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Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

computers, Materials & Continua 2022年第3期70卷 5667-5681页

作者： V.Thanikachalam S.Shanthi K.Kalirajan Sayed Abdel-Khalek Mohamed Omri Lotfi M.Ladhar Sri Sivasubramaniya Nadar College of Engineering Chennai603110India Department of Information Technology Department of Electronics and Communication EngineeringCARE College of EngineeringTiruchirappalli620009India Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Mathematics Department Faculty of ScienceSohag UniversitySohagEgypt Deanship of Scientific Research King Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

The rapid development of biomedical imaging modalities led to its wide application in disease ***-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ***,physicians examine the characteristics of tongue prior to *** this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is *** model can reduce the physical harm made to the *** tongue image analytical methodologies have been proposed ***,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis *** this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI *** proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal ***,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)*** last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color *** design of OGSO algorithm for SqueezeNet model shows the novelty of the *** assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several *** resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

关键词： Biomedical images image processing tongue color image deep learning squeezenet disease diagnosis

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An Efficient Impersonation Attack Detection Method in Fog Computing

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computers, Materials & Continua 2021年第7期68卷 267-281页

作者： Jialin Wan Muhammad Waqas Shanshan Tu Syed Mudassir Hussain Ahsan Shah Sadaqat Ur Rehman Muhammad Hanif Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electronics Engineering FICTBalochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Computer Science Namal InstituteMianwali42200Pakistan

Fog computing paradigm extends computing,communication,storage,and network resources to the network’s *** the fog layer is located between cloud and end-users,it can provide more convenience and timely services to ***,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the *** attacker acts as an impersonator to impersonate other legitimate ***,in this work,we present a detection technique to secure the FC ***,we model a physical layer key generation based on wireless channel *** generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver *** compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and *** simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).

关键词： Fog computing double Sarsa attack detection physical layer key security

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A Novel Image Encryption and Decoding Approach Based on DNA Coding and Arnold's Transformation for IoT Applications

A Novel Image Encryption and Decoding Approach Based on DNA ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Shaltout, Nawal Abd El-Latif, Ahmed A. Al-Adrousy, Waleed Hammad, Mohamed Ateya, Abdelhamied A. Elmougy, Samir Mansoura University Faculty of Computers and Information Department of Computer Science Mansoura35516 Egypt Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science 32511 Egypt Menoufia University Faculty of Computers and Information Department of Information Technology Shibin El Kom32511 Egypt Zagazig University Department of Electronics and Communications Engineering Zagazig Egypt

ISBN: (纸本)9798350351408

Nowadays, the Internet of Things (IoT) is significantly utilized by an extensive range of applications that lead to the enhancement of different aspects of defense and military capabilities. In IoT, a wide range of smart devices that can communicate with each other are connected through it. Therefore, it is essential to protect multimedia data, especially images with an encryption approach that offers a sufficient degree of security in military applications. Hence, this paper introduces an effective scheme for image encryption and decoding relying on Arnold's transformation DNA coding. It proposes a new pseudo-random number generation PRNG using the Sine-Logistic map and Duffing map called SLDM. The encryption method involves three phases: 1- Key generation and PRNG. 2- confusion phase using the Arnold transformation method. 3- diffusion using DNA rules and operations. The results based on the USC-SIPI dataset, show that the suggested scheme is sufficiently resistant to every kind of attack. Furthermore, the suggested method yields NPCR scores of around 99.62% and UACI scores of approximately 33.48%. Due to its low complexity, it has a great deal of potential for safe and real-time picture applications. © 2024 IEEE.

关键词： Random number generation

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Power Allocation Strategy for Secret Key Generation Method in Wireless Communications

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computers, Materials & Continua 2021年第8期68卷 2179-2188页

作者： Bin Zhang Muhammad Waqas Shanshan Tu Syed Mudassir Hussain Sadaqat Ur Rehman School of Electronics and Information Engineering Hunan University of Science and EngineeringYongzhou425199China Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electronics Engineering FICTBalochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Computer Science Namal InstituteMianwali42200Pakistan

Secret key generation(SKG)is an emerging technology to secure wireless communication from ***,the SKG at the physical layer is an alternate solution over traditional cryptographic methods due to wireless channels’***,the physical layer secret key generation(PHY-SKG)depends on two fundamental parameters,i.e.,coherence time and power *** coherence time for PHY-SKG is not applicable to secure wireless *** is because coherence time is for a certain period of ***,legitimate users generate the secret keys(SKs)with a shorter key length in ***,an attacker can quickly get information about the ***,the attacker can easily get valuable information from authentic ***,we considered the scheme of power allocation to enhance the secret key generation rate(SKGR)between legitimate ***,we propose an alternative method,i.e.,a power allocation,to improve the *** results show 72%higher SKGR in bits/sec by increasing power *** addition,the power transmission is based on two important parameters,i.e.,epsilon and power loss factor,as given in power transmission *** found out that a higher value of epsilon impacts power transmission and subsequently impacts the *** SKGR is approximately 40.7%greater at 250 from 50 mW at epsilon=*** value of SKGR is reduced to 18.5%at 250 mW when epsilonis ***,the transmission power is also measured against the different power loss factor values,i.e.,3.5,3,and 2.5,respectively,at epsilon=***,it is concluded that the value of epsilon and power loss factor impacts power transmission and,consequently,impacts the SKGR.

关键词： Secret key generation rate power allocation physical layer wireless communication

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Optimized Spherical Network for Clustering based Fault-Tolerant and Reliable Routing in IoT-Enabled WSNs 8

Optimized Spherical Network for Clustering based Fault-Toler...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Asif, Md Prabha, M. Srinivasan, P. Santhosh Prabagaran, S. Jb Institute of Engineering and Tecnology Department of Electronics and Computer Engineering Moinabad Mandal Ranga Reddy District Telangana Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Mathematics Tamil Nadu Chennai India Karpagam Academy of Higher Education Faculty of Engineering Department of Mechanical Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

This research presents an innovative approach to optimize the performance and longevity of Internet of Things (IoT)-assisted Wireless Sensor Networks (WSNs). The proposed Osprey-Based Bowerbird Spherical Convolutional Network (OBSCN-GIA) framework addresses the critical challenges of energy consumption and fault tolerance. The OBSCN-GIA technique utilizes the Osprey Optimization Algorithm (OOA) for cluster head selection and cluster construction, ensuring efficient network organization. The Bowerbird Optimization Algorithm (BOA) is integrated to enhance fault tolerance and network survivability. A Spherical Convolutional Network (SCN)-based routing technique is employed to optimize WSN route selection, while the Geyser-Inspired Algorithm (GIA) fine-tunes SCN settings for optimal data transfer. The proposed approach was evaluated against existing models, demonstrating superior performance in terms of survivability, packet delivery ratio, network longevity, energy consumption, delay, and throughput. The OBSCN-GIA achieved a remarkable throughput of 99.94% and an average energy consumption rate of 0.02J for a network of 100 nodes. This research contributes to the development of more efficient and sustainable WSNs, paving the way for the widespread adoption of IoT technologies in various applications. © 2024 IEEE.

关键词： Fault tolerance

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Transmission System Expansion Planning Using Optimal Power Flow and Genetic Algorithm

Transmission System Expansion Planning Using Optimal Power F...

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2022 International Conference on Power, Energy and Innovations, ICPEI 2022

作者： Wiwechpaisankul, Weeratep Rattananatthawon, Ongorn Sirisumrannukul, Somporn Sabpayakom, Noppatee. Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand College of Industrial Technology of Power Electronics Engineering Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9781665460194

ISBN: (纸本)9781665460194

Transmission system expansion planning (TSEP) is the process of identifying time, location, and the number of transmission facilities will be needed in the future. TSEP is an optimization problem with the objective to minimize the investment cost to ensure supply-demand adequacy, subject to constraints on the power balance and the apparent power capability limit of transmission lines. This paper presents transmission system expansion planning based on an optimal power flow and a genetic algorithm (GA). The OPF problem was solved by the PowerWorld simulator, and the GA was coded in Python and developed on the built-in SimAuto function in the PowerWorld simulator. The simulation results from a case study of a modified Garver's 6-bus power system show that the developed methodology can determine the optimal sites and capacities of transmission lines to be added to the system. © 2022 IEEE.

关键词： Genetic algorithms

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Investigation of the Use of Renewable Energy in Microgrid Applications 9

Investigation of the Use of Renewable Energy in Microgrid Ap...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Keerthi Kumar, N. Selvan, Saravana Ravindra, G. Kallappan, Seeniappan. Natrayan, L. Vinayagam, Nadanakumar Bms Institute of Technology And Management Department of Mechanical Engineering Karnataka Yelahanka560064 India Aimst University Faculty of Engineering And Computer Technology Kedah08100 Malaysia Sree Vidyanikethan Engineering College Department of Electrical And Electronics Engineering Andhra Pradesh Tirupati517102 India Velammal Institute of Technology Department of Mechanical Engineering Tamil Nadu Chennai601204 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602107 India Hindustan Institute of Technology And Science Department of Automobile Engineering Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350365092

This research focuses on the primary aspects that influence the usage of renewable energy sources in microgrids. These aspects include energy demand, the amount of solar irradiation, and the capacity of the batteries. In addition to this, it exemplifies the relevance of these factors and the dynamic relationship between them in the operation of microgrid systems. A number of factors must be considered during the design and implementation of micro grids, as this study shows that renewable energy sources have a major impact on them. The paper highlights the benefits of microgrids, which include lower energy costs, increased energy autonomy, and fewer emissions of greenhouse gases. The findings of this research have the potential to make a contribution toward the overall goal of constructing a resilient and flexible energy infrastructure. This might be accomplished with the help of the findings of this study. This may be done by optimizing the construction of micro grids that display higher performance and effectiveness in capturing renewable energy resources using the insights gathered from the inquiry. These micro grids would be optimized to use the information gained from the investigation. © 2024 IEEE.

关键词： Renewable energy

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Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy 7

Real-Time Scheduling for Energy Optimization: Smart Grid Int...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gopalakrushna, A. Raj, Vijilius Helena Vani, V. Divya Singh, Navdeep Jethabhai, Patel Yogeshkumar Chakravarthi, M. Kalyan Vignana Bharathi Institute of technology Telangana Hyderabad India New Horizon College of Engineering Department of Applied Sciences Karnataka Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies Gujarat Visnagar India University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman

ISBN: (纸本)9798350350067

Reliable power system operations, cost reduction, demand and supply balance maintenance, and efficient load scheduling all depend on real-time energy optimisation. Nevertheless, the unpredictability of electric loads and renewable energy sources (RES) makes real-time energy optimization difficult. As a result, the Energy Management Controller (EMC) was developed, which can automatically react to DR signals and resolve energy-related issues. In light of this, EDGE, a hybrid algorithm combining elements of genetic algorithm (GA) and enhanced differential evolution (EDE), is devised in this work. The EDGE algorithm is used to train the EMC to automatically react to DR signals and schedule the 3 classes of household loads - hybrid, non-interruptible, and interruptible - to handle energy management issues. The results show that in terms of power cost reduction, minimizing carbon release, PAR relief, and client discomfort maximization, the suggested EDGE algorithm performs better than benchmark methods. © 2024 IEEE.

关键词： energy optimization renewable energy Smart grid

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AI-Driven Test and Measurement Automation in electronics Manufacturing 9

AI-Driven Test and Measurement Automation in Electronics Man...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Agrawal, Anurag Vijay Raju, Kommisetti Murthy Aparna, P. Sravya, Gade Chandrashekhar, A. Ramya, J. J. P. Institute of Engineering And Technology Department of Electronics And Communication Engineering Uttar Pradesh Meerut250001 India Shri Vishnu Engineering College For Women Department of Electronics And Communication Engineering Andha Pradesh Bhimavaram534202 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Shri Vishnu Engineering College For Women Department of Electronics And Communication Engineering Andhra Pradesh Bhimavaram534202 India Faculty of Science And Technology Icfai Foundation For Higher Education Department of Mechanical Engineering Telangana Hyderabad501203 India St. Joseph's College of Engineering Omr Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600119 India

ISBN: (纸本)9798350365092

Innovative strategies to increase output while decreasing expenses and enhancing product quality are essential in the dynamic electronics manufacturing industry. A robust AI-based system for automating test and measurement tasks in electronics manufacture is detailed in this research. Manual testing and measurement is a part of traditional production procedures;it's labor-intensive, time-consuming, and prone to mistakes. Smarter, more adaptable, and more efficient processes are achieved through the automation of tests and measurements facilitated by AI. The proposed approach makes use of machine learning techniques to sift through mountains of industrial data. In order to identify patterns, outliers, and problems, the AI system examines both past and present data. Reducing the likelihood of defective items reaching the final stage of production, this proactive technique helps identify and rectify issues fast. Product quality, cost, scrap rate, and production yield are all enhanced by AI-driven test and measurement automation in electronics manufacturing. In keeping with Industry 4.0, this ground-breaking method improves the manufacturing processes of electronic devices. Industry intelligence, sustainability, and efficiency are all enhanced by it. © 2024 IEEE.

关键词： Learning systems

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A Deep Convolution Network-Based Pneumonia Identification from Thoracic X-Ray Imagery Scans 6th

A Deep Convolution Network-Based Pneumonia Identification fr...

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6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022

作者： Upreti, Kamal Mishra, Prateet Maheshwari, Manish Vats, Prashant Dhankar, Aisha Batra, Reenu Yadav, Saneh Lata Chawla, Tanvi Bhardwaj, Jayant Department of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management Delhi New Delhi India Department of Computer Science and Information Technology Symbiosis University of Applied Sciences MP Indore India Department of Computer Science and Applications Makhanlal Chaturvedi National University of Journalism and Communication MCNUJC Madhya Pradesh Bhopal India Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India Department of Electronics and Communication Bhagwan Parshuram Institute of Technology GGSIPU New Delhi India

ISBN: (纸本)9789811976599

Pneumonia is an infectious ailment which affects the respiratory system and is caused by microbes that infect the human by affecting their lung air sacs and fill them with fluid. Using chest X-ray is the most popular approach for detecting pneumonia, and the results must be calculated and examined by medical experts. The inconvenient way of detecting pneumonia results in a loss of human life owing to incorrect approach and treatment. With the advancement of computer and information technology, developing an autonomous system that can help in the diagnosis and prevention of pneumonia is now viable, especially if the patient is in a place where the medical facilities and expert doctors are limited. In order to solve this challenge, this project uses deep learning technologies. The neural network was customized to accomplish the difficult task of detecting diseases such as pneumonia in order to aid medical experts in their diagnosis and treatment options. CNN applies a filter to a photo in order to extract data from a chest X-ray. The above-mentioned goal was achieved using Python programming language, as well as deep learning and neural network methodologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chest X-rays Deep CNN Recognition of pneumonia Rectified linear function Thoracic X-ray component

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