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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Hemalatha, K. Ganga Devi, S.V.S. Deepika, V. Madanapalle Institute of Technology and Science Dept. of Computer Science & Engineering-Artificial Intelligence Andhra-Pradesh Madanapalle India Madanapalle Institute of Technology and Science Dept. of Computer Science & Engineering-Cyber Security Andhra-Pradesh Madanapalle India Sri Venkateswara College of Engineering Dept. of Computer Science & Engineering Andhra-Pradesh India

ISBN: (纸本)9798350328042

IoT-driven automatic vehicle accident rescue system implemented with Proteus is an inventive solution aimed at enhancing emergency response and rescue operations following vehicle accidents. Given the rising vehicle usage alongside population growth, the frequency of accidents has also surged over time, leading to unfortunate loss of life due to delays in ambulance arrival at accident scenes. Swift transportation of accident victims to medical facilities is of paramount importance. Upon occurrence of an accident, the proposed "IoT-driven automatic vehicle accident rescue system implemented with Proteus"immediately transmits accident details to the ambulance service, investigation unit, and designated emergency contacts. Simultaneously, the accident's precise location is shared via a Google Maps link to the users' smart devices equipped with mobile network connectivity. This facilitates efficient location identification for both ambulance services and police departments. Comprising elements such as accelerometers, GPS (Global Positioning System) modules, GSM (Global System for Mobile Communication) modules, motors, buzzers, LEDs, and more, the system seamlessly integrates these components with a central microcontroller unit using Proteus software. The core objective of this initiative revolves around two key goals: rescuing drivers involved in accidents and expediting victim recovery during the critical early stages of mishaps. © 2023 IEEE.

关键词： Global system for mobile communications

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Experimental Analysis of an Efficient Dental Carries Prediction System Based on Improved Convolutional Neural Network (iCNN) Principle

Experimental Analysis of an Efficient Dental Carries Predict...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ranjith, S. Vanitha Lakshmi, M. Sujatha, V. Govindasamy, Suresh Giri, Pallavi Al-Qawasmi, Khaled Jeppiaar Engineering College Department of Ece Chennai India Soe Presidency University Department of ECE. Bengaluru India S.A. Engineering College Department of Master of Computer Applications Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Laxminarayan Innovation Technological University Department of Civil engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Dental caries is a kind of infectious illness that weakens the structure of teeth, leading to cavities. Because of the discomfort and expense of treatment, studies on dental caries have focused on early diagnosis, as it is one of the most common oral health problems. Due to the high costs and lengthy research times associated with oral healthcare, artificial intelligence has recently been employed to create models that can forecast the likelihood of dental caries. The agony and expensive price of therapy have prompted this investigation toward early detection. Models that may forecast the possibility of dental caries have recently been developed using artificial intelligence. This is in response to the high expenditures and significant time commitments connected with medical research in oral healthcare. In this study, a new method for efficiently identifying dental caries is presented using an Improved Convolutional Neural Network (iCNN). To test how well the suggested scheme works, it is cross-validated with the standard method called Support Vector Machine (SVM). This paper's suggested method, iCNN, demonstrates that dental practitioners may greatly benefit from deep learning to aid in the early diagnosis and treatment of dental caries. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing Heart Disease Detection: An SVM-Based Methodology with Advanced Feature Selection 4

Optimizing Heart Disease Detection: An SVM-Based Methodology...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kumar, V. S. Pavan Rao, G Srinivasa Nagamani, Samineni Rajender, R. Grandhi, Appalaraju Neelima, P. Mallareddy University Department of Cyber security Hyderabad India Hyderabad India Swarna Bharathi Institute of Science & Technology Department of Cse Khammam India Lendi Institute of Engineering and Technology Department of Cse Vizianagaram India Aditya University Department of Cse Surampalem India Srkr Engineering College Department of Cse Bhimavaram India

ISBN: (纸本)9798331529635

This study develops a clear framework to predict heart diseases using a Support Vector Machine (SVM) classifier. The methodology starts with overall data preparation which covers data cleaning which involves confirming the cleanliness of the data by clearing it of all smoothness and, handling of missing values which are solved appropriately, correcting data inconsistencies where such is the case and handling logical data errors if any. To address the class imbalance problem, the Synthetic Minority Over-sampling Technique (SMOTE) is used to create new instances from the minority class hence improving the balance of the data set. The high variability when creating the model is also handled through the usage of IQR which isolates the outliers and prevents excessive impact on the potential model. To enhance the relevance of the selected features and reduce multicollinearity Recursive Feature Elimination (RFE) and Correlation Coefficient analysis techniques are used. The accuracy, precision, recall and F1 scores for the SVM model applied to the SMOTE-balanced dataset with selected features are 98.24%, 89.43%, 93.64%, and 88.79% respectively. This also improves the accuracy of the heart disease model and, more importantly, makes the model clinically usable which involves retrieval drawbacks that are inherent to computational solutions as well as kernel selection. © 2024 IEEE.

关键词： Support vector machines

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NEST: Strong Key-Insulated Password-Based Shared-Custodial Blockchain Wallets 20th

NEST: Strong Key-Insulated Password-Based Shared-Custodial B...

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20th International Conference on Information security and Cryptology, Inscrypt 2024

作者： Gao, Birou Zhang, Rui Xiao, Yuting Zou, Huan Institute of Information Engineering Chinese Academy of Sciences Beijing China Key Laboratory of Cyberspace Security Defense Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology Dongguan University of Technology Guangdong China Information Security Center China Mobile Communication Corporation Beijing China

ISBN: (纸本)9789819647309

Blockchain wallets protect users’ assets by securely storing keys. Research has explored various aspects, including BIP32 for key derivation in cold/hot settings and PDPKS further enhancing key insulation. However, most wallets are either user-managed or exchange-hosted, creating centralized points of failure. Shared-custodial wallets allow users and service providers to collaboratively manage keys, which can lead to potential corruption while still safeguarding asset security. Nonetheless, the need for server involvement in each signature poses challenges to user privacy and efficiency. To address these limitations, we propose NEST, the first efficient wallet that simultaneously satisfies strong key insulation, shared custody and privacy. We restrict malicious users’ access to the server through password-based identity authentication and introduce blindness and unlinkability to prevent malicious servers from associating transactions with specific users. Additionally, we have designed two modes: interactive signing for large transactions with high security requirements, and non-interactive signing for small, high-frequency transactions. Our construction of NEST is based on bilinear pairing and an IND-CCA-secure public key encryption scheme, accompanied by a rigorous security analysis in the random oracle model. We implemented our scheme, and the results demonstrate its practicality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain

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On the design of an AI-driven secure communication scheme for internet of medical things environment

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Digital Communications and Networks 2023年第5期9卷 1080-1089页

作者： Neha Garg Rajat Petwal Mohammad Wazid D.P.Singh Ashok Kumar Das Joel J.P.C.Rodrigues Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Center for Security Theory and Algorithmic ResearchInternational Institute of Information TechnologyHyderabad500032India College of Computer Science and Technology China University of Petroleum(East China)Qingdao266555China Instituto de Telecomunicaçoes 6201-001CovilhaPortugal

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the *** is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare *** other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare ***,this system is prone to securityand privacy-related attacks on healthcare ***,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is *** mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for *** discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to ***,we provide the security analysis of the proposed scheme to show its security against different possible ***,a comparative study of the proposed scheme with other similar schemes is *** results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality ***,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

关键词： Internet of Medical Things(IoMT) security Authentication and key agreement Artificial Intelligence(AI) Big data analytics

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Extending Scratch Framework to Improve the Analytical Skills of Undergraduate Students 24

Extending Scratch Framework to Improve the Analytical Skills...

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10th International Conference on computer technology Applications, ICCTA 2024

作者： Akbar, Ubaid Ul Akbar, Saeed Ali, Mumtaz Ullah, Rahmat Khan, Rizwan Lopes, Ivandro Ortet Department of Computer Science City University of Science and Information Technology Peshawar Pakistan School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Computer Science and Electronic Engineering University of Essex United Kingdom School of Cyber Science & Engineering Huazhong University of Science & Technology Wuhan China

ISBN: (纸本)9798400716386

Scratch is an innovative and the most popular block-based Visual Programming Language designed for beginners to learn programming effectively. However, it lacks the capacity to allow users to learn and solve larger real-world problems such as the Travelling Salesman Problem (TSP). Hence, there is a need for an accessible and effective tool to assist beginners and novice programmers in learning and tackling the TSP. This paper introduces ScratchTSP, an extension of the Scratch programming Framework, specifically designed for the TSP. It offers an opportunity for beginners and novice programmers to grasp the TSP and the various algorithms used to solve it. For evaluation, we conduct a survey among undergraduate students to assess the usability and usefulness of the proposed ScratchTSP extension. Survey findings indicate that ScratchTSP offers a user-friendly and effective tool for beginners or novice programmers, aiding them in learning the TSP while developing their critical thinking and problem-solving skills. © 2024 ACM.

关键词： Students

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Multi Label Classification Based on Ensemble Learning: Investigative Study 25

Multi Label Classification Based on Ensemble Learning: Inves...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Hadi, Wael Samara, Ghassan Al Daoud, Essam Owaida, Suhaila Abu Elrashidi, Ali Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan Zarqa University Faculty of Information Technolog Department of Computer Science Zarqa Jordan Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of It Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

In the big data technology nowadays, activities associated with Multi-Label Classification (MLC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing MLC algorithms suffer from low overall performance. This paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. These PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of MLC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. The results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique. © 2024 IEEE.

关键词： Contrastive Learning

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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

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Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Multi-modal feature fusion for 3D object detection in the production workshop

Multi-modal feature fusion for 3D object detection in the pr...

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作者： Hou, Rui Chen, Guangzhu Han, Yinhe Tang, Zaizuo Ru, Qingjun College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China

3D object detection technology is of great significance to realize intelligent perception and ensure the production safety of a workshop. Existing 3D object detection relies on large-scale, high-quality 3D annotation data and is unsuitable for actual workshop scenes’ perception. This paper proposes a multi-modal feature fusion 3D object detection method (MFF3D) for a production workshop. The design of MFF3D includes the following steps: (1) Improved YOLOv3 attains the 2D prior region of an object, and RGB-D saliency detection obtains the object image pixels in that region. (2) Depth image pixels corresponding to the object are projected to generate the object's frustum point cloud, and a multi-modal feature fusion strategy simplifies the object's frustum point cloud, so as to remove outlier points and reduce the number of point clouds. This can replace the 3D object reasoning process based on deep neural networks;(3) An axis-aligned bounding box algorithm is used to generate the object's 3D bounding box, and principal component analysis algorithm (PCA) is used to calculate the object's pose information. MFF3D is applied in the workshop, and experiments verify the feasibility and detection accuracy. We set up a production workshop object dataset (PWOD) for experimental evaluation. In the case of a small amount of 2D annotation data and no 3D annotation data, experimental results show that when the threshold value of intersection over union of 3D object (IoU3D) is 0.5, the mean average precision value of 3D object (mAP3D) reaches 60.31, and the detection speed reaches 3 FPS. MFF3D does not rely on 3D annotation data and can effectively detect objects of a production workshop. © 2021 Elsevier B.V.

关键词： Object detection

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A Deep Learning Assisted Crop Pest Classification Model by Using Enhanced Light Gradient Boost Machine (ELGBM) Logics

A Deep Learning Assisted Crop Pest Classification Model by U...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Venkata Siva Prasad, Ch. Malarvizhi, C. Porkodi, M. Vaikash, K.G. Sunheriya, N. Al-Mousa, Mohammad R. Jeppiaar Engineering College Department of Ece Chennai India Rajalakshmi Institute of Technology Department of Ece Chennai India Sona College of Technology Department of Eee Salem India School of Mechanical Engineering Vellore Institute of Technology Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Information Technology Zarqa University Department of Cyber Security zarqa Jordan Universiry of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Many different kinds of pests and insects impede the development of vital field crops including wheat, sugarcane, and rice, and they also diminish agricultural yields. Due to their similar appearance in the early stages of crop growth, accurately classifying and identifying all varieties of crop insects is a challenging task for farmers. Automatic feature extraction and handling of complicated high-level features in image classification applications are two requirements for a new model that can solve this problem with the help of deep learning aided architectures. This study investigates the use of an Enhanced Light Gradient Boost Machine (ELGBM), a deep learning algorithm, for the purpose of crop pest classification and pesticide recommendation. The suggested model's efficacy is assessed by cross-validation with a traditional learning technique known as Convolution Neural Network (CNN). Furthermore, the system incorporates a recommendation component that proposes the most efficient pesticide options for crop protection depending on the discovered pests. The suggested methodology outperforms more traditional approaches in terms of classification accuracy, which is around 98%. The outcomes proved that the suggested ELGBM model outperformed pre-trained models in labeling different kinds of field crop insects, suggesting its potential for use in agricultural crop protection systems. © 2024 IEEE.

关键词： Convolutional neural networks

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