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Exploring outliers in global economic dataset having the impact of COVID-19 pandemic

International Journal of Business Intelligence and Data Mining 2023年第3期22卷 287-309页

作者： Desarkar, Anindita Das, Ajanta Chaudhuri, Chitrita Computer Science and Engineering Jadavpur University Kolkata700032 India Amity Institute of Information Technology Amity University Kolkata India

Outlier is a value that lies outside most of the other values in a dataset. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusion detection systems. Similarly, in economic domain, it can be applied to analyse many unexpected events to harvest new knowledge like sudden crash of stock market, mismatch between country’s per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest. These situations can arise due to several reasons, out of which the present COVID-19 pandemic is a leading one. This motivates the present researchers to identify a few such vulnerable areas in the economic sphere and ferret out the most affected countries for each of them. Two well-known machine-learning techniques DBSCAN and Z-score are utilised to get these insights, which can serve as a guideline towards improving the overall scenario subsequently. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： COVID-19

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic Butterfly Optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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Efficient Resource Allocation in Vehicular Networks Using Embedding-Enhanced MAPPO 10

Efficient Resource Allocation in Vehicular Networks Using Em...

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10th IEEE Smart World Congress, SWC 2024

作者： Qiu, Wen Shao, Xun Zhao, Wei Masui, Hiroshi Kitami Institute of Technology Information and Communication Engineering Hokkaido Japan Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Toyohashi Japan Anhui University of Technology School of Computer Science and Technology Anhui China Kitami Institute of Technology Information Processing Center Hokkaido Japan

ISBN: (纸本)9798331520861

With the swift advancement of automotive technology, the need for efficient allocation of resources in V2V (vehicle-to-vehicle) communications has grown significantly. However, the dynamic nature of channels in vehicular networks introduces considerable uncertainty in resource allocation. This research presents a novel approach to addressing these challenges by utilizing multi-agent deep reinforcement learning (MADRL). By leveraging shared resource blocks (RBs) for V2V and vehicle-to-infrastructure (V2I) communications, our approach minimizes interference with V2I links and ensures optimal V2V performance. Unlike traditional methods that select actions sequentially, our approach treats every V2V link as an agent and simultaneously determines all agents' actions to improve coordination and efficiency. To accelerate the training of the multi-agent critic network, we integrate an embedding module to simplify and compress the network input dimensions. Through the use of our embedding multi-agent proximal policy optimization (E-MAPPO) algorithm, the agents collaboratively optimize their transmission strategies to efficiently share spectrum with V2I links. Simulation results show that our method greatly minimizes interference with V2I links while meeting the stringent latency and reliability requirements of V 2 V communications, surpassing baseline approaches. © 2024 IEEE.

关键词： Deep reinforcement learning

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Deep-EERA:DRL-Based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks

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Tsinghua science and technology 2025年第1期30卷 418-432页

作者： Shabeer Ahmad Jinling Zhang Ali Nauman Adil Khan Khizar Abbas Babar Hayat School of Electronic Engineering Beijing University of Posts and TelecommunicationBeijing 1000876China Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38541Republic of Korea School of Information Engineering Xi’an Eurasia UniversityXi’an 710065China Department of Computer Science Hanyang UniversitySeoul 04763Republic of Korea

The rise of innovative applications,like online gaming,smart healthcare,and Internet of Things(IoT)services,has increased demand for high data rates and seamless connectivity,posing challenges for Beyond 5G(B5G)*** is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas,ensuring higher data rates and uninterrupted connectivity while minimizing *** Aerial Vehicles(UAVs)as Aerial Base Stations(ABSs)offer a promising and cost-effective solution to boost network capacity,especially during emergencies and high-data-rate ***,integrating UAVs into the B5G networks presents new challenges,including resource scarcity,energy efficiency,resource allocation,optimal power transmission control,and maximizing overall *** paper presents a UAV-assisted B5G communication system where UAVs act as ABSs,and introduces the Deep Reinforcement Learning(DRL)based Energy Efficient Resource Allocation(Deep-EERA)*** efficient DRL-based Deep Deterministic Policy Gradient(DDPG)mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput *** proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G *** extensive simulations,we validate the performance of the proposed approach,demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.

关键词： Deep Reinforcement Learning(DRL) Unmanned Aerial Vehicles(UAVs) resource allocation energy efficiency 5G and beyond network

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Securing Mobile Cloud Data Offloading in Blockchain Using Deep Deterministic Policy Gradient with Game Theory 4

Securing Mobile Cloud Data Offloading in Blockchain Using De...

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4th International Conference on Multimedia Processing, Communication and information technology, MPCIT 2024

作者： Matheen Fathima, G. Shakkeera, L. Sharmasth Vali, Y. School of Computer Science Engineering & Information Science Presidency University Itgalpura Rajanukunte Yelahanka Bengaluru560064 India

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375466

In the era of ubiquitous connectivity and the proliferation of mobile devices, the demand for efficient and secure data offloading to the cloud has become paramount. This paper presents a novel framework that supports deep reinforcement learning and blockchain technology to enable secure and efficient mobile cloud data offloading. Addressing the challenge of optimizing data offloading decisions in mobile cloud environments by implementing a Deep Deterministic Policy Gradient algorithm (DDPG) to determine the optimal data offloading strategy. To incentivize user participation, introducing a game-theoretic reward mechanism that considers user contributions based on the offloading decisions made by the DDPG agent. This ensures a fair and efficient allocation of resources while maximizing overall system utility. Furthermore, integrating blockchain technology to ensure the security and integrity of the offloaded data's. By recording transactions on an immutable and transparent ledger, to provide a robust mechanism for data provenance, access control, and tamper-proof storage. The experimental results demonstrate the efficacy of the proposed framework in determining optimal strategies for data offloading decisions, enhancing user participation, and ensuring data security in mobile cloud environments. The evaluation of the system and the security analysis revealed advancements in the resource-efficient access control architecture, which attained reduced network latency while also delivering robust security and data privacy compared to existing data sharing models. © 2024 IEEE.

关键词： Cloud security

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Predicting and Navigating Environmental Transformations using Machine Learning 2

Predicting and Navigating Environmental Transformations usin...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Lakshin Archana, A. Nath, Keshab Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam686635 India

In today's world, as ecological challenges continue to rise, the importance of environmental monitoring and conservation cannot be overstated. These efforts play a vital role in countering the adverse impacts of human activities on our planet. The development of predictive models that can accurately predict the results of different conservation measures is essential for the success of these conservation efforts. This paper's objective is to create a Machine Learning (ML) model that predicts how environmental changes can unfold. This prediction helps in proactive monitoring and conservation planning, considering factors like deforestation, air pollution, sea level rise, temperature shifts, and forest preservation. The model relies on machine learning techniques, including AdaBoost, ETS, Prophet, SARIMA, LightGBM, and TL-BLSTM models, to analyze data, uncover trends, and inform decision-making. By offering a thorough assessment of the effects of conservation approaches, this model aids in shaping well-informed decisions regarding the distribution of resources. As a result, this process strengthens initiatives designed to protect biodiversity and the Earth's natural resources. Predicting changes in environmental variables is the main goal of this research, which will improve effective environmental management. In order to draw conclusions that are meaningful, this study compares several models for each of the four crucial factors. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sea level

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Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

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computers, Materials & Continua 2022年第6期71卷 5581-5601页

作者： Durr-e-Nayab Ali Mustafa Qamar Rehan Ullah Khan Waleed Albattah Khalil Khan Shabana Habib Muhammad Islam Department of Computer Systems Engineering University of Engineering and TechnologyPeshawarPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology and Computer Science Pak-Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripurPakistan Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesAl-QassimSaudi Arabia

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related *** surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this *** research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd *** theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the *** Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in *** crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major *** have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority *** algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

关键词： computer vision COVID sparse crowd crowd analysis flow analysis sparse crowd management tawaaf video analysis video processing

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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An Adaptive Frequency Hopping Scheme for Cognitive Radio Networks using Reinforcement Learning 3

An Adaptive Frequency Hopping Scheme for Cognitive Radio Net...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Meena, Y.R. Acharjya, Kalyan Upadhyay, Ramakant JAIN School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350354171

Adaptive Frequency Hopping (AFH) is an essential generation for Cognitive Radio Networks (CRNs). It no longer bests improves spectral efficiency and quality of service of CRNs but also prevents interference amongst customers. However, traditional AFH schemes need help with essential problems. First, they require a large amount of previous expertise approximately the surroundings. 2d, they remain highly static and inflexible, situation to adjustments in the external environment. This paper presents an Adaptive AFH scheme using Reinforcement to gain knowledge of (RL) for CRNs. First off, an environment-conscious AFH approach based on the user-described frequency occupancy possibility is proposed. The method is composed of a probabilistic hopping sequence generator and an intra-frequency hopping detector. Secondly, an adaptive AFH scheme with temporal-spatial getting to know is proposed, which incorporates an actor-critic-based RL controller and recreation-theoretic modeled surroundings. The RL controller generates a dynamic hopping sequence with low hopping frequency collisions at the same time as the sport-theoretic surroundings fashion the temporal-spatial modifications of the wireless medium. Sooner or later, the proposed scheme is proven in a simulated CRN environment. © 2024 IEEE.

关键词： Frequency hopping

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