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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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Computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Cyberbullying Detection Using Deep Learning: A Comparative Study

Cyberbullying Detection Using Deep Learning: A Comparative S...

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Cyber Resilience (ICCR), International Conference on

作者： Mouhammd Alkasassbeh Ammar Almomani Amjad Aldweesh Ahmad Al-Qerem Mohammad Alauthman Khalid Nahar Beenu Mago Computer Science Department Princess Sumaya University for Technology Amman Jordan School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Faculty of I.T Yarmouk University Irbid Jordan School of Computing Skyline University College University City of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Cyberbullying has become a serious issue with the rise in social media usage, especially among adolescents. Victims of cyberbullying often suffer from psychological distress, anxiety, depression and even suicidal tendencies. With the rapid growth of user-generated content online, manual detection of cyberbullying is extremely difficult. Hence, automated cyber-bullying detection systems are needed. In recent years, deep learning models have emerged as a promising technique for cyberbullying detection. This paper provides a comprehensive review of the use of deep learning for cyberbullying detection. The key contributions are: (1) a taxonomy of cyberbullying detection techniques, (2) a review of datasets used for model training and evaluation, (3) a comparative analysis of various deep learning models including CNN, RNN, LSTM, GRU and Transformer- based models, (4) a discussion of key challenges and limitations, and (5) suggestions for future research directions. The paper provides a critical perspective on model performance across multiple datasets. Key findings indicate RNNs and specifically LSTM and GRU models demonstrate state-of-the-art performance for sequential cyberbullying detection. The study concludes that deep ensemble models combining LSTMs and pretrained contextual embeddings show significant promise to advance the field.

关键词： Deep learning Training Analytical models Reviews User-generated content Taxonomy Cyberbullying

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

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Computers, Materials & Continua 2022年第6期71卷 5799-5815页

作者： Ahmed S.Almasoud Siwar Ben Haj Hassine Fahd N.Al-Wesabi Mohamed K.Nour Anwer Mustafa Hilal Mesfer Al Duhayyim Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Due to the advanced developments of the Internet and information technologies,a massive quantity of electronic data in the biomedical sector has been exponentially *** handle the huge amount of biomedical data,automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative ***,multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated *** paper presents a Deep Learning based Attention Long Short Term Memory(DLALSTM)Model for Multi-document Biomedical Text *** proposed DL-ALSTM model initially performs data preprocessing to convert the available medical data into a compatible format for further ***,the DL-ALSTM model gets executed to summarize the contents from the multiple biomedical *** order to tune the summarization performance of the DL-ALSTM model,chaotic glowworm swarm optimization(CGSO)algorithm is *** experimentation analysis is performed to ensure the betterment of the DL-ALSTM model and the results are investigated using the PubMed *** comparative result analysis is carried out to showcase the efficiency of the proposed DL-ALSTM model with the recently presented models.

关键词： Biomedical text summarization healthcare deep learning lstm parameter tuning

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Evaluation of Chinese electronic enterprise from business and customers perspectives 2

Evaluation of Chinese electronic enterprise from business an...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Bamansoor, Samer El-Ebiary, Yousef A. Baker Yusoff, Fakhrul Hazman Pathmanathan, P. Ravindran Saany, Syarilla Iryani Ahmad Bakar, Zainab Binti Abu Aseh, Khairi Pande, Bishwajeet Al Moaiad, Yazeed MEDIU Faculty of Computer and Information Technology Malaysia University of Aden Aden Yemen UniSZA Faculty of Informatics and Computing Malaysia Universiti Teknologi MARA Malaysia International Institute of Applied Science Swiss School of Management Netherlands UniSZA Faculty of Informatics and Computing Malaysia MEDIU Faculty of Computer and Information Technology Malaysia Gyancity Research Consultancy Pvt Ltd Motihari India

ISBN: (纸本)9781665432221

The evaluation of companies in the era of modern technologies is due to how and to what extent these companies use such technologies. Provided that modern technologies are dealt with in all aspects of the company, internally and externally. Also, the first company to provide market data and information for free to all shoppers and visitors to its online platform around the world is the Alibaba Group. Alibaba also has its own mission of making the process of buying or marketing everything in the world uncomplicated for everyone, but specifically to introduce Chinese business to the people of the world serving China's vision of conquering the world with its products. Alibaba Group revenues are rising as it reaches more demand than its opponent in the global market. Besides that, the Alibaba Group communication system and how the business connects with the retailer is very interesting. Their business approach has made them famous and distinguished in Asia. Internationally, it is important for Alibaba to gain a higher connection to join well-known western brands, especially luxury and luxury brands, due to the unfulfilled requirements among Chinese customers for the brand that cannot be reached in China. So, it is also possible for Alibaba to be a lucrative brand for retailers around the world to sell their products and as the world grows, that could also be for the Business to Business (B2B) or Customer to Customer (C2C) marketplace. © 2021 IEEE.

关键词： Electronic commerce

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Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

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Computers, Materials & Continua 2023年第3期74卷 6195-6212页

作者： T.Gopalakrishnan Mohamed Yacin Sikkandar Raed Abdullah Alharbi P.Selvaraj Zahraa H.Kareem Ahmed Alkhayyat Ali Hashim Abbas Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipalIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Public Health Department College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq College of Technical Engineering The Islamic UniversityNajafIraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq

The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped *** the aftermath of the COVID-19 outbreak,a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional *** imaging has become a crucial component in the disease diagnosis process,whereas X-rays and Computed Tomography(CT)scan imaging are employed in a deep network to diagnose the *** general,four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks,such as network training,feature extraction,model performance testing and optimal feature *** current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion(CFPADLDF)approach for detecting and classifying *** presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical ***,the proposed CFPA-DLDF technique employs the Gabor Filtering(GF)approach to pre-process the input *** addition,a weighted voting-based ensemble model is employed for feature extraction,in which both VGG-19 and the MixNet models are ***,the CFPA with Recurrent Neural Network(RNN)model is utilized for classification,showing the work’s novelty.A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model,and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.

关键词： Deep learning medical imaging fusion model chaotic models ensemble model COVID-19 detection

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Priority Based Fair Scheduling: Enhancing Efficiency in Cloud Job Distribution

Priority Based Fair Scheduling: Enhancing Efficiency in Clou...

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International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Saydul Akbar Murad Zafril Rizal M Azmi Faria Jerin Brishti Md Saib Anupam Kumar Bairagi Faculty of Computing College of Computing & Applied Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Malaysia Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh South China University of Technology Guangzhou China Khulna University Khulna Bangladesh

In recent years, there has been a growing interest in cloud computing as a means to enhance user access to shared computing resources, including software and hardware, through the internet. However, the efficient utilization of these cloud resources has been a challenge, often resulting in wastage or degraded service performance due to inadequate scheduling. To overcome this challenge, numerous researchers have focused on improving existing Priority Rule (PR) cloud schedulers by developing dynamic scheduling algorithms, but they have fallen short of meeting user satisfaction. In this study, we introduce a new PR scheduler called Priority Based Fair Scheduling (PBFS), which takes into account key parameters such as CPU Time, Job Arrival Time, and Job Length. We evaluate the performance of PBFS by comparing it with five existing algorithms, and the results demonstrate that PBFS surpasses the performance of the other algorithms. The experiment was conducted using the CloudSim simulator, utilizing a dataset of 300 and 400 jobs. In order to assess the performance, three key metrics were employed: flow time, makespan time, and total tardiness. These metrics were chosen to evaluate and analyze the effectiveness of the proposed scheduling algorithm.

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Design and Implementation of Mobile-based Application for Appointments Systems for Vaccinations in Medical Centers

Design and Implementation of Mobile-based Application for Ap...

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International Conference on Computer Theory and Applications (ICCTA)

作者： Mohammad M. Qabajeh Shima Mousa Hamam Abo Saleh Jamila Abo Hasan Department of Applied Computing Faculty of Applied Science Palestine Technical University Kadoorie Arroob Palestine

ISBN: (数字)9781665478540

ISBN: (纸本)9781665478557

The current patient registration and operating procedure in medical centers are time-consuming and somehow tiresome for patients and their families. Normally, patients have to visit the medical center or hospital to register, they have to bring their medical records, and wait to be called by the medical staff. The situation becomes worse when the patients are kids because they cannot wait for long time and their parents do not have enough time to wait for long time. In view of these problems, several appointment applications are proposed to enhance the registration systems and reduce the waiting time. However, these efforts are still face some drawbacks and limitations. In this paper, we have designed an Android-based mobile application to create appointments with doctors in medical clinics and store the patient’s health records. This mobile application is integrated with an interactive website and a comprehensive database to enable the doctors to handle vaccinations appointment, maintain and retrieve patients’ medical records. The managers also can efficiently create, manage and admin the clinic’s work progress. This mobile application will enhance appointment scheduling and improve the quality of healthcare. In addition, the application can reduce the time and effort consumed to perform the vaccinations appointment process and taking the vaccinations on time.

关键词： Processor scheduling Hospitals Databases Operating systems Vaccines Mobile applications Registers

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UNet with Two-Fold Training for Effective Segmentation of Lung Section in Chest X-Ray 3

UNet with Two-Fold Training for Effective Segmentation of Lu...

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3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Gnanasoundharam, J. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Segmentation and evaluation of the Region of Interest (ROI) in medical imaging is a prime task for disease screening and decision-making. Due to accuracy, Convolutional-Neural-Network (CNN) based ROI segmentation has been widely employed in recent years to evaluate a class of medical images recorded using chosen modality. The proposed work aims to demonstrate the segmentation performance of the UNet scheme with a one-fold and two-fold training process. To experimentally verify the merit of the proposed scheme, segmentation of the lung section from the chest X-ray is studied. This research includes the following parts;(i) Resizing the test image and image mask to pixels, (ii) Training the UNet with one-fold and two-fold approaches, (iii) Extracting the ROI, (iv) Comparing the ROI with the mask to compute the image metrics and (v) Validating and confirming the segmentation performance of UNet. The performance of UNet is then verified with UNet+ and UNet++. The investigational ending substantiates that the proposed approach helps to get better Jaccard (>95%), Dice ((>97%), and Accuracy (>98%) in two-fold training compared to other methods considered in this study. © 2022 IEEE.

关键词： Decision making

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Pneumonia Detection in Chest X-ray using InceptionV3 and Multi-Class Classification 3

Pneumonia Detection in Chest X-ray using InceptionV3 and Mul...

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3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Pandeeswaran, C. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi670632 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The lung is a imperative internal organs in human physiology. The abnormality in the lung will cause severe respiratory problems. Pneumonia is a severe lung infection, and early screening and treatment are essential to heal the illness. This research aims to implement a pre-trained InceptionV3 scheme to detect pneumonia in chest X-ray pictures. This scheme consists of the following phases;(i) Image collection and resizing, (ii) Deep-features extraction using InceptionV3, (iii) Feature reduction with firefly algorithm, (iv) Multi-class classification, and (v) Validation. A four-class classifier is employed in the proposed scheme to classify the X-ray into normal, mild, moderate, and severe classes using 5-fold cross-validation. The experimental outcome of the K-Nearest Neighbor (KNN) classifier confirms that this scheme offered a classification accuracy of 85.18% on the considered image database. © 2022 IEEE.

关键词： Classification (of information)

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