints. This approach gives the possibility to find the path for a wheelmobile robot considering some constraints during the robot movement inboth known and unknown environments. The feasible path is determinedbetween the start and goal positions by generating wave of points in alldirection towards the goal point with adhering to constraints. In simulation,the proposed method has been tested in several working environments withdifferent degrees of complexity. The results demonstrated that the proposedmethod is able to generate efficiently an optimal collision-free path. Moreover,the performance of the proposed method was compared with the A-star andlaser simulator (LS) algorithms in terms of path length, computational timeand path smoothness. The results revealed that the proposed method hasshortest path length, less computational time and the best smooth path. Asan average, GLS is faster than A∗ and LS by 7.8 and 5.5 times, respectivelyand presents a path shorter than A∗ and LS by 1.2 and 1.5 times. In orderto verify the performance of the developed method in dealing with constraints, an experimental study was carried out using a Wheeled Mobile Robot(WMR) platform in labs and roads. The experimental work investigates acomplete autonomous WMR path planning in the lab and road environmentsusing a live video streaming. Local maps were built using data from a live video streaming with real-time image processing to detect segments of theanalogous-road in lab or real-road environments. The study shows that theproposed method is able to generate shortest path and best smooth trajectoryfrom start to goal points in comparison with laser simulator.

关键词： Path planning generalized laser simulator wheeled mobile robot global path panning local path planning

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Effective Anatomy Learning Using Markerless Augmented Reality Interface for Medical Education

Effective Anatomy Learning Using Markerless Augmented Realit...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Athinarayanan, S. Masthan, M. Chandrasekaran, Saravanan Ashwin Ram, V. Mahesh, T.R. Vivek, V. Department Of Cse Chennai Avadi600062 India Arffy Technologies Private Limited Karnataka Bengaluru India Srm Institute Of Science And Technology Department Of Computer Science And Engineering Chennai Tamil Nadu Ramapuram600089 India Faculty Of Engineering And Technology Department Of Computer Science & Engineering Bengaluru562112 India

ISBN: (纸本)9798350364828

Anatomy is a branch of biology dealing with the identification and explanation of living things body systems. Human anatomy is a significant chapter in every Biology class for secondary and post-secondary students. Traditional approaches and tools for studying human body anatomy include manuals with illustrations and images, as well as artificial anatomy mannequins. It isn't sufficient but and its miles tough to assist medical students to recognize it with actual and correct information and images approximately the anatomy of our human body. This is due to students' inability to visualize the actual configuration of human anatomy body parts and their lack of familiarity with 2D representations in textbooks. Furthermore, some students have difficulty interpreting 2D representations in textbooks, making it impossible for them to comprehend and gain information as quickly as possible in studying the layout of human anatomy body parts. Using Google AR Core and Unity3D, we proposed a system that builds a human anatomy learning android application that assists the medical students to visualize and analyze the Human Anatomy subject in an interactive way by using the Augmented Reality (AR) technique. The markerless AR approach is being used to create this scheme which uses the novel method to use the mobile computing platform as an alternative learning tool for studying the intricate structures of human body anatomy more practically and engagingly. © 2024 IEEE.

关键词： Augmented reality

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Misuse of Wi-Fi Data to Analyze User Behaviour 15

Misuse of Wi-Fi Data to Analyze User Behaviour

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15th International Conference Elektro, ELEKTRO 2024

作者： Kapicak, Lukas Stipal, Jiri Trubak, Kamil Machnik, Petr Burdiak, Patrik Rozhon, Jan VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Telecommunications Ostrava Czech Republic

ISBN: (纸本)9798350372359

There has been a huge increase in the number of mobile devices over the past few years. Many mobile users own more than one mobile device. Compared to conventional laptops, mobile devices are constantly active and their users use them outdoors more often than personal computers. Compared to desktop computers, mobile devices accompany their owners on their travels and therefore there is a significant risk of tracking and eavesdropping on user data without a user being aware of it. This paper describes techniques how the attacker could track users by using the Wireless Fidelity (Wi-Fi) network. Every mobile device generates traffic that can be divided into 'Automatic' and 'User'. When' Automatic' traffic is generated, the device can be found because of its pattern. The paper also describes how the attacker can get into the victim's Wi-Fi network without being in its range. When breaching the Wi-Fi network security, the attacker can connect to the victim's network during his first appearance in range of this network. Security pre-cautions and possible directions of future work are discussed at the end of this paper. © 2024 IEEE.

关键词： Personal computers

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Insights into research on blockchain for smart contracts: a bibliometric analysis

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Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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Cell-based allometry:an approach for evaluation of complexity in morphogenesis

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Quantitative Biology 2023年第2期11卷 183-203页

作者： Ali Tarihi Mojtaba Tarihi Taki Tiraihi Department of Computer Engineering Faculty of Computer Science and EngineeringShahid Behshti UniversityTehran 19839-69411Iran Department of Computer Engineering Sharif University of TechnologyTehran 11365-11155Iran Department of Anatomical Sciences School of Medical SciencesTarbiat Modares UniversityTehran 14117-13116Iran

Background:Morphogenesis is a complex process in a developing animal at the organ,cellular and molecular *** this investigation,allometry at the cellular level was ***:Geometric information,including the time-lapse Cartesian coordinates of each cell's center,was used for calculating the allometric coefficients.A zero-centroaxial skew-symmetrical matrix(CSSM),was generated and used for constructing another square matrix(basic square matrix:BSM),then the determinant of BSM was calculated(d).The logarithms of absolute d(Lad)of cell group at different stages of development were plotted for all of the cells in a range of development stages;the slope of the regression line was estimated then used as the allometric ***,the lineage growth rate(LGR)was also calculated by plotting the Lad against the logarithm of the *** complexity index at each stage was *** method was tested on a developing Caenorhabditis elegans ***:We explored two out of the four first generated blastomeres in *** *** ABp and EMS lineages show that the allometric coefficient of ABp was higher than that of EMS,which was consistent with the complexityindexas well ***:The conclusion of this study is that the complexity of the differentiating cells in a developing embryo can be evaluated by allometric scaling based on the data derived from the Cartesian coordinates of the cells at different stages of development.

关键词： embryogenesis allometry complexity C.elegans bioinformatics skew matrix morphogenesis

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A Comprehensive Analysis of Identifying Patients with Chronic Kidney Disease 2

A Comprehensive Analysis of Identifying Patients with Chroni...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Geetha, S. Marimuthu, C.N. Maheswari, S. Deepa, B. Anna University Faculty of Information and Communication Engineering Chennai600025 India Nandha Engineering College Department of Electronics and Communication Engineering Erode638052 India Nandha Engineering College Department of Computer Science and Engineering Erode638052 India

ISBN: (纸本)9798350387933

Chronic Kidney Disease (CKD) is a prevalent and progressive health condition affecting millions of individuals worldwide. Early identification and management of CKD are critical to mitigate its adverse effects on patients' health and reduce the burden on healthcare systems. This study presents a comprehensive analysis of various methodologies and approaches for identifying patients with Chronic Kidney Disease. The research encompasses a detailed review of traditional and emerging diagnostic techniques, by exploring the sensitivity, specificity, and accuracy of these methods, considering their applicability in diverse patient populations and healthcare settings. Furthermore, this analysis delves into the role of artificial intelligence (AI) and machine learning in enhancing CKD detection and risk prediction. The synthesis of this comprehensive analysis provides valuable insights into the current landscape of CKD identification and offers recommendations for optimizing diagnostic strategies. © 2024 IEEE.

关键词： Risk assessment

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Cloud based Weather Station using IoT Technology to Track the Air Parameters 2

Cloud based Weather Station using IoT Technology to Track th...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Jayanthi, S. Srividhya, E. Ramshanka, N. Srinivasan, T.R. Prabhakar, P.B. Edwin Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Jimma Institute of Technology Jimma University Faculty of Electrical and Computer Engineering Department of Computer Engineering Jimma Ethiopia New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350346640

The Internet of Things (IoT) technology is a viable alternative for monitoring meteorological data in a specific area and making the data accessible from anywhere in the world. This is based on the idea that IoT technology connects electronic sensors to the public internet. Data from the embedded system can be accessed via the internet from anywhere in the world. In some regions, it may be impossible to examine and monitor vital weather parameters utilizing wires and analog devices during some weather threats. This research proposes a comprehensive approach for monitoring the weather conditions in a particular region and making the information accessible in other parts of the globe using a system described in this study. The IoT is the underlying technology that enables this. It is a highly developed and effective method for connecting things to the internet and bringing the whole internet of things together in a network. This research work has developed an IoT-based forecast concerning sustainable development using an embedded system that can report weather status such as air pressure, heating rate, moisture, air quality, illuminance, and so on in real time. The measurement data has been given as an input to Thingspeak's server for processing and assessment. © 2023 IEEE.

关键词： Internet of things

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