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A secured trusted routing using the structure of a novel directed acyclic graph-blockchain in mobile ad hoc network internet of things environment

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Multimedia Tools and applications 2024年第40期83卷 87903-87928页

作者： Ilakkiya, N. Rajaram, A. Department of Masters in Computer Application E.G.S Pillay Engineering College Tamilnadu Nagapattinam611002 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamilnadu Nagapattinam611002 India

In the contemporary technology context, several physical things may be used to support human activities. The Internet of Things is a cutting-edge technology solution that is used to link physical items with the digital world via a range of networks as well as communication technologies. The Internet of Things benefits from the smart settings in which mobile ad hoc networks (MANET) operate, making it more appealing to users and financially viable. Wireless sensors with MANETs may be integrated using the Internet of Things to develop new MANET-IoT devices and IT-based networks. Such a strategy lowers network deployment costs while increasing user mobility. That does, however, also bring up fresh, challenging issues about networking concerns. We introduced a new DAG-Blockchain architecture for MANET-IoT security in this study. The Multi-Factor PUF identification technique protects the network. Network topology is separated into clusters with all allowed nodes. We introduced the Secure Trust-based Dijikstra’s Method with numerous criteria for secure data transfer. We presented a Bi-Directional GRU for deep packet analysis. Deep packet inspection is used to identify intrusions, which are then blocked by a blocking mechanism. The suggested method achieved increased efficiency in the packet delivery ratio, productivity, time analysis, detection accuracy, and security level using the combination of algorithms. The numerical findings from the simulation and analysis of the proposed DAG-IDS in the WSN-IoT network reveal significant improvements across various performance metrics. Specifically, the DAG-IDS demonstrates enhanced network lifetime, achieving up to 220 cycles compared to 100 cycles in the BLOCKCHAIN method. The analysis also highlights superior packet delivery ratios (PDR) exceeding 90%, reduced delays, and efficient throughput, showcasing the effectiveness of the proposed approach in optimizing the network's quality of service, energy efficiency, and security. ©

关键词： Mobile ad hoc networks

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Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots 2

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Singh, Aashdeep Dhawan, Sakshi Verma, Jyoti Singh, Gurpreet Kaur, Amanpreet Dept. of Computer Science & Engineering Punjab Rajpura India Dept. of Computer Application Punjab Rajpura India Chitkara University Institute of Engineering & Technology Dept. of Computer Science & Engineering Punjab Rajpura India

ISBN: (纸本)9798350360523

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning. © 2024 IEEE.

关键词： Motion planning

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Predicting Bank Loan Approval with Artificial Intelligence 1

Predicting Bank Loan Approval with Artificial Intelligence

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Ratan, Raj Yadav, Sudeept Singh Malhotra, Ritika Kumar, Avneesh Galgotias University School of Computer Application and Technology Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

In the contemporary financial landscape, banks play a crucial role in facilitating individuals and businesses with their monetary needs. Central to their operations is the assessment of borrower creditworthiness through loan approval processes, ensuring the security of loans extended. However, traditional manual assessment is not only time-consuming but also prone to human error. Therefore, there is an urgent need to develop an automated and reliable credit approval system. In this study, diverse machine learning calculations are compared to decide the ideal show for anticipating whether a bank ought to endrose a advance for a person. Employing a dataset gotten from Kaggle, diverse machine learning methods to a degree logistic reversion, decision trees, K-nearest neighbors (KNN), haphazard woodlands and neural networks are exploited. The animation of these models is evaluated handling measurements to a degree veracity, accuracy, review and F1 score. In particular, the influence of the model is improved by taking into account a number of factors that include the borrower's personal characteristics, such as tax liabilities, purpose of the loan, credit history, loan amount and home ownership, in addition to traditional checking account information. This holistic approach allows for a more accurate estimation of the likelihood of loan default. Furthermore, the logistic regression model proves to be the top performer with an impressive accuracy of 81.89%. Additionally, a trait importance analysis is performed to identify the variables that have the greatest impact on loan approval decisions. The results underline the superiority of ML models over conventional statistical approaches and illustrate their potential to significantly improve the accuracy of loan forecasting. © 2024 IEEE.

关键词： Logistic regression

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Responsible AI Framework For Large Language Models (LLMs) 11

Responsible AI Framework For Large Language Models (LLMs)

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Kandhari, Naman Tripathi, Bharat Kumar, Sheetesh Singh, Kulvinder Singh, Narendra Pal Chandigarh University Department of Computer Science & Engineering Punjab India Allenhouse Business School Department of Computer Application UP India Invertis University Bareilly Department of Computer Science & Engineering UP India

ISBN: (纸本)9798350379136

Large language models (LLMs) have become quite popular in recent years due to their remarkable capacity to produce language that is human-like. The creation and use of LLMs, however, also bring up serious ethical questions on topics like prejudice, discrimination, privacy, security, transparency, and unexpected consequences. Responsible AI frameworks for LLMs are becoming more and more necessary in order to solve these issues. A responsible AI framework can offer direction on how to create and use LLMs in a way that is morally right, just, and advantageous for everyone. This paper covers important factors for creating responsible AI frameworks for LLMs and emphasizes the need for such frameworks. Transparency, justice, privacy, responsibility, safety, and social duty are some of these factors. We can ensure that these models are created and used in a way that is consistent with societal values and goals and that they help to create a better and more just world by adopting a responsible AI framework for LLMs. © 2024 IEEE.

关键词： Differential privacy

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Electroencephalography (EEG) Based Prediction of Learning and Responsiveness among Students 7

Electroencephalography (EEG) Based Prediction of Learning an...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Agrawal, Alok Kumar Ahmed, Bilal Jakhar, Yogesh Kumar Bajaj, Karan Kumar, Girish Kumar, Mukesh Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India School of Computer Application Lovely Professional University India University of Engineering & Management Department of Computer Application Jaipur India

ISBN: (纸本)9798350340600

The constructivist method of teaching has been shown to have a major influence on the learning outcomes of students. In order to improve classroom instruction, it is essential to evaluate students' levels of involvement and attentiveness while they are being taught. Electroencephalography (EEG) can now be used to assess students' attentiveness in real time thanks to recent neuro-engineering breakthroughs and mobile brainwave sensors' availability. The purpose of this study is to provide an overview of EEG-based techniques with the intention of predicting constructivism and engagement among students of varying levels. The purpose of this study is to evaluate the efficacy of these approaches and to determine the extent to which they have the potential to enhance the design of future learning systems. This survey analyzes whether or not approaches based on EEG may be used to determine the level of constructivism and involvement displayed by pupils. The findings provide helpful insights that can be used to develop tailored teaching strategies and improve education's overall efficiency. However, some obstacles need to be overcome in order for EEG-based predictions to be accurate. Some of these obstacles include data quality and variability, individual differences, interpretability, generalization, and ethical considerations. © 2023 IEEE.

关键词： Forecasting

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Lifestyle Classification Amidst Psychological Impact of COVID: A Machine Learning Approach 7

Lifestyle Classification Amidst Psychological Impact of COVI...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

ISBN: (纸本)9798350340600

The COVID pandemic has caused significant social changes, affecting not just physical health but also individuals' psychological well-being. This study investigates the complex interaction between lifestyle choices and the psychological impact of the pandemic, using a Machine Learning (ML) technique to categorise and assess distinct lifestyle patterns in the context of COVID. In this study, a significant data from a variety of sources, including questionnaires, social media, and healthcare records, to get insight into how the pandemic has affected lifestyle characteristics such as exercise, food, social connections, work habits, and leisure activities. This study has analysed the psychological effects of these lifestyle changes, including stress, anxiety, sadness, and overall mental health. This research article implements the basic classification models like (Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, and XGBoost), which can classify various lifestyle changes among individuals based on their pandemic-related behaviours. The impact of these lifestyle changes on mental health outcomes is investigated, offering light on the intricate interplay between personal decisions and psychological well-being during times of crisis. © 2023 IEEE.

关键词： Machine learning

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Smart Water Management for a Sustainable Tomorrow: A Digital Twin Approach for Rural India 3

Smart Water Management for a Sustainable Tomorrow: A Digital...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Singh, Dinesh Kumar Sharma, Vishnu Fce Poornima University Department of Computer Science and Engineering Jaipur India Fce Poornima University Department of Computer Science and Application Jaipur India

ISBN: (纸本)9798331518592

Rural areas in India, such as Randullabad village in Satara, are facing significant water challenges related to UN SDG6. It includes inadequate water supply, inefficient distribution, and poor water quality. Current methods of water governance rely on manual data collection and limited use of sensors. This leads to lack of modelling, inaccurate estimation, delayed response times, and inefficient decision-making. These limitations results in water-related conflicts, water-wastage, and economic losses. To address these issues, a digital twin approach is proposed for sustainable water management. This approach integrates existing and historical data with new data from advanced sensors, using machine learning algorithms to create a virtual replica of the village's water supply network. The proposed methodology and high-level implementation architecture of Digital Twin of Randullabad Watershed and Groundwater (DToGW) could set new water-governance standards at the village level and potentially transform water management in rural communities. © 2024 IEEE.

关键词： Decision making

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Saha, Rupa Karmakar, Koushik Biswas, Suparna Narula Institute of Technology Department of Computer Application India Narula Institute of Technology Department of Computer Science and Technology India Department of Computer Science & Engineering Makaut W B India

ISBN: (纸本)9798331513894

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis. © 2025 IEEE.

关键词： Wearable sensors

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Breast MRI Registration Using Gorilla Troops Optimization 1st

Breast MRI Registration Using Gorilla Troops Optimization

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Nayak, Somen Mondal, Subhodip Department of Computer Application University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur India

ISBN: (纸本)9789811954023

Metaheuristics assume a significant part in enhancing issues, and most of them are enlivened by the aggregate knowledge of living peculiarities in nature. In excess of 10% of ladies are experiencing bosom disease in their lives in the entire world. Registration of breast magnetic resonance imaging is a method to align pre- and post-contrast images to analysis and classifying of cancer category. In this research, breast magnetic resonance imaging registration using gorilla troops algorithm is proposed. GTO is a metaheuristics-based optimizations algorithms that tested to registering the image of breast magnetic resonance. After that, images are successfully registered using GTO algorithm. The result of GTO-based registration method is compared with registration based on PSO method. The results implicates, in case of registration of breast magnetic resonance images, the GTO-based registration methods beat the PSO-based registration method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Particle swarm optimization (PSO)

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Role of cloud computing in management and education

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Materials Today: Proceedings 2023年 80卷 3726-3729页

作者： Gupta, Awaneesh Mazumdar, Bireshwar Dass Mishra, Manmohan Shinde, Priyanka P. Srivastava, Surabhi Deepak, A. United Institute of Management Prayagraj India Department of Computer Science &Engineering IERT Prayagraj211002 India Department of Computer Application United Institute of Management Prayagraj211010 India Master of Computer Application Department Government College of Engineering Maharashtra Karad415124 India Koneru Lakshmaiah Education Foundation India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

In the current day, information and communication technologies are tightly linked to provide ultimate business solutions. Cloud computing is one of these topics that has recently been hotly contested and carefully explored, but still has a lot of room for further research and growth. Technology-assisted learning is becoming more common, with most educational institutions across the globe using learning management systems, content management systems, virtual networks, and virtual machines to enhance student learning. In this day and age, educational institutions are even using private clouds to improve the student experience. Despite the fact that many educational institutions, colleges, and universities have effectively implemented and incorporated instructional technology, many of them still fall short in a variety of areas. The purpose of this research is to investigate the influence of cloud computing technology on educational institutions and corporate management. © 2021

关键词： Cloud computing

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