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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Namburi, Venkata Lakshmi Adapa, Srinivasa Rao Chamala, Shanti Swaroop Kumar Yerram, Manish Gupta, Pranjal Upreti, Kamal Software Systems Engineer Danlaw Inc MI United States Software Engineer OpenLogix LLC MI United States Software Developer Software Bots Inc MI United States Computer Science with IOT VNRVJIET Hyderabad India Computer Science Engineering ADGIPS Delhi India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (纸本)9798350353266

The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility. © 2024 IEEE.

关键词： Autonomous vehicles

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Leveraging Meteorological Data and Machine Learning for Improved Rainfall Forecasting in Australia

Leveraging Meteorological Data and Machine Learning for Impr...

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2024 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2024

作者： Das, Akash Asieb Hasan, K.M. Dept. of Computer Science Federation University Australia Ballarat Australia Software Engineer Glichin’ Dhaka Bangladesh

ISBN: (纸本)9798331521219

This study explores the application of machine learning techniques to predict rainfall in Australia, addressing the challenges posed by the country’s highly variable climate. Utilizing a comprehensive dataset spanning approximately 10 years of daily weather observations from multiple Australian locations, we implemented and compared six machine learning models: Random Forest, XGBoost, Logistic Regression, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) Classifier, and Naive Bayes. The models were evaluated on their ability to predict the binary outcome of rainfall occurrence for the following day. Our results demonstrate that ensemble methods, specifically Random Forest and XGBoost, achieved the highest accuracy at 86%, closely followed by Logistic Regression at 85%. All models showed stronger performance in predicting non-rainy days compared to rainy days, reflecting the dataset’s inherent class imbalance. The ANN Classifier exhibited the highest recall for rain prediction, suggesting its potential utility in identifying possible rainfall events. ©2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Deep Convolutional Neural Networks to Detect Pedestrians Effectively in Various Urban Environments 6

Enhancing Deep Convolutional Neural Networks to Detect Pedes...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Hasan, Md. Mehedi Aman, Nakib Jahan, Md. Wadud Software Engineer i Developer EXperience Hub Rajshahi Bangladesh Pabna University of Science and Technology Department of Computer Science and Engineering Pabna Bangladesh

ISBN: (纸本)9798350385779

In computer vision, accurately identifying pedestrians is a crucial difficulty, especially in applications where safety is of the highest priority, such autonomous driving and urban planning. Deep learning has made progress, but challenges such changing conditions, barriers, and processing power requirements still exist. In this paper, we present an improved deep convolutional neural network (DCNN) designed with pedestrian identification in mind. Improving accuracy in various settings while maximizing processing efficiency is our goal. We carefully prepare the data and create a novel DCNN architecture that can handle a variety of pedestrian appearances and urban surroundings by utilizing well-known datasets such as Penn-Fudan and PnPLO. Our model performs exceptionally well in terms of accuracy, speed, and durability, according to a thorough evaluation that compares it to recognized benchmarks. In addition to making important advances, our study has shown novel approaches to enhancing performance in challenging environments, such as the analysis of transfer learning and the deployment of real-time systems. This work paves the way for future developments in intelligent, self-governing systems and is a maj or leap forward in pedestrian detection technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Handwritten Text Analysis and Identification using Deep Learning Models 3

Handwritten Text Analysis and Identification using Deep Lear...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Bipin Nair, B.J. Koushik, K.S. Ruth, R. Kulsum, Umme Venkitesan, Pranav School of Computing Dept. of Computer Science Amrita VishwaVidyapeetham Mysuru India Software Engineer Frontend Zenloop Gmbh Berlin Germany

ISBN: (纸本)9798350375190

Handwritten text analysis for author identification is a significant but difficult topic with wide applications across several areas. The effectiveness of deep learning models, notably AlexNet and ResNet-101, in identifying the writers of written samples is investigated in this work. This study has experimented with these algorithms to obtain discriminative features for purposes of identification, focusing on text images from various persons. As a part of the research process, the models were trained on a variety of handwritten sample datasets and their performance was assessed using relevant metrics. Using a variety of handwritten text datasets, this study explores writer identification using deep learning models, namely AlexNet with 95.46% accuracy and ResNet-101 with 96.02% accuracy. The models' promise in forensic analysis and historical document authenticity is highlighted by the use of varied handwritten samples in training and evaluation. © 2024 IEEE.

关键词： Deep learning

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Handwriting Analysis for Classification of Human Personality 2

Handwriting Analysis for Classification of Human Personality

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Bipin Nair, B.J. Koushik, K.S. Suraj, Ankitha Nithya, K. Venkitesan, Pranav Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru India Software Engineer Frontend Zenloop GMBH Berlin Germany

ISBN: (纸本)9798350383867

This paper explores handwriting analysis for personality classification, leveraging machine learning algorithms like KNN, SVM, Naive Bayes, Decision Trees, and Random Forest. We have Extracted features such as stroke pressure, letter size, slant, and spacing from a diverse dataset, the study compares algorithm performance, showing promising results in accuracy, precision, recall, and F1-score. Implications for psychology, machine learning, and handwriting analysis are discussed. In our study, the results show that models such as SVM achieved over 95% accuracy, followed closely by KNN with more than 94% accuracy, indicating significant progress in data analysis and Classification modeling. © 2024 IEEE.

关键词： Random forests

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Optimal Cluster and Node Balancing Technique Using K-Mean Based on Q-Learning for Wireless Sensor Network 15th

Optimal Cluster and Node Balancing Technique Using K-Mean Ba...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Raparthi, Mohan Dodda, Sarath Babu Maruthi, Srihari Keshta, Ismail Agarwal, Abhishek Software Engineer Alphabet Life Science DallasTX75063 United States Software Engineer Central Michigan University Dallas Texas75009 United States Senior Technical Solutions Engineer University Of New Haven West Haven United States Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Vermont State University Vermont United States

ISBN: (纸本)9789819786305

Several real-time applications can benefit from wireless sensor networks (WSNs) because of their size, cost-effectiveness, and ease of distribution. It is possible for WSNs to change dynamically as a result of external or internal factors, requiring depreciating dispensable redesign. WSNs are traditionally explicitly programmed, making them hard to react dynamically. Q-learning based on K-Means can be used to avoid such scenarios. Q-learning is used to build a learning mechanism from past simulation data. In order to achieve minimal transmission delay, the work is estimated based on throughput, delay, network lifetime, and packet delivery ratio (PDR). The Q-learning-based mechanism is also investigated as an outcome of the simulation and rounds. Throughput, delay, network lifetime, and PDR analysis of the proposed mechanism are all validated by the comparative investigation against three existing models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction 4

Reinforcement Learning for Language Grounding: Mapping Words...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adapa, Srinivasa Rao Gupta, Himanshu Mukherjee, Susmoy Mallupalli, Praneeth Reddy Avhad, Priyanka Upreti, Kamal OpenLogix LLC Software Engineer MI United States Software Engineer Meta NJ United States VIT Institute Electrical and Electronics Engg. Tamil Nadu India Arcesium Hyderabad India Veermata Jijabai Technological Inst. Computer Engineering Mumbai India Christ University Dept. of Computer Science Delhi NCR Campus India

ISBN: (纸本)9798350353266

Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. © 2024 IEEE.

关键词： Reinforcement learning

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Design of Flexure Robotic Hand for Teleoperation 6th

Design of Flexure Robotic Hand for Teleoperation

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6th Joint International Conference on Multibody System Dynamics and 10th Asian Conference on Multibody Systems, IMSD-ACMD 2020

作者： Zubair, Mohammad Bhandari, Shubham Chatterjee, Indranath Department of Mechanical Engineering Adamas University Kolkata India Software Development Engineer-I HashedIn by Deloitte Bengaluru India Department of Computer Science Manchester Metropolitan University Manchester United Kingdom

ISBN: (纸本)9789819775248

Robots are getting deployed at home and in industries, becoming an integral part of our daily lives. Sophisticated mechanisms are required to make the joints of the robots compact and simple. In this paper, we have developed a flexure joint for the fingers of the robotic hand. The flexure joint is arranged in series to make a three-DOF underactuated finger. Five fingers are arranged to make a robotic hand. Castigliano’s theorem is used to develop the deformation related to the applied loads, using the equation the flexure mechanism was designed. Pseudo-rigid model is developed to determine the stiffness of the flexure mechanism. A virtual model of the flexure mechanism is developed in a CAD environment. Finite Element (FE) simulation is conducted to the different loading conditions to evaluate the performance of the flexure mechanism. It was seen that the flexure mechanism is flexible about the rotational axis and stiff about the other axes. The stiffness model developed using the pseudo-rigid model has a good correlation with the FE simulation model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Improving COVID-19 Forecasts using Hybrid Machine Learning Models 5

Improving COVID-19 Forecasts using Hybrid Machine Learning M...

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5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Onuoha, Michael Kulkarni, Aditya Saxena, Navrati San Jose State University Dept. of Computer Science San JoseCA United States Software Engineer III Walmart Inc. RedmondWA United States

ISBN: (纸本)9798350352863

The spread of COVID-19 has impacted human life in every aspect. Because of the uncertainty of the virus's future impact, highly advanced machine learning approaches are needed to enhance the accuracy of COVID-19 forecasts. Therefore, we propose the following hybrid models to address the challenge: CNN-LSTM-Prophet, GRU-LSTM-Prophet, and RNN-LSTM-Prophet. The RNN-LSTM-Prophet model performs the best in predicting daily COVID-19 cases in the United States as it has the lowest MSE, RMSE, and MAE values. Moreover, all of the hybrid models perform better than the individual LSTM and Prophet models. Based on the benchmark results, RNN-LSTM-Prophet performs the best compared to the other models in this study. The hybrid model also performs better than the baseline as the MSE, RMSE, and MAE are lower by 2.48%, 1.24%, and 1.85% respectively. Governments worldwide can use RNN-LSTM-Prophet to better determine COVID-19's future trajectory and then implement the appropriate policies and protocols. Moreover, the model can be used to deal with new diseases on the rise. © 2024 IEEE.

关键词： COVID-19

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