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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

computer Systems science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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IoT Enabled Smart Mess with Nutrition Benefits and Waste Management 4

IoT Enabled Smart Mess with Nutrition Benefits and Waste Man...

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4th International Conference on Advancement in electronics and communication engineering, AECE 2024

作者： Utsav, Ankur Kumari, Annu Awasthi, Shivam Kumar, Aniket Rahman, Md Atiqur Galgotias College of Engineering Department of Electronics and Communication Engineering Greater Noida India Iit Patna Department of Computer Science and Engineering Patna India Iilm University Department of Computer Science and Engineering Greater Noida India Gec Vaishali Department of Electronics and Communication Engineering Vaishali India Gec Vaishali Department of Electrical Engg. Vaishali India

ISBN: (纸本)9798350364729

This research explores the implementation of a smart mess system tailored to meet fitness enthusiast dietary needs through the use of RFID technology integrated with a mobile application. The system begins with people inputting basic information such as height, weight, and any food allergies into the app, which calculates their BMI and stores this data on an RFID card. At the mess gate, an RFID receiver grants access to the automated door, allowing students to enter and receive meal recommendations based on their BMI, including what to eat and in what quantities. In the second scenario, the app provides a solution for people who fall ill by enabling them to order food suitable for their symptoms, thus ensuring they receive proper nutrition even when they cannot physically visit the mess. The third case introduces a smart dishwasher system, where leftover waste is efficiently disposed of, and plates are automatically transported via a conveyor belt to the washing section, streamlining the cleaning process. This smart mess system aims to enhance student health, convenience, and operational efficiency within educational institutions. © 2024 IEEE.

关键词： Belt conveyors

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Evaluating and Contrasting the Performance of Web Development Technologies 9

Evaluating and Contrasting the Performance of Web Developmen...

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9th International Conference on communication and electronics Systems, ICCES 2024

作者： Swedheetha, C. Shruthika, C. Preethi, Rubi Vedha Varshini, T.B.J. Maryam, Sabreen Manikandan, P. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350377972

The next generation of webpages is heavily influenced by large scale, high concurrency, and massive amounts of data. Building data-intensive web apps has made *** popular and effective. We employed scenario testing and benchmark testing to examine and contrast the performance of ***, Python-Web, and PHP. Per the testing results, PHP and Python-Web handle a lot less requests in a given amount of time than ***, which is evident from some useful performance metrics. Our findings, taken together, show that *** is the most lightweight and efficient of the three, making it a good choice for I/O-intensive websites;PHP, on the other hand, is best suited for small- to medium-sized applications, while Python-Web is developer-friendly and excels at large web architectures. This research study includes the performance evaluation of web development technologies. © 2024 IEEE.

关键词： Python

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RETRACTED: Identifying LCF-DLT approach and educating engineering students to learn foreground perception in video surveillance

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International Journal of Electrical engineering and Education 2023年第1_SUPPL期60卷 3069-3079页

作者： Sivabalasundari, M. Banumathi, A. Ulaganathan, G. Venkatarajan, T.S. Department of Computer Science and Engineering SACS MAVMM Engineering College Madurai India Department of Electronics and Communication and Engineering Thiagarajar College of Engineering Madurai India Best Dental Science College Madurai India Department of Mathematics GMS MAVMM Polytechnic Madurai India

Real world actions occur often in crowded and dynamic environments. In this present world, surveillance plays a vital role in many applications. Here this paper proposed an intelligent surveillance for detecting moving objects extraction in indoor and outdoor environment using Likelihood Chain Follow algorithm. This signifies that the proposed method is easy to be scrutinized and can recognize the shadow inclination adaptively, which extracts shadow from moving object accurately. This method can be adapted to a more challenging domain like understanding pupil behaviour becomes essential in ambient assist living field especially for monitoring college learner’s especially young students community where motion significantly shows more assorted occurrences in much wider area. It also helps to recognise that campus security cameras are capable of monitoring campus activities, to ensure that policies are properly followed in engineering colleges especially in electrical, communication and computer departments. © The Author(s) 2019.

关键词： Feature extraction

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Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly Detection 3

Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly De...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Krishnan, R. Santhana Gopikumar, S. Muthu, A. Essaki Raj, J. Relin Francis Kumari, D. Abitha Malar, P. Stella Rose Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical AndTechnical Sciences Department of Electronics and Communication Engineering Chennai India Rmk Engineering College Department of Computer Science and Engineering Thiruvallur India J.P. College of Engineering Department of Electronics and Communication Engineering Thenkasi India

ISBN: (纸本)9798350375190

In various regions, the maintenance of manholes is imperative, given the potential consequences for public health and safety. Neglecting this responsibility can result in severe outcomes, including loss of lives and the spread of diseases within the community. To address these challenges, an advanced anomaly detection system is proposed, leveraging deep learning with Autoencoders. Notably, the entire system operates on solar power, aligning with a commitment to environmentally sustainable practices. At the core of the system's functionality is the Autoencoder, a deep learning model to differentiate complex manhole conditions. The Autoencoder is trained on normative sensor data, capturing nuanced patterns inherent in routine manhole states. This training enables the Autoencoder to perform real-time anomaly detection by swiftly identifying deviations such as the presence of noxious gases, irregular sewage levels, or unauthorized attempts at access. When anomalies are detected, signaling potential hazards, the Autoencoder triggers alert messages disseminated to municipal authorities via a cloud server. This proactive approach empowers timely intervention, particularly critical in scenarios involving toxic gases, ensuring the safeguarding of maintenance personnel. Additionally, the system integrates an NFC reader, streamlining authorized access for designated personnel while upholding robust security measures. In essence, the incorporation of Autoencoders into the system furnishes an intelligent anomaly detection infrastructure for manhole surveillance. By continually adapting to normative patterns, the Autoencoder enhances the system's capacity to identify and promptly communicate potential risks, thereby improving the overall safety and efficiency of manhole maintenance practices. © 2024 IEEE.

关键词： Internet of things

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CAVIaR crayfish algorithm enabled Deep Kronecker Network for Wormhole attack mitigation on Network Control System 5

CAVIaR crayfish algorithm enabled Deep Kronecker Network for...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Avina, A. Geetha, M.R. Department of Computer science and engineering Ponjesly College of Engineering TamilNadu Nagercoil629003 India Department of Electronics and Communication Engineering Ponjesly College of Engineering TamilNadu Nagercoil629003 India

ISBN: (纸本)9798350361155

Wireless Sensor Network (WSN) is expansively employed in the Network Control System (NCS). Wormhole (WH) attack detection is a burdensome process, specifically over WSN. A distributed passive state of WH attack makes the network extremely challenging to identify. Here, the CAVIaR crayfish algorithm enabled Deep Kronecker Network (CCA-DKN) is introduced for mitigation of WH attack on NCS. Firstly, the WSN model is simulated and routing is executed by Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Then, three stages namely in-band (IB)-WH detection, out-of-band (OB)-WH detection, and Neighbour Ratio Threshold (NRT) are accomplished for WH detection. After the accomplishment of the NRT stage, the transmission range stage is performed in OB-WH detection. Thereafter, the Packet Delivery Ratio (PDR) and Round Trip Time (RTT) are done on IB-WH detection. Next, the detection of WH attack is performed by ResNeXt-DSAE. Furthermore, ResNeXt-DSAE is designed by combining ResNeXt and Deep Stacked Autoencoder (DSAE). Lastly, attack mitigation is executed, whereupon a percentage of data reduction is carried out by exploiting the Deep Kronecker Network (DKN) and it is trained by the CAVIaR crayfish algorithm (CCA). Furthermore, CCA is an assimilation of Conditional Autoregressive Value at Risk (CAViaR) and Crayfish Optimization Algorithm (COA). Additionally, CCA-DKN achieved high PDR and throughput of 0.769 and 0.533Mbps as well as low delay of 0.712sec. © 2024 IEEE.

关键词： Wireless sensor networks

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Resource Allocation in 6G Network for High-Speed Train Using D2D Outband communication 3rd

Resource Allocation in 6G Network for High-Speed Train Using...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Garg, Aakansha Arya, Rajeev Singh, Maheshwari Prasad Wireless Sensor Network Lab Department of Electronics and Communication Engineering Patna India Department of Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9789819743582

The growth of 6G results in rising data transmission rate in static scenario. To achieve fast communication and computation with quality service in high-speed train is a challenge. This is due to fast handover and Doppler shift. To effectively communicate in high-speed train, the placement of a mobile relay (MR) with small server at the rooftop of train coach is proposed. The base station sends channel state information to mobile relay which stores it in the stack. The resource assignment has been conducted by an improved grey wolf optimization algorithm. The focus is at improving throughput with reduction in latency and energy consumption. The simulation outcomes manifests that the presented algorithm carry-out finer in comparison of the Hungarian algorithm. The accomplished results are refined almost three times in respect of energy consumption, latency, and throughput. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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IoT based Automated Rehabilitation using 3D Printed Hand and Health Monitoring for Paralyzed Patients 5

IoT based Automated Rehabilitation using 3D Printed Hand and...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Govindaraj, S. Maheswari, S. Loganathan, P. Sharanya Nivasini, J.S. Ponkarthika, M. Velalar College of Engineering and Technology Department of Biomedical Engineering Erode India Excel Engineering College Department of Electronics and Communication Engineering Namakkal India Erode Sengunthar Engineering College Department of Computer Science and Design Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350389609

Paralysis is a condition characterized by the loss of muscle function in part of the body, leading to impaired movement and control. It can significantly impact an individual's daily life, mobility, and overall well-being. Paralysis can result from various factors, including spinal cord injuries, strokes, traumatic injuries, neurological disorders, and certain medical conditions. According to the World Health Organization (WHO), millions of people worldwide live with some form of paralysis, with varying degrees of severity. Tracking the improvement of patients using these techniques might be difficult. Rehabilitation results inadequate monitoring of critical health factors including temperature and blood oxygen levels. The suggested approach is creating a 3D - printed hand incorporating Internet of Things sensors. Paralysis patients can benefit from this hand real-time sensory feedback and customized therapy routines. IoT sensors are incorporated in the hand to track the patient's hand movements, blood oxygen saturation (SpO2), heart rate, and muscle activity (EMG). Arduino UNO serves as the central controller that facilitates automation, while the Wi-Fi module and voice control enhance user interaction. Additionally, Peltier modules keep the affected extremities at their ideal temperature, improving patient comfort and promoting blood flow. Suggested approach provides a multi-dimensional approach to enhance their entire quality of life and health condition for people with this difficult medical condition by combining all those features. © 2024 IEEE.

关键词： Sensory feedback

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SmartSignQuiz A Colour-Based Interactive Quiz System for Hard to Talk and Hard to Hear Students 5

SmartSignQuiz A Colour-Based Interactive Quiz System for Har...

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5th International Conference on Sustainable communication Networks and Application, ICSCNA 2024

作者： Subbulakshmi, N. Chandru, R. Ariffa Begum, S. Pavalla, Manju Vallabha Palagiri, Mohammed Muzammil Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Sri Ramakirshna Engineering College Department of Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9798331530013

The growing field of artificial intelligence (AI) provides new opportunities to develop inclusive educational tools tailored to diverse student needs. This study introduces SmartSignQuiz, an innovative colour-based interactive quiz system for hard-to-talk and hard-to-hear students. The system combines facial recognition and colour detection technologies, facilitating real-time participation without relying on verbal communication. SmartSignQuiz ensures accurate student identification through a face recognition module while employing colour-coded responses for quiz interaction. The primary objective is to create an accessible, non-verbal learning platform that promotes active engagement for all students. The study evaluates the system's accuracy, computational efficiency, and impact on educational outcomes, demonstrating its potential to enhance inclusivity in classroom environments. © 2024 IEEE.

关键词： Colour Detection computer Vision Facial Recognition

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