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Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

computers, Materials & Continua 2022年第12期73卷 5299-5314页

作者： K.Pradeep Mohan Kumar Jenifer Mahilraj D.Swathi R.Rajavarman Subhi R.M.Zeebaree Rizgar RZebari Zryan Najat Rashid Ahmed Alkhayyat Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Information Technology School of Engineering and TechnologyKebridehar UniversityKebridehar250Ethiopia Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Computer Network Department Technical College of InformaticsSulaimani Polytechnic UniversitySulaimaniIraq College of Technical Engineering The Islamic UniversityNajafIraq

Recently,smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available *** the advantages of smart cities,security remains a huge challenge to be ***,Intrusion Detection System(IDS)is the most proficient tool to accomplish security in this ***,blockchain exhibits significance in promoting smart city designing,due to its effective characteristics like immutability,transparency,and *** order to address the security problems in smart cities,the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning(PPSF-BODL)*** proposed PPSFBODL model includes the collection of primary data using sensing ***,z-score normalization is also utilized to transform the actual data into useful ***,Chameleon Swarm Optimization(CSO)with Attention Based Bidirectional Long Short TermMemory(ABiLSTM)model is employed for detection and classification of *** is employed for optimal hyperparameter tuning of ABiLSTM *** the same time,Blockchain(BC)is utilized for secure transmission of the data to cloud *** cloud server is a decentralized,distributed,and open digital ledger that is employed to store the transactions in different methods.A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSFBODL model over recent approaches with a maximum accuracy of 97.46%.

关键词： Blockchain smart city security intrusion detection system chameleon swarm optimization deep learning parameter tuning

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Deep Convolutional Neural Networks for Early-Stage Detection and Prognostication of Lung and Colon Cancer 2

Deep Convolutional Neural Networks for Early-Stage Detection...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Laxmikant, Kumar Arthi, A. Vinodhini, V. Natarajan, B. Bhuvaneswari, R. Selvam, Prabu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Karpagam College of Engineering Computer Science and Technology Coimbatore India University College of Engineering Department of Electronics and Communication Engineering Ariyalur India VIT University School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350317558

Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. These results are mirrored in testing, where the accuracy reaches 96.59% and the loss value is 0.0441. Beyond just numerical achievements, this research revolutionizes cancer diagnostics by providing a sophisticated tool for personalized and early detection. This study surpasses the mere influence on individuals and extends its reach to bring hope for society and the economy by tackling the pervasive weight of cancer. It distinguishes itself in the realm of advanced medical investigation, affirming the efficacy of the model and inspiring additional inquiry, encompassing the utilization of more extensive sets of data and implementation in diverse medical domains. © 2024 IEEE.

关键词： Colon Cancer Convolutional Neural Network Lung Cancer

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

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computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Role of Cloud computing in Education 2

Role of Cloud Computing in Education

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alduhisat, Masar H. Alkhresha, Hala G. Al-Dmour, Nidal A. Ateeq, Karamath Maaitah, Omer N. College Of Engineering Mutah University Department Of Computer Engineering Jordan School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Jordan

ISBN: (纸本)9798350394962

Education is a crucial factor in economic and social progress are interdependent and crucial for societal development. In the present-day society, students are increasingly technologically savvy, and learning and teaching are incorporating new technologies. Various educational institutes utilize cloud-based applications offered by service providers enables students and other users to carry out academic and various tasks. The education sector is experiencing advancement and growth due to the influence of cloud computing. educators and students are able to access a diverse array of resources and applications easily and at reasonable cost, enabling them. Cloud computing offers storage solutions that are both dependable and transparent. This paper examines the significance of cloud computing in the education sector and how we can leverage cloud-based technology to facilitate the sharing of high-quality education. © 2024 IEEE.

关键词： Students

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Detection of Diabetic Retinopathy using Transfer Learning 14

Detection of Diabetic Retinopathy using Transfer Learning

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Gupta, Himanshu Arora, Neha Dutt, Sarthika Gulati, Aditi Gulati, Akriti Coer University College of Smart Computing Roorkee India College of Engineering Roorkee Department of Computer Science & Engineering Roorkee India

ISBN: (纸本)9798350335095

Diabetic retinopathy is the problem of diabetes that affects the eyes. It is caused when one of the parts of an eye called retina or say the blood vessels in the tissue at the back of the eye is damaged. The persons who had diabetes for more than 10 years are affected the most. It is observed that it is most common in the people of 70-79 years of age group. Early symptoms of diabetic retinopathy are found to be the blurriness, difficulties in perceiving colours and dark areas of vision. Chronic retinopathy can also cause *** this disease shows very few symptoms, as a result the treatment is not given on time. Due to which, many people are being affected by this. The doctor who manually detects this disease is called ophthalmologists. The process of detecting diabetic retinopathy from the retinal images manually is a time-consuming process and also the diagnosis depends upon the expertise of the *** this study, the authors collected two datasets for diabetic retinopathy, consisting of retinal images categorized into five groups. In the first dataset, the authors computed handcrafted HOG features and utilized the Support Vector Machine (SVM) algorithm for classification. They achieved an accuracy of 71.9% using this approach. In the second dataset, the authors employed a deep Convolutional Neural Network (CNN)-based framework to detect diabetic retinopathy and determine its severity level from retinal images. Early detection of the disease can lead to better disease management, and medical intervention can slow down disease progression. The proposed framework offers an easy method for *** paper utilized two datasets. In the first dataset, a machine learning algorithm was employed, achieving an accuracy of 71.9%. In the second dataset, three state-of-the-art transfer learning methods were employed. It was discovered that deep learning models outperformed the machine learning model. Among the deep learning models, the InceptionResNetV2 model e

关键词： Diseases

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Signature Based Identification using Locality-Sensitive Hashing for IoT Devices 2

Signature Based Identification using Locality-Sensitive Hash...

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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Singh, Amit Sindhu Madhuri, G. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

On current days, the growing applications in Internet of Things (IoT) is increasing in various real-time sectors as smart city applications (air pollution, atmospheric noise canceling and traffic flow management). Now, these technologies are combined to provide the comfort configuration for each application. However, malware / virus are highly possible to extract the device software or hardware information in IoT. All sensed information about the environment or any event is stored in these devices which are hacked by the malicious attackers. To overcome these adversities, we introduced the concept of locality sensitive hash (LSH) for IoT device trustworthiness identification. The main objective of our proposed system is to allow secure transfer of data from the resource constrained devices. In this project, the resource constrained devices are the wireless sensor nodes. The experimental analysis confirmed that our proposed approach is suitable for the application or environment with resource constrained devices. Also, the time, energy, latency and packet overhead is considerably reduced than the existing systems considered for this project. © 2023 IEEE.

关键词： Internet of things

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Weighted Average Ensembled Probability Pipeline of Pre-trained CNN Models for Hand Gesture Classification 15

Weighted Average Ensembled Probability Pipeline of Pre-train...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gunti, Rahul Rohan Reddy, B. Swathi, Gunti Sharath Kumar Reddy, G. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

The automatic detection of sign language from hand gesture images is fundamental for effective human-computer interaction, especially for individuals with hearing and speech disorders. Achieving accurate detection and classification of sign language gestures is paramount. However, accurately recognizing these gestures frequently presents challenges due to dull backgrounds, variations in gesture orientation, and fluctuations in lighting conditions. Previous approaches tried to overcome these challenges but still the model performance lacks recognition accuracy and visual quality. To address this need for accuracy, we proposed a Weighted Average Ensembled Probability Pipeline composed of EfficientNetB7, ResNet-50, and Xception models. Leveraging the Extensive Hand Gesture Recognition Database (HGRD), comprising 20,000 images, our individual models achieved notable accuracies: 96.498 % for EfficientNetB7, 93.2 % for ResNet-50, and 90.175 % for Xception. Through ensemble learning, we integrate these models to attain an impressive overall accuracy of 98.95 %. This research contributes to robust gesture recognition system and underscores the significance of ensemble techniques in enhancing performance for Gesture recognition systems. © 2024 IEEE.

关键词： Gesture recognition

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"Socially Assistive Robot Privacy Model": A Multi-model Approach to Evaluating Socially Assistive Robot Privacy Concerns 16th

"Socially Assistive Robot Privacy Model": A Multi-model Appr...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Collins, Sawyer Stanojević, Čedomir Bennett, Casey Henkel, Zachary Henkel, Kenna Baugus Abbott, Nikki M. Bethel, Cindy L. Śabanović, Selma Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States College of Behavioral Social and Health Sciences Clemson University ClemsonSC United States College of Computing and Digital Media DePaul University ChicagoIL United States Department of Computer Science and Engineering Mississippi State University StarkvilleMS United States

ISBN: (纸本)9789819635214

As socially assistive robots (SARs) enter more diverse care settings, including users’ homes, it is critical to identify the shifting privacy risks and dimensions of privacy this technology and its data collection capabilities may affect. We propose a new model of privacy to address the complex nature of SARs as a multimodal technology within the healthcare space. To construct this new model, we combine a previous three-dimensional model from the healthcare literature and synthesize it with a seven-dimension technology-related model. We then use this new combined model to analyze self-report data from several workshops with prospective users of the dog-like robot Therabot to map out the dimensions of privacy identified as future concerns for clinicians and those living with depression. Finally, we suggest this model can be used in future studies to support the in-depth exploration of privacy implications of SARs within healthcare through discussions about privacy among clinicians, those receiving care, and robot designers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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