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A new lightweight data security system for data security in the cloud computing

Measurement: Sensors 2023年 29卷

作者： Mohammed, Shameer Nanthini, S. Bala Krishna, N. Srinivas, Inumarthi V. Rajagopal, Manikandan Ashok Kumar, M. University of Technology and Applied Sciences Sur Oman Department of AI & DS Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Thandalam Chennai India Department of CSE Mohan Babu University &SreeVidyanikethan Engineering College Andhrapradesh Tirupati517102 India Mumbai400077 India School of Business and Management Christ University Bangalore560029 India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria

In recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat Information (CTI) sanitization procedure before releasing CTI for analytic strategy. In addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTI information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. In conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture. © 2023 The Authors

关键词： Cryptography

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Multi-Classification of Depression Levels Based on Blood Biomarkers

Multi-Classification of Depression Levels Based on Blood Bio...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Rahmi Kavak Selma Ayşe Özel Sema Polat Sinan Özler Department of Software Engineering Adana Alparslan Türkeş Science and Technology University Adana Turkey Department of Computer Engineering Çukurova University Adana Turkey Department of Anatomy Faculty of Medicine Çukurova University Adana Turkey Department of Mental and Nervous Diseases The Seyhan State Hospital Adana Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Depression is a psychiatric condition characterized by a persistent feeling of sadness and diminished interest in significant activities. Diagnosing this health problem is challenging because it relies on several social and physiological factors. The timely identification of depression aids in the prevention of severe outcomes, such as suicide. Early detection of depression levels is crucial to prevent adverse effects and enhance the quality of everyday life. The purpose of this work is to use data preprocessing techniques to create a clean dataset from noisy and incomplete medical data that includes blood biomarker values of patients and then apply data mining techniques to this dataset to predict the degree of depression. Adana Dr. Ekrem Tok Mental Health Hospital supplied the raw data for the study, with consent from the ethics committee. Missing data are completed by filling with the constant value and the most frequent value methods (i.e., 0 and mode values) in the relevant column. The classification of the dataset is performed using AdaBoost, Decision Tree (DT), and Logistic Regression (LR) classifiers, which have been previously used in medical datasets and demonstrated to be effective. The Logistic Regression classifier achieved the highest success rate (Accuracy: 0.541 and weighted F-score: 0.481).

关键词： Logistic regression Ethics Hospitals Prevention and mitigation Mental health Depression Noise measurement Decision trees Medical diagnostic imaging Blood

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Brain sight: a novel system for substituting vision sensor

Brain sight: a novel system for substituting vision sensor

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The 3rd International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： N. Y. Elsaeed M. M. Alshaarawy A. E. S. Esmaiel S. E. D. Elmahdy O. Abdelhameed M. M. Fakhry N. A. Ogabih N. M. Ali R. El-Deeb Software Engineering Department Faculty of Computers and Information Mansoura University Mansoura 35516 Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura 35516 Egypt

In a world where nearly everything we do depends on sight; it is ever more challenging for the unsighted to cope with it and lead a normal life without being reliant on the presence of a companion. Finding a mechanism to convey visual information to the brain is the most difficult part of the research as well as the development of efficient sensory replacement and augmentation systems. In this paper, we aim to propose Brain Sight as an innovative system to help blind people identifying and discovering things in front of them using vibro-tactile display for vision sensory substitution. Brain Sight is a wearable vest-like fabric containing small vibration motors that displays pulses to map the seen images on the Lower-back of the visually impaired. A deep labeling segmentation and edge detection techniques are applied to the captured image after preprocessing. A simple prototype is implemented and tested with volunteers. Thanks to neuroplasticity and the cognitive cortex inside humans' brain, the implemented vest showed promising results. Without the need for intensive training, Brain Sight enables blind to recognize different shapes and numbers.

关键词： brain computer vision edge detection handicapped aids haptic interfaces image sensors neurophysiology

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Incorporation, Transparency and Cognitive Extension: Why the Distinction Between Embedded and Extended Might Be More Important to Ethics Than to Metaphysics

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Philosophy and Technology 2022年第1期35卷 1-21页

作者： Farina, Mirko Lavazza, Andrea Institute for Humanities and Social Sciences Universitetskaya St 1 Republic of Tatarstan Innopolis 420500 Russian Federation Lab of Industrializing Software Production (LIPS) Faculty of Computer Science and Engineering Universitetskaya St 1 Republic of Tatarstan Innopolis 420500 Russian Federation Centro Universitario Internazionale Via Antonio Garbasso AR Arezzo 42 - 52100 Italy

We begin (Sect. 1) by introducing our readers to the Extended Mind Thesis and briefly discuss a series of arguments in its favour. We continue (Sect. 2) by showing of such a theory can be resisted and go on to demonstrate that a more conservative account of cognition (called Embedded Theory) can be developed. We acknowledge (Sect. 3) a stalemate between these two different accounts of cognition and notice a couple of issues (namely, dimensions of transparency and excess of metaphysics in the debate) that we argue have prevented further progress in the field. To overcome the stalemate, we propose (Sect. 4) to focus on the ethical implications of these theories. We link (Sect. 5) work conducted on the Extended Mind Thesis with contemporary transhumanist research and claim that extended may be ethically preferred over embedded because it is a more progressive and inclusive theory. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Embedded mind Ethics Extended mind Morality Transhumanism

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A speech-driven pupil response system with affective highlight by virtual lighting

A speech-driven pupil response system with affective highlig...

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作者： Sejima, Yoshihiro Kawamoto, Hiroki Sato, Yoichiro Watanabe, Tomio Faculty of Informatics Kansai University 2-1-1 Ryozenji-cho Takatsuki-shi Osaka569-1095 Japan PEOPLE SOFTWARE CORPORATION 1-7-2 Achi Kurashiki-shi Okayama710-0055 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University 111 Kuboki Soja-shi Okayama719-1197 Japan

In face-to-face communication, talkers perceive various affects and emotions of other talkers as the eye-impressions such as highlight or dilated pupil. In the previous studies, focusing on the eye-impressions in social robots, various expression methods such as changing the LED color or blinking the LED were proposed and developed. However, it is difficult to generate the affective expressions with the highlight that has an optical reflection characteristic on the surface of the eyeball by using the LED. Therefore, in order to enhance the eye-impressions of social robots, it is expected to develop an expressible method with the affective highlight. In this study, we proposed an affective highlight to enhance the eye-impressions of social robots. This method introduces a virtual lighting in virtual space and can generate rich affective expressions by controlling various parameters of the virtual lighting such as brightness or colors. In addition, we developed a speech-driven pupil response system that applied the affective highlight to the pupil response interface which has been developed. This system can generate the affective expressions by the dilation of pupil response that is synchronized with the talker’s speech as well as the highlight expression with talker’s intentional input. The effectiveness of the developed system with the affective highlight was demonstrated by sensory evaluations. © 2022 The Japan Society of Mechanical Engineers.

关键词： Light emitting diodes

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th International Conference on Advances in Computing and Data sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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Detection of Ophthalmic Disorders Through Deep Feature Fusion

Detection of Ophthalmic Disorders Through Deep Feature Fusio...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Muhammad Toheed Hussain Muhammad Faraz Sumair Aziz Khushbakht Iqtidar Muhammad Umar Khan Gabriel Axel Montes Department of Electronics Engineering University of Engg. & Tech. Taxila Taxila Pakistan Faculty of Science and Technology University of Canberra Canberra ACT Australia Dept. of Computer & Software Engineering National University of Sciences & Tech. Islamabad Pakistan University of Newcastle Callaghan NSW Australia

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Given the rising prevalence of ophthalmic disorders, this study aims to meet the urgent need for an automated technique capable of diagnosing and treating these conditions. Our approach combines deep learning and machine learning techniques to develop a diagnostic system that can detect and classify seven distinct ophthalmic disorders. Manual diagnosis of ophthalmic disorders is labor-intensive and slow, worsened by a shortage of medical experts. In this paper, we propose a medical diagnostic system that utilizes deep features extracted from a ResNet-50 CNN, which has been trained on retinal images enhanced through contrast-limited adaptive histogram equalization (CLAHE). These deep features serve as input for the machine learning model. To address the class imbalance problem, we employed ADASYN to generate synthetic features for all classes. An Ensemble subspace KNN classifier, trained on these deep features, achieved a remarkable mean accuracy of 99.18% compared to other classifiers evaluated in this study. The proposed technique has the potential to assist medical experts in achieving timely and accurate diagnoses of ophthalmic diseases.

关键词： Deep learning computers Histograms Accuracy Manuals Feature extraction Retina

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Shunt Capacitor Bank Fault Detection and Localization Using Sub-Cycle Algorithm

Shunt Capacitor Bank Fault Detection and Localization Using ...

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IEEE Canada Electric Power Conference, EPEC

作者： Mohsen Tajdinian Behzad Behdani Ali Goodarzi Harold R. Chamorro Vijay K. Sood Department of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft the Netherlands Fars Electricity Distribution Company Fars Iran School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Ontario Canada

ISBN: (纸本)9781665463195

Fast internal detection and location in Shunt Ca-pacitor Banks (SCBs) can lead to the prevention of damages to other SCBs' elements and consequently avoid undesirable performance and effects in power system operation. This paper targets the performance of phasor-based algorithms of failure detection and fault location of SCBs. Being dependent on the fundamental phasor components which usually are calculated based on the Discrete Fourier Transform (DFT), the failure detection and fault location algorithms suffer from almost one-cycle delay. This paper provides sub-cycle phasor estimation based on the least-square technique. The proposed algorithm is evaluated for different configurations of SCBs considering different fuse protection designs. The proposed method provides a criterion for relay decision-making in the case of multiple faulty phases condition. The proposed method is designed to monitor and detect consecutive failures based on the existing data of commercial relays. Performance evaluations are conducted under different circumstances namely voltage unbalance conditions and multiple internal fault locations.

关键词： Performance evaluation Fault detection Capacitors Discrete Fourier transforms Estimation Voltage Fault location

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Automated Recognition of Structures in Scanning Electron Microscopy Images Using Specialized Algorithms in MATLAB and Python: An Overview

Automated Recognition of Structures in Scanning Electron Mic...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Valentina Marascu Marius Iulian Mihailescu Stefania Loredana Nita Marius Rogobete Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science “Spiru Haret” University Bucharest Romania Low Temperature Plasma Department National Institute for Laser Plasma and Radiation Physics Bucharest Romania Department of Computers and Cybersecurity “Ferdinand I” Military Technical Academy Bucharest Romania Telematics Harman International Bucharest

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Applied physics serves as the bridge connecting theoretical principles with their practical implementations in everyday situations. Informatics plays a crucial role in this subject because of its comprehensive and intricate capacity to simulate and forecast physical phenomena. This research highlights the indispensability of computer-based algorithms for analyzing the Scanning Electron Images of the obtained materials, particularly materials synthesized by laboratory plasma. Herein, the conducted research highlights the advantages of image processing via Matlab and Python algorithms, in order to analyze the synthesized structures. This approach can offer perspectives on physical processes by employing image processing techniques. The approaches and future directions were identified.

关键词： Scanning electron microscopy software algorithms Tungsten Process control software Oxidation Plasmas

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Life cycle assessment, exergoenvironmental analysis, and AI-driven optimization of an oxy-biogas power plant combined with liquefied CO2 and H2 generation units

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Energy 2025年 330卷

作者： Wang, Zheng Li, Jialing Salih, Sinan Q. Shaban, Mohamed Samad, Sarminah Almadhor, Ahmad Abdullaev, Sherzod Alturise, Fahad Alkhalaf, Salem Khairy, Yasmin Chongqing Chemical Industry Vocational College Chongqing4001228 China School of Artificial Intellegence Chongqing Youth Vocational & Technical College Chongqing401320 China Technical College of Engineering Al-Bayan University Baghdad Iraq Department of Physics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia Department of Management College of Business Administration Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Faculty of Chemical Engineering New Uzbekistan University Tashkent Uzbekistan Researcher of Scientific Department Tashkent State Pedagogical University Tashkent Uzbekistan Department of Cybersecurity College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Physics Department College of Science King Khalid University AlQura'a P.O. Box 960 Abha Saudi Arabia

Addressing the environmental challenges associated with current polygeneration systems necessitates the development of innovative strategies to mitigate irreversible processes and their environmental consequences. This study introduces an innovative and sustainable heat design network, encompassing an oxy-biogas combustion combined to a gas turbine cycle, which operates in integration with supercritical and transcritical CO2 cycles. This setup also uses a CO2 capture unit, subsequently liquefying the captured CO2, allowing the system to be a zero-emission model. Additionally, the electric power yielded by the integrated power plants is utilized to generate hydrogen via solid oxide electrolysis. The resulting hydrogen is then processed through a cryogenic unit based on the principles of a Claude cycle to liquefy the hydrogen gas for efficient storage and transport. To evaluate the planned arrangement's irreversibility and environmental impact, thorough exergy, exergoenvironmental, and life cycle assessments are conducted, followed by advanced optimization techniques. The optimization is conducted through an artificial intelligence-driven approach that combines artificial neural networks with a multi-objective grey wolf optimization method. The findings reveal that combustion temperature is the significant factor affecting system's operation. The optimal outcomes related to exergy destruction rate, sustainability index, and environmental impact rate are also determined to be 10665 kW, 1.767, and 1.76 × 105 mPts/h, respectively. In addition, the exergoenvironmental factor and the specific exergoenvironmental impact of products are found at 0.5650 and 5311 mPts/GJ. Besides, the specific exergoenvironmental impacts associated with liquefied hydrogen and liquefied CO2 are attained to be 6078 mPts/GJ and 4546 mPts/GJ, respectively. © 2025 Elsevier Ltd

关键词： Carbon capture and storage

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