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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sharma, Smita Sharma, Tarunika Praveena, K. Kumar, Manish Syambabu, V. Hameed, Ali Abduhussien Amity University Department Of Electronics & Communication Engineering Greater Noida India New Horizon College Of Engineering Department Of Applied Sciences Bangalore India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Mlr Institute Of Technology Department Of Electronics And Communication Engineering Telangana Hyderabad India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

This piece talks about research that has been done on sending signals over sensor radio networks and making sure that links are safe. Connections and sharing info must be effective in a world that is always changing. To make this happen, we show a full plan that speeds up the network by using three important methods, each backed by a statistical model. Dynamic Propagation Modeling (DPM) guesses how signals will behave as they happen. Network managers can make better decisions when they use dynamic mathematical models that consider changes in the environment and the movement of nodes. The second method, called the Disturbance Mitigation technique (IMA), changes the broadcast power and channel selection on the fly to stop close devices from interfering. In busy WiFi networks, this makes sure that joining is effective. Algorithm 3: ARPC changes route paths based on how the network is working right now. It consistently links, even when the network changes. Using all three of these methods together helps us consider differences in propagation, get the most out of transmission power, and choose communication lines on the fly. This makes the network work better and be more reliable. Mathematical models help us figure out things like throughput, packet loss rate, end-to-end latency, network life, energy saves, and the dependability of connections. © 2024 IEEE.

关键词： Wireless sensor networks

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Privacy-Preserved Incentive Mechanism for Split Learning in Edge Computing System

Privacy-Preserved Incentive Mechanism for Split Learning in ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Lu Chang, Zheng Hämäläinen, Timo Min, Geyong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Jyväskylä Faculty of Information Technology P.O. Box 35 JyväskyläFIN-40014 Finland University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

ISBN: (纸本)9798350351255

In the edge computing system, split learning (SL) is an emerging distributed learning approach that allows mobile users (MUs) and edge nodes (ENs) to train the model together without sharing the raw data of the MU. Although MU can preserve its privacy in SL, attacks on the intermediate data at the cut layer for model training can still lead to privacy leakage, which prevents privacy-sensitive MUs from participating in training. Therefore, it is important to implement an effective incentive mechanism to motivate MUs to join SL while preserving their privacy. In this work, from the perspective of maximizing the utility of edge service provider (ESP) while considering the privacy-sensitivity of different MUs, the incentive problem of ESP and MUs is transformed into the utility optimization problem, and an incentive mechanism based on the differential privacy (DP) and contract theory is established to model the interactions between the MUs and EN. The obtained convex optimization problem is obtained through the mathematical derivations, and the optimal contract is presented by solving this problem. The simulation results demonstrate the effectiveness of our proposed privacy preserved incentive mechanism. © 2024 IEEE.

关键词： Differential privacy

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Hybrid CNN Classifier for Accurate Detection of Oral Cysts in Panoramic Dental Radiograph 9

Hybrid CNN Classifier for Accurate Detection of Oral Cysts i...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sivagami, S. Senthilvadivu, S. Geetha, P. Yukthavarshika, J. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

In dentistry, machine learning approaches play a significant role in the early detection and diagnosis of oral cysts. When planning accurate medical treatment, oral and maxillofacial surgeons can benefit from preoperative detection of these cysts. Surgical planning is directly affected by classifying oral cysts as dentigerous, radicular, ameloblastoma, odontogenic carcinoma, and odontogenic keratocyte. In dentistry, the outcomes of differential diagnosis based solely on imaging are unacceptable. This research proposes a three-step framework for autonomous cyst classification, segmentation, and detection based on a Convolutional Neural Network (CNN). To begin, DENT-CNN, a modified CNN architecture is proposed to classify the oral cyst as normal or abnormal. Second, UNet is a CNN variant used to segment the categorized cyst regions on panoramic dental images. Third, the Gray Level Co-occurrence Matrix (GLCM) is combined with CNN to identify segmented cysts as benign or malignant. The proposed work is extensively evaluated using 1500 real-time dental panoramic radiographic images of the oral cyst collected from a dental hospital. To alleviate overfitting, the data augmentation approach is used. The performance of the proposed work is analyzed in terms of a cyst classification rate of 96.8 %, a Dice Score of 96.3 % for cyst segmentation, and an accuracy for the Identification phase of 96.5%. © 2024 IEEE.

关键词： Dentistry

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Intelligent road recognition system for an autonomous vehicle 20

Intelligent road recognition system for an autonomous vehicl...

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20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Dlugosz, Marek Brodzicki, Michal Skruch, Pawel Szelest, Marcin Cieslar, Dariusz Agh University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering al. A. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

The article described the car road detection system. This type of system, also known as green carpet or free space system, is part of an advanced driver assistance system (ADAS). The proposed system works by using the image of the camera located at the front of the car. The road detection system uses various operations on the image in appropriately selected color spaces. To improve the detection and reliability of the developed system, shadow areas are removed from the input images. The operation of the road detection system was practically tested using an experimental electric vehicle. © 2022 IEEE.

关键词： Autonomous vehicles

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DTLM-DBP:Deep Transfer Learning Models for DNA Binding Proteins Identification

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computers, Materials & Continua 2021年第9期68卷 3563-3576页

作者： Sara Saber Uswah Khairuddin Rubiyah Yusof Ahmed Madani Department of Computer Engineering Faculty of EngineeringArab Academy of Science and TechnologyEgypt Centre for Artificial Intelligence&Robotics Malaysia-Japan International Institute of TechnologyUniversiti Teknologi Malaysia

The identification of DNA binding proteins(DNABPs)is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g.,in the study of the biological,biophysical,and biochemical effects of antibiotics,drugs,and steroids on *** paper presents an efficient approach for DNABPs identification based on deep transfer learning,named“DTLM-DBP.”Two transfer learning methods are used in the identification *** first is based on the pre-trained deep learning model as a feature’s extractor and *** different pre-trained Convolutional Neural Networks(CNN),AlexNet 8 and VGG 16,are tested and *** second method uses the deep learning model as a feature’s extractor only and two different classifiers for the identification *** classifiers,Support Vector Machine(SVM)and Random Forest(RF),are tested and *** proposed approach is tested using different DNA proteins *** performance of the identification process is evaluated in terms of identification accuracy,sensitivity,specificity and MCC,with four available DNA proteins datasets:PDB1075,PDB186,PDNA-543,and *** results show that the RF classifier,with VGG-Net pre-trained deep transfer learning features,gives the highest ***-DBP was compared with other published methods and it provides a considerable improvement in the performance of DNABPs identification.

关键词： DNABPs deep transfer learning AlexNet 8 VGG 16 SVM RF

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JOINT-SEMANTICS MULTI-SIMILARITY HASHING FOR CROSS-MODAL RETRIEVAL 49

JOINT-SEMANTICS MULTI-SIMILARITY HASHING FOR CROSS-MODAL RET...

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2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024

作者： Wang, Weigang Guo, Zhongwen Yang, Chao Wang, Jinxin Jiang, Sining Zhang, Tianao Faculty of Information Science and Engineering Ocean Nuiversity of China Qingdao China School of Computer Science University of Technology Sydney Sydney Australia Qingdao High-Tech Zone Investment and Development Group Co. LTD Qingdao China

ISBN: (纸本)9798350344851

Recently, cross-modal hashing has attracted much attention in large-scale image retrieval scenarios. However, most existing methods ignore the potential higher-order relationships and label semantic information between heterogeneous modality data. Besides, the imbalanced training samples could bias the learning process in most classes and affect the retrieval performance. To solve the above problems, we proposed a Joint-semantics Multi-Similarity Hashing method for cross-modal retrieval (JMSH). We first construct a joint semantic similarity matrix, which supervises hash learning by integrating multi-modal features and semantic labels. This method generates higher-order semantic features that maintain semantic correlation effectively. Then, we propose a multi-similarity loss based on adaptive margin, which can collect and weight informative pairs efficiently and accurately, thus producing more discriminative hashing code and improving retrieval performance. Extensive experiments on two benchmark datasets show the superiority of JMSH in cross-modal retrieval tasks. © 2024 IEEE.

关键词： cross-modal hashing cross-modal retrieval multi-similarity loss supervised learning

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Detection of Skin Cancer Through Dermoscopy Images Using Hybrid Deep Feature Extraction

Detection of Skin Cancer Through Dermoscopy Images Using Hyb...

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Computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Aniqa Mujahid Shoaib Hassan Muhammad Hassan Muhammad Umair Muhammad Zubair Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan School of Computer Science & Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

A common and dangerous type of cancer that affects millions of people each year is known as Skin Cancer. Skin cancer, like melanoma, is difficult to identify because of its resemblance with benign moles. Melanoma early detection is very necessary as it increases the chances of survival. With technological advancement, AI has emerged as a powerful technique in the medical field. Machine- deep learning and neural networks have shown promising results in automating skin cancer detection using dermoscopy images. This paper proposes an efficient skin cancer detection technique through dermoscopy images using Hybrid Deep Feature Extraction. A publicly available dataset is used in this paper. Data preprocessing technique: Morphological closing and gamma correction are applied to it. Then, feature extraction with handcraft methods HOG, LBP, and automated methods DenseNet201 and ResNet50 will be performed on the preprocessed dataset. All the extracted features from these algorithms are concatenated into a single array. Different feature extraction combinations are tested with SVM, Random Forest, Artificial Neural Network, XGBoost, and LightGBM classifiers. After classification, the results are measured using the performance evaluation metrics: accuracy, precision, recall, and F1 score. All models give efficient results, but the classifier ANN with the feature extraction combination HOG and DenseNet201 are giving the best result with the highest accuracy, recall, precision, and F1-score as 0.988, 0.988, 0.988, and 0.988, respectively.

关键词： Support vector machines Accuracy Biological system modeling Melanoma Feature extraction Skin Artificial intelligence Sustainable development Random forests Residual neural networks

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A Review on the Role of Blockchain technology for Decentralized Identity Management: A Future Without Passwords

A Review on the Role of Blockchain Technology for Decentrali...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Rohini Raut Palash Gourshettiwar Gayatri Thakre Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Advanced identity mechanisms are essential for secure client-identifiable proof in the unambiguously computerized world. Because the board frameworks rely on aggregated databases, fraud, data breaches, and unauthorized access are significant security risks. By providing a decentralized, immutable structure, blockchain technology advances security and protection. Through Self-Sovereign Identity, blockchain gives people authority over their own data. While also improving confirmation and check mechanisms. The potential of blockchain-based Identity, this article examines the board frameworks, focusing on five essential components: authentication, privacy, trust, integrity, and simplicity. It examines how interoperability, flexibility, and collaboration with outdated frameworks prevent decentralized IDMS being more widely used. The study also covers customer satisfaction, legal concerns, and potential future developments, such as Web 3.0, security enhancements, and integration with decentralized money. By addressing these problems and looking into new possibilities, blockchain-based IDMS may be able to reform advanced identity across the entire board.

关键词： Semantic Web Sentiment analysis Reviews Finance Authentication Blockchains Security Protection Interoperability Business

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Computational Models to Prove Security Protocols Against Adversary 4th

Computational Models to Prove Security Protocols Against Adv...

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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Albermany, Salah Abdulhadi Razzaq, Iman Saleem Alkhafaji, Mohammed Ayad Fatan, Muna Hakim Zearah, Sajad Ali Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq College of Engineering Technology National University of Science and Technology Dhi-Qar Iraq Scientific Research Center Al-Ayen University Dhi-Qar Iraq

ISBN: (纸本)9789819937158

Telecommunication security is a broad and important field at present. Various models of security have been developed that aim to provide secure communication between the parties. Security sought by the protocol can be demonstrated through appropriate safety models that consider the parties that communicate with each other through the protocol, mutual trust, communication environment, and other aspects. This paper aims to discuss security models and to provide a comprehensive overview in this direction as it is considered an active field. This paper discusses the computational security models that demonstrate the security of protocols and measures their analysis and breakdown by the attacker. Provide a summary of the advantages and disadvantages of each model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Network security

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A Smart Robotic Design to Identify and Diffuse Bombs in Defence Environments Using Sensors Assisted Internet of Things (IoT) technology

A Smart Robotic Design to Identify and Diffuse Bombs in Defe...

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

作者： Vijayakumari, P. Mary, S. Jeya Deepa, N. Swarna, M. Narkhede, Alok Al-Safarini, Maram Y. Sairam Engineering College Dept of Ece Chennai India Einstein College of Engineering Dept of Ece Tirunelveli India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India Dr. M.G.R. Educational and Research Institute University Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Numerous news media outlets cover incidents in which trained personnel or combatants are injured or perish while defusing bombs. The military robot that conducts intelligence operations. While it has been beneficial to the Army, firefighters, and police officers in critical situations, many of the current systems are inefficient due to their lack of accuracy and technology. Our objective is to develop the most efficient robot possible in order to achieve accuracy and technology. Robotic limbs equipped with metal detectors are being installed to provide manual assistance. The wireless explosive disposal device will enhance our nation's defense against terrorists, suicide bombers, and other similar activities. Experts are required to operate the explosives detectors and disposal system. However, this method of analysis necessitates additional time and poses a risk to the lives of experts. A novel robot, the IoT Defense Robotic Model (IoTDRM), is developed in this paper with the assistance of Internet of Things (IoT) technology. The proposed model is cross-validated against the conventional logic of the Smart Bomb Diffusing Robot (SBDR) to assess its performance. The signals are transmitted from the sensor end to the IoT controller in this robot, and they are retransmitted to the server end via a wireless system. The user controls the automaton manually using a personal computer or mobile phone. When the sensor detects a metal, a buzzer alarm will sound. The metal is examined using a wireless camera to determine whether it is a weapon. Since the identified metal is a bomb, the user instructs the robot to detonate it via the computer. © 2024 IEEE.

关键词： Bombs (ordnance)

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