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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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An Analysis of Anonymous MQTT for Publish-Subscribe-Based IoT Networks

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IEEE Transactions on Network science and engineering 2025年

作者： Fukushima, Yudai Tsunamoto, Hotaru Sakai, Kazuya Sun, Min-Te Ku, Wei-Shinn Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo191-0065 Japan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States

Message queuing telemetry transport (MQTT) is widely used as a communication primitive in publish-subscribe-based IoT applications. However, the current MQTT standard does not support the privacy of IoT devices and users. Therefore, in this paper, we address the anonymous communications for MQTT-based IoT networks by tailored onion routing. To this end, we first introduce the concept of distributed IoT broker networks, in which a collection of IoT brokers are interconnected with each other. These brokers serve as onion routers. Then, we propose a generic anonymous messaging protocol (A-MQTT) for publish-subscribe-based IoT systems. Unlike typical anonymous communications, there are multiple destination IoT devices, called subscribers, in MQTT contexts. Hence, the privacy notions, such as traceable rate, device anonymity, and path anonymity, are redesigned. To illuminate the fundamental privacy issues, closed-form approximations for these privacy metrics are modeled. In addition, simulation results demonstrate that our A-MQTT significantly improves the security performance and that our proposed models closely approximate the various privacy performance. Furthermore, a proof-of-concept protocol is implemented on a Raspberry Pi 4 in order to show that the proposed A-MQTT can be deployed in an IoT environment. © 2013 IEEE.

关键词： Anonymity

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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Improved Video Steganography with Dual Cover Medium,DNA and Complex Frames

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computers, Materials & Continua 2023年第2期74卷 3881-3898页

作者： Asma Sajjad Humaira Ashraf NZ Jhanjhi Mamoona Humayun Mehedi Masud Mohammed A.AlZain Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan School of Computer Science SCSTaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubangJaya47500SelangorMalaysia Department of Information Systems College of Computer and Information SciencesJouf UniversityKSA Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

The most valuable resource on the planet is no longer oil,but *** transmission of this data securely over the internet is another challenge that comes with its ever-increasing *** order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic *** objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video *** the previous approach,DNA was used only for frame *** this DNA is compromised,then our frames with the hidden and unencrypted data will be *** the frame selected in this way were random frames,and no consideration was made to the contents of *** data in this way introduces visible artifacts in *** the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex *** chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random *** results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual ***,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved *** proposed methodology has been implemented in Matlab.

关键词： Video steganography data encryption DNA embedding frame selection

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Health-Care Monitoring of Patient using CNN based Model in Internet of Things 1

Health-Care Monitoring of Patient using CNN based Model in I...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Parameshachari, B.D. Hemalatha, K.L. Rahebi, Javad Baker, Mohammed Rashad Upendra Roy, B.P. Nitte Meenakshi Institute of Technology Department of Electronics and Communications Engineering Bengaluru India Sri Krishna Institute of Technology Department of Information Science and Engineering Bangalore India Istanbul Topkapi University Department of Software Engineering Turkey University of Kirkuk College of Computer Science and Information Technology Department of Software Kirkuk Iraq Channabasaveshwara Institute of Technology Gubbi Department of Electronics and Communication Engineering Tumkur India

ISBN: (纸本)9798350323795

There is rising public concern about exposure to radiofrequency (RF) electromagnetic fields (EMF) as more and more wireless communications become concentrated in everyday living surroundings. There are a number of obstacles, primarily originating from infrastructure expenses, but recent technology breakthroughs are shifting attention to Internet of Things (IoT) devices that enable automatic and continuous real-time EMF monitoring. The Internet of Things (IoT) has made the world a better place by expanding the capabilities of telemedicine and allowing for more precise remote monitoring of patients. Improved healthcare facilities for both patients and doctors/hospitals are only one way that IoT is making a significant impact in the medical field. Five main parts make up the proposed system: patient data gathering, primary report creation, hospital care, pharmacy care, and diagnostics. This study presents an Internet of Things (IoT)-based healthcare scheme accomplished of real-time nursing of energetic signs and environmental factors for discrete patients. Five sensors, including a heart-rate monitor, a body temperature monitor, a room temperature monitor, a carbon monoxide monitor, and a carbon dioxide monitor, are utilised to collect data from the hospital setting. Medical personnel receive patient updates through a portal and use this data for further diagnosis and care. The success of the system demonstrates that the built prototype is ideal for healthcare monitoring. This study has investigated unique approaches to the automatic construction of convolutional neural network (CNN) topologies, and it has also developed a new method for diagnosis. The results of the trials prove that the recommended algorithm achieves improved than the state-of-the-art techniques. © 2023 IEEE.

关键词： Diagnosis

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Effects of Online Educational System on Personal Health of Students and Teachers in COVID-19 Crises

Effects of Online Educational System on Personal Health of S...

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International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

作者： Shaikh, Fariha Massan, Shafiq-ur-Rehman Bhatti, Sania Chandio, Shafqat Shahzoor Shaikh, Muhammad Mujtaba Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Department of Computer Science Newports Institute of Communications and Economics Karachi Pakistan Department of Basic Sciences and Related Studies Mehran University of Engineering and Technology Jamshoro Pakistan

ISBN: (纸本)9783031270987

Healthful living is the central characteristic to live a wonderful life which is badly affected when people are physically inactive. Physically unfit people are restricted in several walks of life. Therefore, physical fitness and activeness is key to live a healthy life. COVID-19 restricted people due to lock-downs. Several physical activities were seized. Along with human lives, all businesses, import-export, economies of countries and many other areas were affected. Similarly, educational system was badly affected. Due to lockdowns, it was not possible to carry traditional educational system (TES) in COVID crises, therefore, everyone had to move towards online educational system (OES). By adopting OES, going to academies, attending physical classes, performing practical sessions, sports, face-to-face interactions with friends, being ready for outdoor and several other physical activities were eliminated from student’s and teacher’s routine. Whereas, excessive screen time, continuous sitting, increased laziness, weight gain, spine issues and other health problems were reported. This work focuses higher educational institutes – universities – of Pakistan, and attempts to investigate seriousness of health risks, diseases and chances of being unhealthy during such COVID times. The aim is to investigate the unsettled health issues by gathering the perceptions of students and teachers through surveys. With the help of different statistical techniques and comparisons, responses are analyzed to identify the health problems. To avoid and prevent students and teachers from recognized health issues in future, some suitable and relevant suggestions have been provided to audience with the help of existing studies and doctors’ opinion, which will help academia to adopt OES smoothly and more effectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Resume Recommendation using RNN Classification and Cosine Similarity 7th

Resume Recommendation using RNN Classification and Cosine Si...

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7th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Huseyinov, Ilham Diallo, Issa Raed, Mhd Wasim Software Engineering Department Istanbul Aydin University Istanbul Turkey Graduate Institute of Science Artificial Intelligence and Data Science Program Istanbul Aydin University Istanbul Turkey Computer Engineering Department Istanbul Aydin University Istanbul Turkey

ISBN: (纸本)9783031437885

One way to save time and resources in the human recruitment and hiring process is to post open job positions on the Internet, but the overload of applications creates challenges for hiring managers and companies to select an adequate candidate. One of the solutions is to apply intelligent tools such as Deep Learning and recommender system algorithms to speed up the hiring process and identify the right candidates. In this paper, we propose a two-fold algorithmic approach to 1) building an RNN classifier for resume classification;2) using cosine similarity for resume recommendation to find a candidate that fits job requirements best after selecting all the resumes that belong to the right category. The performance of the proposed RNN classifier is evaluated in terms of accuracy, precision, recall, F1-score, and confusion matrix criteria. The experiment results have shown that the RNN classifier performs better than the other classifiers such as GNB, Linear SVM, RF, and BERT on the same dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recommender systems

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K-Banhatti Invariants Empowered Topological Investigation of Bridge Networks

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computers, Materials & Continua 2022年第12期73卷 5423-5440页

作者： Khalid Hamid Muhammad Waseem Iqbal Erssa Arif Yasir Mahmood Ahmad Salman Khan Nazri Kama Azri Azmi Atif Ikram Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of Lahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Informatics and Applied Mathematics University Malaysia TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of Lahore54000Pakistan

Any number that can be uniquely determined by a graph is called graph *** the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation *** the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular *** this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or *** review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti *** concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

关键词： Bridge networks invariants k-banhatti indices maple network graph molecular graph

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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