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An Improved Deep Structure for Accurately Brain Tumor Recognition

computer Systems Science & engineering 2023年第8期46卷 1597-1616页

作者： Mohamed Maher Ata Reem N.Yousef Faten Khalid Karim Doaa Sami Khafaga Department of Communications and Electronics Engineering MISR Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Electronics and Communications Delta Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain *** using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive *** paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning *** proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor ***,a deep neural network(DNN)model has been proposed to select the most salient features from the ***,it manipulates the extraction of the additional high levels of salient features from a proposed CNN ***,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition *** common datasets have been applied and tested,Br35H and FigShare *** first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no ***,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training *** results show that the proposed methodology has achieved superior performance compared with the current state of art *** proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.

关键词： Brain tumor convolutional neural network gray level co-occurrence matrix noninvasive FigShare dataset Br35H dataset

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A Bioinformatics Approach to Identify Hub Genes Across Schizophrenia, Anxiety, Bipolar Disorder, and Depressive Disorder for Network-Based Drug Discovery 2

A Bioinformatics Approach to Identify Hub Genes Across Schiz...

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Biswas, Md. Emran Hasan, M.D. Galib Hossain, Md. Jobare Basar, Md. Abul Hossain, Md. Selim Department of Electronics and Communication Engineering HSTU Bangladesh Department of Computer Science and Engineering Engineering Uttara University Bangladesh Department of Information and Communication Technology MBSTU Bangladesh

ISBN: (纸本)9798331508227

Mental health disorders represent a significant challenge in society, requiring immediate attention and comprehensive knowledge to mitigate their widespread impacts. Although considerable research has focused on hub gene identification for one or two illnesses, there is a lack of literature examining multiple common disorders and their implications for drug discovery. This study utilizes a bioinformatics methodology to identify shared and critical genes linked to anxiety disorders, bipolar disorders, schizophrenia, and depressive disorders, along with potential therapeutic targets for these conditions. We methodically collected gene-related data from the National Center for Biotechnology Information (NCBI) for these disorders. Through protein-protein interaction analysis, we discovered hub genes, including GSK3B, MAPT, NR3C1, ESR1, TNF, SOD1, APP, CREB1, NOS1, APOE, GNB3, DISC1, SIRT1, CACNA1C, and BDNF. Additionally, we identified potential therapeutic agents targeting these hub genes, including Kaempferol, Simvastatin (CTD 00007319), Luteolin, Clozapine (CTD 00005693), Curcumin, Resveratrol, Melatonin (CTD 00006260), Felodipine, Apigenin, Docosahexaenoic Acid, Diazepam, and Alprazolam, using the DSigDB database. This study seeks to elucidate the genetic connections across mental health diseases and to find therapeutic compounds that may provide common treatment advantages. ©2024 IEEE.

关键词： Diseases

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Search Engine for QnA using Distributed Inverted Index System 3

Search Engine for QnA using Distributed Inverted Index Syste...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Dey, Snehasis Moharana, Bhimasen De, Utpal Chandra Samant, Tapaswini Behera, Trupti Mayee Banerjee, Shobhan College of Engineering Electronics & Telecommunication Engineering Bhubaneswar India Lovely Professional University Department of Computer Science & Engineering Punjab India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9798350381931

The internet along with its global uses has also evolved as a platform where various people can pose their questions on certain sites and get them answered by others from the same domain. Certain sites like Quora, Reddit, Yahoo Answers, etc. are sites enabling people to enhance their range over the net to find solutions to various issues or answers to various questions. But this leads to redundancy in data & hence increased memory consumption where different users can pose the same question in different ways. The same thing stands for the answers posted by different users for the same question in various ways. In this paper, we have worked on a search engine system for Question & Answers where given a question, we can find similar questions with the same semantic meaning using Elastic Search which uses an inverted index system & looks at various aspects of the formulated problem statement. © 2024 IEEE.

关键词： Search engines

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Investigating the impact of sensor axis combinations on activity recognition and fall detection: an empirical study

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Multimedia Tools and Applications 2024年 1-40页

作者： Kavuncuoğlu, Erhan Özdemir, Ahmet Turan Uzunhisarcıklı, Esma Department of Computer Technology Gemerek Vocational School Cumhuriyet University SivasTR-58840 Turkey Department of Electrical and Electronics Engineering Engineering Faculty Erciyes University KayseriTR-38039 Turkey Department of Electrical and Electronics Engineering Yozgat Bozok University Engineering Faculty YozgatTR-66900 Turkey

Activity recognition is a fundamental concept widely embraced within the realm of healthcare. Leveraging sensor fusion techniques, particularly involving accelerometers (A), gyroscopes (G), and magnetometers (M), this technology has undergone extensive development to effectively distinguish between various activity types, improve tracking systems, and attain high classification accuracy. This research is dedicated to augmenting the effectiveness of activity recognition by investigating diverse sensor axis combinations while underscoring the advantages of this approach. In pursuit of this objective, we gathered data from two distinct sources: 20 instances of falls and 16 daily life activities, recorded through the utilization of the Motion Tracker Wireless (MTw), a commercial product. In this particular experiment, we meticulously assembled a comprehensive dataset comprising 2520 tests, leveraging the voluntary participation of 14 individuals (comprising 7 females and 7 males). Additionally, data pertaining to 7 cases of falls and 8 daily life activities were captured using a cost-effective, environment-independent Activity Tracking Device (ATD). This alternative dataset encompassed a total of 1350 tests, with the participation of 30 volunteers, equally divided between 15 females and 15 males. Within the framework of this research, we conducted meticulous comparative analyses utilizing the complete dataset, which encompassed 3870 tests in total. The findings obtained from these analyses convincingly establish the efficacy of recognizing both fall incidents and routine daily activities. This investigation underscores the potential of leveraging affordable IoT technologies to enhance the quality of everyday life and their practical utility in real-world scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wearable sensors

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Hybridization of DNA Computing and Chaotic System for Ensuring the Randomness and Integrity of Image Data 4

Hybridization of DNA Computing and Chaotic System for Ensuri...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Dhakal, Madhav Shakya, Subarna Tribhuvan University Central Department of Computer Sci. & Information Technology Kathmandu Nepal Pulchowk Campus Tribhuvan University Department of Electronics and Computer Engineering Institute of Engineering Kathmandu Nepal

ISBN: (纸本)9798331523923

In this paper, the concept of DNA computing and 3-D chaotic system is implemented to maintain the security of grayscale image while transmitting through the publicly available network. Here, random selection technique for sequencing of DNA bases with secret key generated through chaotic sequence encryption technique is purposed. Generation of permutation box for diffusion of image pixels carried out on the basis of chaotic system, which create more randomness and difficult to retrieve the secret key and maintain confidentiality from the intruder. System parameter and initial values of chaotic system work as the private key. In this proposed concept, first, plain image is divided in to various block of 512 bit size and padding is necessary if the last block does not contain the block of 512 sizes and then data obtained from the blocks are XOR with the key value obtained through chaotic system. After that, results obtained from this operation are transformed in to the cipher image with using the concept of Central Dogma of Molecular Biology. Experimental result was thoroughly examined using various techniques including histogram analysis, chi-square test, information entropy assessment, differential attack and the result shows that the proposed concept is secure and maintain the randomness, confidentiality and integrity of image data. © 2025 IEEE.

关键词： Gene encoding

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Land Cover Segmentation for Agricultural Monitoring Using Synthetic Aperture Radar (SAR) Imagery with Convolutional Neural Networks and Attention-Based Transformer Models

Land Cover Segmentation for Agricultural Monitoring Using Sy...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Prakash, E.P. Sathiya, K.G. Manoranjitham, S. Sekar, G. Munaf, S. Santhia, R.K. Dr. N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore641048 India Arunai Engineering College Department of Electronics and Communication Engineering Tiruvannamalai606604 India Agni college of Technology Department of Information Technology Chennai600130 India Vsb College Of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore642109 India Sri Ramakrishna Institute of Technology Department of Electronics and Communication Engineering Coimbatore641010 India Pondicherry University Department of Computer Science and Engineering 605014 India

ISBN: (纸本)9798331509828

Automated classification of land cover types is one of the most crucial aspects within many remote sensing applications, including the monitoring of agricultural land and territories. This work proposes a novel approach for division of land covers using a hybrid of CNNs and Attention-based Transformer networks. CNNs are adopted to extract the spatial features from different levels of the SAR image, while Transformer Model Arch applies self-attention functions to explore the long-range dependency and contextual relation of the entire image. The model learning process is performed end-to-end from multi-temporal SAR data to differentiate different types of land covers, including crops, vegetation, water, and urban surfaces. In this case, we apply the proposed approach to different agricultural areas to show that it outperforms traditional methods, such as thresholding and other machine learning approaches. The results highlight the viability of this mixed form to solve the noise and changeable nature of SAR imagery, in terms of enhancing the use of the agricultural monitoring application. © 2025 IEEE.

关键词： Convolutional neural networks

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A Deep Analysis on Security Aspects of Cloud Computing Ontologies 5

A Deep Analysis on Security Aspects of Cloud Computing Ontol...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Gobi, N. Kapse, Vinod Mansiram Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

The primary objective of this research is to get a comprehensive understanding of Cloud Computing ontologies, their applications, and their specific areas of interest. The proposed research methodology for this systematic review comprises a well-defined research topic, criteria for including and excluding studies, and a comprehensive search strategy to identify relevant source papers for the review. The chosen research is categorized into four main areas: elucidation of cloud resources and services, elaboration of cloud security, clarity of cloud interoperability, and understanding of cloud services discovery and selection. Utilizing ontologies to improve the security and smooth integration of cloud computing services uncovers various challenges and identifies areas for more research. © 2024 IEEE.

关键词： Cloud Computing Cloud service ontology systematic review

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5G Advanced: A Step Towards 6G

5G Advanced: A Step Towards 6G

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Routray, Sudhir K. Singh, Monika Samal, Laxmipriya Samal, Debashisa Sarangi, Susanta K. Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India SoA University Department of Electronics and Communication Engineering Bhubaneswar India

ISBN: (纸本)9798350390759

This article explores the features and proximity of 5G advanced (well known as 5.5G) to 6G cellular communication system, emphasizing its speed, connectivity, and intelligence. Expected benefits of 5.5G include an enhanced user experience, industrial revolution, and support for the Internet of Things (IoT). The motivation behind 5.5G arises from addressing 5G limitations, accommodating IoT growth, enabling real-time applications, providing personalized experiences, and driving economic growth. Anticipated applications span from enhanced mobile broadband to ultra-reliable low latency communication and IoT-based massive machine-type communication, extending to personalized experiences, next-gen gaming, connected healthcare, and advanced education. Despite being in development, 5.5G holds immense significance for bridging the physical and digital world, fostering innovation, and unlocking the potential of the digital age. © 2024 IEEE.

关键词： 5G mobile communication systems

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Impact of fraud on revenue loss, detection features, and energy fraud identification 1st

Impact of fraud on revenue loss, detection features, and ene...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Indarjit, Elisha Balyan, Vipin Adonis, Marco Department of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape Town Bellville South Africa

ISBN: (纸本)9781032874036

The increase of fraudsters and fraud attacks on the communication network plays a major role in the loss of revenue, network abuse, and degradation of services. Communication and cloud networks belong to separate companies and consist of handover points from one network to another. The scope is to bring the communication and cloud networks closer and understand network traffic profiles using Software-Defined Networking (SDN) concept. The SDN controller will serve to route the extracted tapped data to a central server instead of making use of the live network. To apply policies to the traffic and identify which user traffic is a fraud case, and further to send a block signal to the network charging element on the communication network. The flow process will be automated, and to protect the network from fraud attacks, using the SDN controller in a seamless approach and maintaining the performance of the network. The article defines the impact of fraud and fraud case detection. At which Smart Grid leverages intelligent communication technologies to modernize the electric infrastructure and raises new vectors for cyber risks and energy frauds. While services on the network require a secure platform that serves to block fraudster. The main purpose is to achieve a higher energy/service fraud detection rate than similar work in the field, to provide a solution that integrates within the traditional network, and further blocks the fraud within the network. © 2025 the Author(s).

关键词： Cloud network Communication network Detection Fraud Software-Defined Network

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Secured intelligent transportation with privacy retention through blockchain framework

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10507-10521页

作者： Kaur, Gaganpreet Shobana, M. Kavin, F. Sellakumar, S. Meenakshi, D. Bharathiraja, N. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology KTR Chengalput Tamil Nadu Chennai India Department of Electronics and Instrumentation M.S. Ramaiah Institute of Technology Karnataka India Department of Mechanical Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu India

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture. © 2024 - IOS Press. All rights reserved.

关键词： Big data

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