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Enhancing Postal Convenience and Security IoT-Enabled Smart Postboxes and Delivery Lockers Using Cloud Computing 8

Enhancing Postal Convenience and Security IoT-Enabled Smart ...

8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Mohankumar, N. Rakesh, Nitin Nandhini, R. Priyadarshini, S. Brindha, G. Srinivasan, C. Maharashtra Pune India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of English Tamil Nadu Salem India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331505462

This paper presents an efficient approach to integrate Internet of Things (IoT) technology into traditional postboxes and delivery lockers to secure them. By equipping conventional postboxes with IoT capabilities, receivers are provided with real-time notifications upon the delivery of mail or deliveries. The belongings will remain safe as it requires secure access procedures fortified by the electronic locks and identification verification. Monitoring temperature ensures the safety of perishable commodities, while anti-tampering sensors strengthen the protection provided by the system. Integration with delivery schedules and routes simplifies operations while improving the efficiency of postal employees' commutes. Providing a channel for feedback from recipients helps increase engagement. The paper investigates the possible advantages, disadvantages, and effects of integrating IoT technology into postal infrastructure, aiming to reinvent how the postal ecosystem interacts with and provides services to its customers. © 2024 IEEE.

关键词： Cloud computing

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Enhancement of Security in IoT using Secure Hash Cryptography Algorithm with Double Encryption (SHCA-DE) 9

Enhancement of Security in IoT using Secure Hash Cryptograph...

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

作者： Uganya, G. Rajalakshmi, D. Vangapandu, Ramarao Dinesh, M. Giri, Jayant Odeh, Mahmoud Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vignan's Institute of Engineering for Women Department of Computer Science and Engineering Vishakhapatnam India Saveetha University Department of Vlsi Microelectronics Saveetha School of Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350377972

The 'Internet of things' is the developing technology to transfer the world into smart innovation. 'IoT' security is the most problematic issue in recent applications due to its properties including 'centralized architecture', 'limited capacity devices', and 'ambiguity'. So, blockchain with 'IoT' applications provides secure data communication compared to traditional 'IoT' applications. Blockchain is a recent growing technology that has features including 'decentralized structure', 'transparency', 'immutable records', 'increased capacity', and 'quick backtracks'. The 'cryptography hash algorithm' is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-DE) to protect the private key message. Then, this proposed algorithm has been analyzed with MD5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm. © 2024 IEEE.

关键词： Encryption algorithms

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Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

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IEEE Transactions on Multimedia 2025年 27卷 3637-3649页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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Underwater estimation of audio signal prediction using fruit fly algorithm and hybrid wavelet neural network

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Journal of Reliable Intelligent Environments 2022年第2期8卷 211-221页

作者： Sagayam, K. Martin Ghosh, Ahona Bhushan, Bharat Andrew, J. Cengiz, Korhan Elngar, Ahmed A. Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computational Science Brainware University Kolkata India School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electrical and Electronics Engineering Traka University Edirne Turkey Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

Underwater acoustic signal is one of its most vital features in oceanography for predicting the local variants, helps to process the signal, and also assists to discover non-stationary signals. wavelet neural network (WNN) architecture acquires a huge benefit from the artificial neural network (ANN) and time-frequency localizing features of wavelet analysis (WA). However, the system could train using the ANN model, it has tendency of falling into local minima and generating convergence. A wavelet-aided neural network is integrated with a meta-heuristic optimizer named fruit fly algorithm (FFA) that aids our proposed system in which reduces the computational complexity and improves performance measures. The main objective of the proposed work is of modelling an optimized wavelet neural network for efficient measurement of the mean absolute error and the root mean square error. The simulation of the sound signals has been performed with the use of optimized neural networks that has predicted the unknown signal. The proposed model is assessed by comparing the outcomes with the complements of it, i.e. the traditional neural networks. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Forecasting

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Comparative Analysis of SVM and CNN Classifiers for EEG Signal Classification in Response to Different Auditory Stimuli

Comparative Analysis of SVM and CNN Classifiers for EEG Sign...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Tibermacine, Ahmed Akrour, Djouher Khamar, Rofeida Tibermacine, Imad Eddine Rabehi, Abdelaziz University of Biskra Department of Computer Science Biskra Algeria Sapienza University of Rome Department of Computer Automation and Management Engineering Rome Italy Zian Achour University of Djelfa Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Djelfa17000 Algeria

ISBN: (纸本)9798331527396

This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants' favorite music, and Interstellar's main theme. By analyzing the unique EEG responses elicited by these stimuli, the study explores cognitive and emotional processing differences. Two prominent classifiers, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), were utilized. SVM, combined with Power Spectral Density (PSD) features, achieved the highest accuracy of 95% when distinguishing Quran recitation from rest. CNN, particularly with Kernel Density Estimation (KDE) features, exhibited strong performance in distinguishing between different auditory stimuli, with an average accuracy exceeding 90%. These results highlight the critical role of feature extraction techniques, such as PSD and KDE, in improving classification performance and deepening our understanding of EEG-based emotional and cognitive responses to auditory stimuli. © 2024 IEEE.

关键词： Convolutional neural networks

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Unrestricted molecular motions enable mild photothermy for recurrence-resistant FLASH antitumor radiotherapy

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Bioactive Materials 2024年第7期37卷 299-312页

作者： Hanchen Shen Hongbin Wang Jianlan Mo Jianyu Zhang Changhuo Xu Feiyi Sun Xinwen Ou Xinyan Zhu Lidong Du Huaqiang Ju Ruquan Ye Guangfu Shi Ryan TKKwok Jacky WYLam Jianwei Sun Tianfu Zhang Shipeng Ning Ben Zhong Tang Affiliated Cancer Hospital&Institute of Guangzhou Medical University School of Biomedical EngineeringGuangzhou Medical UniversityGuangzhouChina Department of Chemistry Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and ReconstructionDivision of Life Scienceand State Key Laboratory of Molecular NeuroscienceThe Hong Kong University of Science and TechnologyKowloonHong KongChina The Second Ward of Breast Surgery Cancer Hospital Affiliated to Harbin Medical UniversityHeilongjiang150081China Department of Anesthesiology Maternal and Child Health Hospital of Guangxi Zhuang Autonomous RegionNanningChina MOE Frontiers Science Center for Precision Oncology Faculty of Health SciencesUniversity of MacaoMacaoChina Department of Chemistry State Key Laboratory of Marine PollutionCity University of Hong KongHong KongChina Department of Breast Surgery The Second Affiliated Hospital of Guangxi Medical UniversityNanningChina School of Science and Engineering Shenzhen Institute of Aggregate Science and TechnologyThe Chinese University of Hong KongShenzhen(CUHK-Shenzhen)Guangdong518172China

Ultrahigh dose-rate(FLASH)radiotherapy is an emerging technology with excellent therapeutic effects and low biological ***,tumor recurrence largely impede the effectiveness of FLASH *** tumor recurrence is crucial for practical FLASH ***,we prepared an agarose-based thermosensitive hydrogel containing a mild photothermal agent(TPE-BBT)and a glutaminase inhibitor(CB-839).Within nanoparticles,TPE-BBT exhibits aggregation-induced emission peaked at 900 nm,while the unrestricted molecular motions endow TPE-BBT with a mild photothermy generation *** balanced photothermal effect and photoluminescence are ideal for *** 660-nm laser irradiation,the temperature-rising effect softens and hydrolyzes the hydrogel to release TPE-BBT and CB-839 into the tumor site for concurrent mild photothermal therapy and chemotherapy,jointly inhibiting homologous recombination repair of *** enhanced FLASH radiotherapy efficiently kills the tumor tissue without recurrence and obvious systematic *** work deciphers the unrestricted molecular motions in bright organic fluorophores as a source of photothermy,and provides novel recurrence-resistant radiotherapy without adverse side effects.

关键词： FLASH radiotherapy Cancer recurrence Mild photothermal therapy Aggregation-induced emission Molecular motion

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Dynamic Exit Selection and Counting Area Adjustment for Efficient Crowd Evacuation in Asymmetrical Layouts 25

Dynamic Exit Selection and Counting Area Adjustment for Effi...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Alidmat, Omar Khair Alla Alazaidah, Raed Arabiat, Mohammad Khafajeh, Hayel Aljaidi, Mohammad Yusof, Umi Kalsom Elrashidi, Ali Zarqa University Computer Science Department Zarqa Jordan Zarqa University Data Science and Artifical Inteligence Department Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Universiti Sains Malaysia School of Computer Sciences Pulau Pinang Malaysia University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

This research introduces an innovative methodology to address the significant challenge of evacuating large crowds from buildings with multiple exits, particularly in scenarios with asymmetrical exit configurations and high population densities. The proposed model incorporates dynamic counting areas that adjust according to evacuee density, alongside a probabilistic exit selection mechanism influenced by enhanced dynamic parameters - congestion, distance, and empty. It also accounts for heterogeneous agent behaviors to more accurately represent evacuee dynamics in complex, asymmetrical environments with dense crowds. Key findings indicate that the model effectively alleviates bottlenecks at congested exits, optimizes exit usage, and enhances overall evacuation safety. Comparative analyses demonstrate that the proposed model significantly improves evacuation efficiency and reduces travel distances compared to previous models. Simulation results indicate that agent density directly affects evacuation time, with lower congestion levels facilitating faster egress. These advancements lead to more precise evacuation time predictions and crowd flow assessments, offering valuable insights for refining emergency egress strategies in complex settings. Future research will focus on refining the model with real-world data to expand its applicability in diverse settings, such as stadiums and public transportation hubs, thereby enhancing emergency preparedness and response. © 2024 IEEE.

关键词： Lead refining

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Detecting Hydrocephalus Using CT Scans and Machine Leaning Techniques 2

Detecting Hydrocephalus Using CT Scans and Machine Leaning T...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Pourhossain Latifi, Muhammad Sadegh Gourabi, Ahmad Alqammaz, Abdullah Moradi, Ehsan Hosseini, Rahil Boostani, Reza Sabeti, Maliheh Islamic Azad University North Tehran Branch Department of Computer Engineering Tehran Iran Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Shahid Beheshti University of Medical Sciences Department of Neurosurgery Tehran Iran Islamic Azad University Department of Computer Engineering Shahr-e-Qods Branch Tehran Iran Shiraz University Faculty of Electrical and Computer Engineering Cse and It Department Shiraz Iran

ISBN: (纸本)9798350373363

Every year, hydrocephalus affects many people, whether they are infants or adults with higher incidence in pediatric group. This means that a large number of families affected by this disease, and automatic determination of hydrocephalus in medical images could help people who are affected with this disease and their condition is at a dangerous stage. In this research, we try to implement an algorithm that could detect hydrocephalus from CT images. Our work is divided into two stages of implementing the proposed algorithm and comparing it with the results of similar researches. We try to design and implement all steps from scratch. The proposed model reaches the result with appropriate accuracy but slower running time. Our Method is able to detect hydrocephaly from 40 patients CT Images and calculate ventricular indices with accuracy of 64% to 93% in comparison with manually extracted ventricular indices by a neurosurgeon. In addition, we also checked the K-nearest neighbor (KNN) and Watershed methods and concluded that Region Growing is the best among this category. © 2023 IEEE.

关键词： computerized tomography

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Development of Granular Fuzzy Relation Equations Based on a Subset of Data

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1416-1427页

作者： Dan Wang Xiubin Zhu Witold Pedycz Zhenhua Yu Zhiwu Li School of Computer Science and Technology Xi’an University of Science and TechnologyXi’an 710054China School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of AlbertaEdmonton T6R 2V4 ABCanada Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Institute of Systems Engineering Macao University of Science and TechnologyMacaoChina

Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy *** each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant *** this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy *** subset of rules is selected by maximizing their performance of the obtained *** originality of this study is conducted in the following *** with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and ***,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm.

关键词： A subset of data granular fuzzy relation equations interval-valued fuzzy relation particle swarm optimization(PSO)

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Banana Leaf Disease Detection using Advanced Convolutional Neural Network

Banana Leaf Disease Detection using Advanced Convolutional N...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： E Mohanraj R Hariharan S Keerthana K B Cavin Department of Computer Science and Engineering Faculty of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Student of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India

Any plant’s ability to grow disease-free is crucial for both the environment and human existence. Nevertheless, various diseases, viruses, and fungi affect the plant and highly influence the yield quality and production quantity. If the disease and its origin are not appropriately diagnosed, illness control procedures might be a waste of effort and resources, causing additional plant losses. Plant disease tracking by manual process is quite challenging. It requires a considerable amount of work, expertise in plant diseases, and protracted processing durations. Traditional neural networks require a large number of parameters to train effectively, which can make them slow and computationally expensive. The previous study includes the leaf disease detection performed by CNN, R-CNN, and SVM models. Using the Faster R-CNN, which outperforms other models in terms of computation time and accuracy, this study intends to develop a banana leaf disease detection system.

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