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5th college of science International Conference on Recent Trends in Information technology, CSCTIT 2022

作者： Mohammed, Nadia M. Al-Kateeb, Zeena N. College of Computer Science and Mathematics University of Mosul Department of Software Engineering Mosul Iraq College of Computer Science and Mathematics University of Mosul Department of Computer Science Mosul Iraq

ISBN: (纸本)9798350334920

The widespread exchange of data in various formulas such as text and images via multimedia has created a great need for encryption techniques development, that can protect sharing of important secure data from untrusted or unauthorized access. In this paper, a two-level encryption method is proposed, the first level involves the use of an RC4 algorithm with several different length keys. The number of used keys depends on the length of the input original text. The keys are extracted from a 4D hyperchaotic Sprott_S system. The first encryption process is followed by the second encryption using the XOR gate for the first encryption output, with another key chain extracted from the same hyperchaotic system, that is pre-processed differently. Several measurements were used to measure the efficiency of the algorithm, including the time of application of the algorithm, histogram, bit error rate, and correlation. The experiments that were conducted on texts of different sizes showed the efficiency of the algorithm's performance, its high level of security, and the lack of time it needs, which confirms its quality. © 2022 IEEE.

关键词： Cryptography

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Inclusion of IoT, ML, and Blockchain Technologies in Next Generation Industry 4.0 Environment

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Materials Today: Proceedings 2023年 80卷 3471-3475页

作者： Shrivastava, Anurag Murali Krishna, K. Lal Rinawa, Moti Soni, Mukesh Ramkumar, Gowtham Jaiswal, Sushma ECE Lakshmi Narain College of Technology and Science Indore India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Andhra Pradesh Duvvada Visakhapatnam India Department of Mechanical Engineering Government Engineering College Jhalawar Rajasthan India Computer Science & Engineering Department Jagran Lackcity University Bhopal India SRM Institute of Science and Technology Chennai India Koni Bilaspur495009 India

Industry 4.0 is a revolutionizing concept which has a more significant implication to numerous industries of various sectors worldwide. Industry 4.0 acts as a technical catalyst in accelerated growth through systematic adaptation and new-generation cutting-edge engineering tools. IoT, Blockchain, Artificial Intelligence, Augmented Reality, 3D printing, Big-data analytics, cloud-computing are some of the primary technologies which work in join hand to make the vision of Industry 4.0 successful. In addition, it promotes autonomous industry, which in turn considerably reduces human intervention, thus resulting in efficient working and safeguard companies' interests. Due to the faster adaption of Industry 4.0, authors have attempted to define each and every concept related to it and to explore a futuristic roadmap, special focusing on its implication in the contemporary market. In the 21st century Industry, 4.0 is the need of the hour, so it has become essential to understand the state-of-the-art technologies involved. © 2021

关键词： Industry 4.0

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Integrating environmental sustainability in construction Time-Cost trade-off for decision-making using hybrid NSGA-III and MOPSO approach

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Asian Journal of Civil engineering 2025年第4期26卷 1527-1542页

作者： Nagayo, Analene Montesines Singh, Rekha Dhawan, Amit Manjunath, T.C. Qasem, Altayeb Sethi, Krushna Chandra Sharma, Kamal Department of Engineering and Technology University of Technology and Applied Sciences-Al Musanna Al Muladdah Oman Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Department of Civil Engineering Dr. Bhim Rao Ambedkar Polytechnic College Gwalior India Computer Science Engineering Department Rajarajeswari College of Engineering Bangalore India Department of Building Engineering College of Architecture and Planning Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Civil Engineering Centurion University of Technology and Management Gajapati India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India

Construction project management often involves optimizing time and cost while ensuring minimal environmental impact. This study presents an innovative hybrid approach combining non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective particle swarm optimization (MOPSO) to address the time-cost-environmental sustainability trade-off (TCEST) in construction projects. The proposed model aims to minimize project completion time and cost while maximizing environmental sustainability. A case study is conducted to validate the model, incorporating diverse construction activities and their respective time, cost, and environmental sustainability metrics. The results reveal Pareto-optimal solutions demonstrating significant trade-offs among the three objectives. The hybrid approach outperforms standalone algorithms in terms of solution diversity, convergence, and hypervolume indicators. Weighted sum methods are employed to select the most suitable solution from the Pareto front based on project priorities. Correlation analysis further explores interdependencies among objectives, emphasizing the feasibility of balancing these critical factors. The study contributes a robust decision-support tool for sustainable project planning, facilitating informed decision-making in modern construction management. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Hybrid Optimization MOPSO NSGA-III Sustainable Construction Time–Cost-Environmental Trade-off

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Federated Causally Invariant Feature Learning 39

Federated Causally Invariant Feature Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Xianjie Yu, Kui Cui, Lizhen Yu, Han Li, Xiaoxiao School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education China School of Software Shandong University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://***/Xianjie-Guo/FedCIFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Deployment Strategies for Wireless Sensor Networks Using Math Modeling Techniques

Deployment Strategies for Wireless Sensor Networks Using Mat...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sahal, Nisha Murugan, R. Raizada, Aasheesh College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

Wi-Fi sensor networks comprise small, low-value, low-energy sensors configured to interface with the physical surroundings. Due to their constrained power, wireless sensor networks should be deployed and operated in a distributed way. It affords an undertaking for many obligations, which include topology manipulation, energy-green routing, and coverage tracking. This paper proposes numerous deployment strategies for wireless sensor networks using mathematical modeling strategies. Mainly, the proposed techniques use convex optimization and integer programming to research the trade-offs between network insurance, power intake, interference, and topology. Simulation effects display the effectiveness of the proposed techniques in enhancing network overall performance. The paper also discusses extending those strategies to metaheuristics further to improve the scalability and applicability of the proposed method. © 2024 IEEE.

关键词： constrained networks obligations optimization surroundings

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Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

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computer Systems science & engineering 2023年第4期45卷 869-886页

作者： Sirisati Ranga Swamy SPhani Praveen Shakeel Ahmed Parvathaneni Naga Srinivasu Abdulaziz Alhumam Department of CSE Vignan’s Institute of Management and Technology for Women GhatkesarTelanganaIndia Department of CSE PVPSITVijayawadaAndhra Pradesh520007India Department of Computer Science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science and Engineering-AIML VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at *** pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already *** established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the *** problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical *** models help predict the effects of the disease outbreak and help detect the effects in the coming *** this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan *** are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from *** current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better *** model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion *** is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

关键词： Chest CT COVID-19 classification ROC curves multi-feature disease analysis

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Classification of Brain Disorder Diseases using Machine Learning based Data Mining Techniques

Classification of Brain Disorder Diseases using Machine Lear...

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2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Shalini, Alajangi Balarengadurai, C. Prasad, Boddu Ravi Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of CSE Marri Laxman Reddy Institute of Technology and Management Hyderabad India

ISBN: (纸本)9798350376913

Brain disorders include a wide range of illnesses, including mental illnesses and neurological disorders, for which an early and precise diagnosis is essential to successful treatment. This review aims to give an in-depth analysis of current developments and problems in this important field by examining the evolution of machine learning (ML) with respect to data mining usage in the categorization of brain illnesses. The study starts by clarifying the need of prompt and accurate diagnosis in brain illnesses, highlighting the possibility of utilizing machine learning and data mining tools to supplement conventional diagnostic approaches. A thorough analysis of the literature demonstrates the wide range of approaches used for classifying brain disorders, from sophisticated deep computational systems to traditional machine learning methods. The exploration clarifies the complexities of model construction by covering important topics including feature selection, dataset qualities, and assessment measures. Numerous research show how well machine learning approaches may be used to differentiate between various brain illnesses, showing encouraging outcomes in regard to specificity, sensitivity, and accuracy. The report also outlines the difficulties those working in this subject encounter, such as the requirement for vast and diverse datasets, accessibility, and variety of data. Potential alternatives are also covered, including the creation of model-based interpretation for improved practicality and the synthesis of multimodal information. This study is an invaluable tool for scholars, employees, and health care providers working at the nexus of data mining, machine learning, and brain illness categorization. Through a comprehensive analysis of the present status of the sector and the identification of potential areas for advancement, this study adds to the continuing efforts to use technologies in the identification and therapy of brain illnesses. © 2024 IEEE.

关键词： Data mining

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Currency Portfolio using Combination of Assets and Cryptocurrency based on LSTM-TLS 2022

Currency Portfolio using Combination of Assets and Cryptocur...

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13th International Conference on E-Business, Management and Economics, ICEME 2022

作者： Guo, Liangyuan Deng, Hewen He, Wenyue College of Mathematics and Statistics Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450396394

Virtual currency has been greeted with an avalanche of attention these days. In this case, allocate investments into traditional assets and virtual currency properly seems very important. In this paper, we select gold and bitcoin as our research objects, and select a series of representative indicators in the financial field. After data preprocessing, XGBoost algorithm is used to sort the importance of indicators, thus eliminating some unimportant indicators. Next, LSTM is used to predict the price of gold and bitcoin respectively. Therefore, the portfolio can be built based on it. In reality, trades often come with transaction costs. So we improve the Mean-Variance model considering the transaction costs, so as to get the initial portfolio strategy. On this basis, taking investment potential into account, we propose Traffic Light Signal(TLS) model, and successfully increasing the gross profit rate from 11.582% to 13.614%. Finally, we prove our portfolio model earns the highest returns by comparing it to other traditional portfolio models in terms of metrics Cumulative Yield, Annual Yield, and Max Drawdown Ratio. © 2022 ACM.

关键词： Bitcoin

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Paws and Pixels: Enhancing Canine Dermatology Through Image Analysis

Paws and Pixels: Enhancing Canine Dermatology Through Image ...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Yashu Kukreja, Vinay Srivastava, Prateek Garg, Ashish Hariharan, Shanmugasundaram Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Vardhaman College of Engineering Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350361537

This study focuses on the urgent need for sophisticated diagnostic tools in the field of canine dermatology. We have created a sophisticated system that uses Convolutional Neural Networks (CNN) and Random Forest to accurately identify and categorise seven common skin disorders in dogs. The model attained an overall accuracy of 79.02% by using a diversified dataset consisting of 4,053 photos. The precision and recall measures provide detailed and subtle observations about the model's performance in relation to various illnesses, highlighting significant advantages in some scenarios. The research emphasises the capacity of machine learning to assist veterinarians in precise and effective diagnoses, especially in the realm of skin illnesses that often present difficulties for traditional approaches. Although the current level of accuracy is encouraging, further improvement and enlargement of the dataset are necessary to further strengthen the model's reliability and suitability in various clinical situations. This study enhances canine healthcare by providing a crucial tool for promptly and accurately identifying skin illnesses. As a result, it leads to better treatment results and general well-being for our canine friends. © 2024 IEEE.

关键词： Convolutional neural networks

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An Analysis of Security Challenges in Internet of Things (IoT) based Smart Homes 2

An Analysis of Security Challenges in Internet of Things (Io...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Joel, M. Robinson Manikandan, G. Bhuvaneswari, G. Kings Engineering College Department of Information Technology Chennai India Loyola Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350346640

The term "Internet of things (IoT) security"refers to the software industry concerned with protecting the IoT and connected devices. Internet of Things (IoT) is a network of devices connected with computers, sensors, actuators, or users. In IoT, each device has a distinct identity and is required to automatically transmit data over the network. Allowing computers to connect to the Internet exposes them to a number of major vulnerabilities if they are not properly secured. IoT security concerns must be monitored and analyzed to ensure the proper working of IoT models. Protecting personal safety while ensuring accessibility is the main objective of IoT security. This article has surveyed some of the methods and techniques used to secure data. Accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve are the assessment metrics utilized to compare the performance of the existing techniques. Further the utilization of machine learning algorithms like Decision Tree, Random Forest, and ANN tests have resulted in an accuracy of 99.4%. Despite the results, Random Forest (RF) performs significantly better. This study will help to gain more knowledge on the smart home automation and its security challenges. © 2023 IEEE.

关键词： Internet of things

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