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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Govindan, Niranjana Baskaran, Bharathi Azizan, Azizul Yakub, Fitri Dhamanti, Inge Dhaya, C. SRMIST Faculty of Engineering & Technology Department of Computing Technologies Kattankulathur Chennai India Faculty of Intelligence Kuala Lumpur Malaysia Malaysia-Japan International Institute of Technology UTM Electronic System Engineering Department Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia Adhiparasakthi Engineering College Department of Computer Science and Technology Melmaruvathur India

ISBN: (纸本)9798350340204

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model. © 2024 IEEE.

关键词： Adversarial machine learning

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A Thermal-Aware Scheduling Algorithm for Reducing Thermal Risks in DAG-Based Applications in Cyber-Physical systems 2nd

A Thermal-Aware Scheduling Algorithm for Reducing Thermal R...

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Ali, Irfan Shehzad, Muhammad Naeem Bashir, Qaisar Elahi, Haroon Awais, Muhammad Naeem Geman, Oana Liu, Pin Department of Computer System Engineering Institute of Business Administration Sukkar Sukkur65200 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan Intel Corporation AustinTX78746 United States Department of Computing Science Umeå University Umeå9036 Sweden Department of Computers Electronics and Automation Stefan Cel Mare University of Suceava Suceava720229 Romania School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9789819902712

Directed Acyclic Graph (DAG)-based scheduling applications are critical to resource allocation in the Cloud, Edge, and Fog layers of cyber-physical systems (CPS). However, thermal anomalies in DVFS-enabled homogeneous multiprocessor systems (HMSS) may be exploited by malicious applications posing risks to the availability of the underlying CPS. This can negatively affect the trustworthiness of CPS. This paper proposes an algorithm to address the thermal risks in DVFS-enabled HMSS for periodic DAG-based applications. It also improves the current list scheduling-based Depth-First and Breadth-First techniques without violating the timing constraints of the system. We test the algorithm using standard benchmarks and synthetic applications in a simulation setup. The results show a reduction in the temperature peaks by up to 30%, average temperature by up to 22%, temperature variations up to 3 times, and temperature spatial gradients by up to 4 times as compared to the conventional Depth-First Scheduling algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical system

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Personalized Collaborative Search Engine for Decision Making

Personalized Collaborative Search Engine for Decision Making

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Mohd. Murshleen Prabhu A Garima Jain Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science & Business System Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Web search engines are used for retrieving information but still there exists ambiguity in their retrieval process. It is so because traditional search engines laid emphasis on matching of keywords rather than maintaining semantic relations among terms lack of ability to produce precise results, presence of ambiguous keywords and verifying needs of users are few hindrances that are acting as scenario for web search engines. The paper focuses on improving search quality of users by collecting user experiences through creating hierarchical user profile it leads to collaborative search engine that helps in reaching at particular decision then the collected user's data are uploaded to recommendation server which are than displayed a search engines like Google/Yahoo.

关键词： Semantics Decision making Collaboration Search engines Servers Web search Engines

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Ev-IDID: Enhancing Solutions to Interactive Dynamic Influence Diagrams through Evolutionary Algorithms 21

Ev-IDID: Enhancing Solutions to Interactive Dynamic Influenc...

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21st International Conference on Autonomous Agents and Multiagent systems, AAMAS 2022

作者： Ma, Biyang Pan, Yinghui Zeng, Yifeng Ming, Zhong School of Computer Science and Engineering Minnan Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781713854333

Interactive dynamic influence diagrams (I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most of the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Decision making

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Security Issues and Workable Solutions in the Context of Sensor Cloud

Security Issues and Workable Solutions in the Context of Sen...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Sanjeev Kumar Mandal Manali Gupta Braiehdra Sharma Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science & Business System Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Notable tech companies such as Facebook, Microsoft, Apple, Google, and a number of game companies have launched bold plans to bring the metaverse to life. It is inevitable that in the years to come, virtual worlds will change significantly as a result of the metaverse ecosystem's ongoing enhancement and the rapid advancement of new technology. Nonetheless, a great deal of work still has to be done before the metaverse can be incorporated into our everyday lives and physical reality. This paper explores the metaverse's rising importance in the area of medicine and looks at its technological roadmap. We see a metaverse having the potential to drastically change intelligent health care facilities in the future, ushering in a new age of creative healthcare solutions.

关键词： Cloud computing Metaverse Social networking (online) Computational modeling Medical services Companies Games Predictive models Security Context modeling

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Interdependency patterns of potential seaport risk factors in relation to supply chain disruption in Indonesia

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Journal of Shipping and Trade 2023年第1期8卷 6页

作者： Do. Bagus, Muhammad Reza Hanaoka, Shinya Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152-8550 Japan Department of Information System College of Informatics and Computer Management Tidore Mandiri (STMIK Tidore Mandiri) Tidore Indonesia

As an integral part of the global supply chain network, Indonesian supply chain entities should understand conditional seaport risk factors that could lead to seaport threats that affect supply chain continuity. This study aims to provide a procedure for evaluating the interdependencies, implications, and correlations among various seaport risk factors for supply chain threats, specifically by investigating current practices in the developed economic region of Indonesian seaport operations. The study uses a rough set method to solve feature selection problems and multivariate analysis of variance to assess the correlation between dependent and independent variables. We find 39 conditional seaport risk factors that are potentially influenced by about 21 dependent factors related to seaport-fulcrum supply chain entities. Furthermore, threats from the planning process, infrastructure, seaport service process, distribution process, financial costs of nuclear enterprises and security existed and affluent highest potential risk in Indonesia. © 2023, The Author(s).

关键词： Association rule mining Genetic algorithm Interdependency risk analysis Rule rough set theory Seaport risk factors

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Internet of Things for Agriculture Industry: A Bibliometric Analysis in the Last Five Years 11

Internet of Things for Agriculture Industry: A Bibliometric ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Puspitasari, Chasandra Hamim, Miftahul Adi, Puput Dani Prasetyo Arifuddin, Rahman Widartha, Vandha Pradwiyasma School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Business School Bina Nusantara University Entrepreneurship Business Creation Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Pukyong National University Department of Information System Busan Korea Republic of

ISBN: (纸本)9798350389654

With the arrival of new technologies, the farm business is always changing. It is one of the most important in the world. The Internet of Things (loT) is one of the most exciting new technologies of the past few years. It could completely change the way we farm. We look at the literature about loT in agriculture over the last five years in this study. According to our findings, the use of loT in agriculture has been steadily increasing over the previous five years. This research provides a bibliometric evaluation of academic papers covering the years 2018-2022, with an emphasis on the Internet of Things (loT) in the agricultural sector. The analysis is performed using data obtained from the Scopus database. 120 documents were chosen and evaluated to acquire insights on the present state of research in this topic. Co-citation analysis identified wearable technology, farmers, loT devices, food, and wireless sensor networks (WSNs) as prominent research subjects in the agricultural industry's use of the Internet of Things (loT). The findings can provide valuable insights for scholars, practitioners, funding agencies, and policymakers in comprehending the patterns and focal points in this domain. The authors and institutes highlighted in this analysis are useful resources for collaborating and staying updated on the newest advances in the loT for Agriculture Industry. © 2024 IEEE.

关键词： Agricultural economics

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Review of the issues and a thorough investigation of biometric authentication systems 1

Review of the issues and a thorough investigation of biometr...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Johri, Prashant Arora, Ms. Sonia Galgotias University Department of Computer Applications Uttar Pradesh Greater Noida India Institute of Engineering and Technology Department of Computer Science and Business System Noida Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

A computer system's user authentication process is based on security mechanisms including passwords, keys, ID cards, and pins, among others. Nonetheless, as innovation propels day to day, there is likewise an expansion in the misappropriation and robbery of these safety efforts. By applying biometrics innovation, an individual can be verified in light of 'what his identity is' as opposed to a component vector got from physiological or potentially social qualities of a person, which is the way biometric security systems became. All the arising biometric innovations and the hardships in every innovation have been completely depicted in this paper to help tenderfoots in picking their exploration subject or a particular biometric innovation. Also, future improvements in biometric innovation are thought of. The main factor in determining whether a claim made by the real entity is true is authentication. Biometrics is one approach to increase authentication's sturdiness among other methods. Since the last decade, biometric technology has been widely used and recognised worldwide to verify a person's identity. Additionally, the technology employed solves the drawbacks of the conventional authentication procedure, such as knowledge-based problems with password and token authentication of an individual. The proposed methodology implemented Convolution neural network with brain organization which will be utilized for the preparation of picture tests and a pre prepared organization of brain can be used in CNN which could eliminate handling time. In results the training accuracy is 6.3 and validation accuracy is 5.3. © 2022 IEEE.

关键词： Machine learning

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Blockchain Enabled Intelligent Digital Forensics system for Autonomous Connected Vehicles

Blockchain Enabled Intelligent Digital Forensics System for ...

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2022 International Conference on Communication, Computing and Internet of Things, IC3IoT 2022

作者： Tyagi, Ranu Sharma, Sachin Mohan, Seshadri Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India University of Arkansas at Little Rock System Engineering Department Little RockAR United States

ISBN: (纸本)9781665479950

Autonomous connected vehicles are revolutionizing the automotive industry vision and impacting their business model. This trend will likely impact other related industries and their business model such as automotive insurance companies, tier-II automotive manufactures, and automotive maintenance service companies. The growing number of connected vehicles will require new policy formulation by the government about privacy and security issues in the era of massive digitalization and automation. Organizations such as 3GPP and IEEE are evolving standards that address security issues for connected vehicles. However, since Artificial Intelligence (AI) and Machine Learning (ML) will play a significant role in 5G networks and beyond, we address the privacy and security issues with a blockchain-based intelligent digital forensics system for autonomous connected vehicles (ACVs) in a connected smart world incorporating artificial intelligence. The proposed system includes a novel algorithm for the autonomous connected vehicles and the users' security and privacy. We utilize short randomizable signatures to anonymously authenticate witness's identities and to protect the witnesses' privacy. Then, we leverage fine-grained access control based on ciphertext-policy attribute-based encryption for evidence access and evaluate the feasibility of the proposed system by simulating the proposed model, its computational costs and communication overhead by implementing a prototype on a local Ethereum blockchain network platform. The simulation results show the proof-of-concept of the proposed system. We envision the application of the proposed system to ensure security and privacy of ACVs and their security applications for use with 5G-V2X and future XG-V2X networks. © 2022 IEEE.

关键词： Blockchain

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation 42

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Haozhao Xu, Wenchao Fan, Yunfeng Li, Ruixuan Zhou, Pan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350334142

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods. © 2023 IEEE.

关键词： Learning systems

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