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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Manoj, Polina Sai Reddy, Kondreddy Sumanth Kumar Sambath, M. Thangakumar, J. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Bradykinesia, tremors, rigidity, and postural instability are symptoms of a degenerative neurological condition called Parkinson's disease (PD) affecting the motor movements. Deep brain stimulation, medication, and other therapies can all be used to treat theParkinson's disease symptoms, yet as of today, there is no effective treatment. Parkinson's disease must be recognized as early and precisely as feasible for effective disease treatment and the development of new therapies. The goal of this work is to create a model that can detect Parkinson's disease based on relevant clinical and demographic variables. It employs a number of machinelearning techniques, including Logistic Regression, Support Vector Machine (SVM), Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Random Forest Classifier, and ensemble method, which is a voting classifier. A significant dataset that contains data on changes in speaking patterns was used to train the model. Using measures for accuracy, precision, recall, and F1-score, the machine learning model's performance is assessed and contrasted with that of the most widely used techniques for Parkinson's disease diagnosis. © 2023 IEEE.

关键词： Support vector machines

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Vehicle Motion Prediction Based on Selective Perception and Weak Lane Guidance 29

Vehicle Motion Prediction Based on Selective Perception and ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Xiaoliang Zheng, Shiqi Zou, Junkang Liu, Yuzhen Hunan University of Science and Technology Department of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350330717

Trajectory prediction is crucial for ensuring the safety and reliability of autonomous driving systems. Accurately predicting the future trajectory of a vehicle can aid drivers or automated driving systems in developing reasonable movement plans and avoiding potential collisions. In real-world scenarios, drivers selectively observe areas that influence their driving decisions based on real-time traffic scenarios, adapting to changing traffic situations to ensure safety. To simulate this behavior, we propose a selective perception module that utilizes historical trajectories and lane centerlines to extract vital information from the changing traffic scene. Furthermore, vehicles tend to follow lane guidance to some extent, we use a normalized flow decoder and small-scale guided loss to induce the distribution of predicted trajectories, slightly increasing the probability of lane centerlines in the predictions as a weak lane guidance. We conduct ablation studies using the public dataset nuScenes and comparative experiments with state-of-the-art methods, showcasing the predictive diversity of our approach while maintaining high accuracy. © 2023 IEEE.

关键词： autonomous driving motion prediction

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Understanding the Emotions Behind Spoken Word Signals With Machine Learning

Understanding the Emotions Behind Spoken Word Signals With M...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Pravin, A. Prem Jacob, T. Sreekrishna, M. Gopika, G.S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350305708

Speech analysis has emerged as a crucial tool in bridging the gap between the real and virtual worlds as the amount of human contact with machines increases. One subfield that has long been investigated in both psychiatry & phonology is the identification of affective states in spoken language. The use cases for speech emotion recognition are many. The fundamental objective of this study is to classify feelings from spoken language and categorise them into the following seven emotion output classes: rage, apathy, contempt, fear, happy, sorrow, & neutrality. The suggested method employs the Berlin dataset of heartfelt moment and is predicated on the MFCC & intensity of the acoustic features used as characteristic sources. To train various feature algorithms, such as SVM, RDF, & Gradient Boosting, the characteristics voice data is converted into an input vectors. The greatest rate of correctly anticipated emotions (81.05%) was achieved by Random Forest. © 2023 IEEE.

关键词： CNN RAVDESS Database RF SVM

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Augmented Reality-based Indoor Navigation using Unity Engine

Augmented Reality-based Indoor Navigation using Unity Engine

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Rokesh Maran, B. Giridharan, L. Krishnaveni, R. Hindustan Institute of Technology and Science Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350333602

The evolution of technology with the introduction of mobile devices has provided users with day-to-day advancements in existing technologies. Augmented Reality (AR) is combined with an Artificial Intelligence navigation agent to trackthe environment that is provided specifically. Unity 3D engine provides an inbuilt AR framework easily accessible and the navigation mesh agent tracks penetrating paths with segregated walkable areas and areas that cannot be accessed. The current machine learning systems use inertial sensors and high-end cameras for computer vision-based motion tracking. Whereas, this system does not require external hardware as the environment model is constructed with provided measurements in Unity Engine and virtual trackers, which can be functioned in any AR-enabled device. The proposed model aims to provide the framework with real-time virtual visualization for a better user experience. © 2023 IEEE.

关键词： Navigation

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The Future of Online Shopping: Blockchain- Powered E-Commerce Platforms

The Future of Online Shopping: Blockchain- Powered E-Commerc...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Sri, Nemani Divya Ayyasamy, Ramesh Kumar Chinnasamy, P. Vellanki, Sreelakshmi Vujwala, Maddula MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Faculty of Information and Communication Technology Kampar Malaysia School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798331540685

A cutting-edge online marketplace that uses blockchain technology to transform how we purchase and sell goods and services is known as a blockchain- powered e-commerce platform. This platform uses a decentralized network of computers, also known as nodes, to maintain a transparent and impenetrable record of all transactions instead of depending on a central authority like a bank or a company. A blockchain-based e-commerce platform is a secure, transparent, and trusted online marketplace. It does away with the need for intermediaries, improves security, and provides a world market where users can confidently conduct peer-to-peer transactions. This novel strategy could change how online commerce and shopping are performed. © 2024 IEEE.

关键词： Marketplaces

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Dempster-shafer deep capsule attention model (DDCAM)

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Multimedia Tools and Applications 2025年 1-29页

作者： Mehravaran, Zahra Ghanizadeh, Ahmad Navid Hamidzadeh, Javad Harati, Ahad Mashhad Iran Department of Computer Science Saarland University Saarbrücken Germany Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran

A capsule neural network faces significant challenges in achieving high accuracy on complex datasets due to its high computational complexity and limited ability to represent features. To overcome these limitations, this paper proposes a novel framework, Dempster-Shafer Deep Capsule Attention Model (DDCAM), that combines 3D convolution with attention mechanisms to improve feature representation. This model utilizes the Dempster-Shafer Theory to manage uncertainty and optimize feature selection, resulting in more reliable and accurate learning. In this architecture, dense capsule blocks are used to improve gradient flow and mitigate the effects of vanishing gradients. The DDCAM consistently outperforms state-of-the-art capsule networks on a wide range of datasets, especially complex datasets such as MNIST, CIFAR10, SVHN, and Fashion MNIST. DDCAM improves accuracy by 1.29% on CIFAR10 and 0.76% on SVHN compared to existing models, while achieving precision improvements of 1.22% and 1.08% on CIFAR10 and SVHN, respectively. Additionally, experiments conducted on the AffNIST dataset demonstrate the robustness of DDCAM in handling affine transformations, where it achieved significantly higher accuracy compared to existing models. These results illustrate the model’s enhanced ability to capture complex data relationships and improve both accuracy and precision, advancing capsule neural network performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Affine transforms

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FedRec:Trusted rank-based recommender scheme for service provisioning in federated cloud environment

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Digital Communications and Networks 2023年第1期9卷 33-46页

作者： Ashwin Verma Pronaya Bhattacharya Umesh Bodkhe Deepti Saraswat Sudeep Tanwar Kapal Dev Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabadGujarat382481India Department of Institute of Intelligent Systems University of JohannesburgPO Box 524South Africa

The emergence of on-demand service provisioning by Federated Cloud Providers(FCPs)to Cloud Users(CU)has fuelled significant innovations in cloud provisioning *** to the massive traffic,massive CU resource requests are sent to FCPs,and appropriate service recommendations are sent by ***,the FourthGeneration(4G)-Long Term Evolution(LTE)network faces bottlenecks that affect end-user throughput and ***,the data is exchanged among heterogeneous stakeholders,and thus trust is a prime *** address these limitations,the paper proposes a Blockchain(BC)-leveraged rank-based recommender scheme,FedRec,to expedite secure and trusted Cloud Service Provisioning(CSP)to the CU through the FCP at the backdrop of base 5G communication *** scheme operates in three *** the first phase,a BCintegrated request-response broker model is formulated between the CU,Cloud Brokers(BR),and the FCP,where a CU service request is forwarded through the BR to different *** service requests,Anything-as-aService(XaaS)is supported by 5G-enhanced Mobile Broadband(eMBB)*** the next phase,a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender(RBR)model based on the CU *** the final phase,based on the matching recommendations between the CU and the FCP,Smart Contracts(SC)are executed,and resource provisioning data is stored in the Interplanetary File Systems(IPFS)that expedite the block *** proposed scheme FedRec is compared in terms of SC evaluation and formal *** simulation,FedRec achieves a reduction of 27.55%in chain storage and a transaction throughput of 43.5074 Mbps at 150 *** the IPFS,we have achieved a bandwidth improvement of 17.91%.In the RBR models,the maximum obtained hit ratio is 0.9314 at 200 million CU requests,showing an improvement of 1.2%in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.76

关键词： Blockchain 5G-enhanced mobile broadband Federated clouds Rank-based recommender model Smart contracts

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Book Genre Classification System Through Supervised Learning Technique 1

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sethy, Abhisek Rout, Ajit Kumar Nayak, Soumya Ranjan Kumar, Raghvendra Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Odisha Gunupur India

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

Machine learning is used in this digitizing era so there is an ever-increasing desire for computers to execute human-like jobs. Text classification is rapidly becoming one of machine learning’s most significant tasks. Manually reading the entire book and classifying it based on its genre is, however, a time-consuming operation. As a result, machine learning methods are crucial for classification and in this paper, a book description-based text classification is depicted, where with wide range of wealth of information about the book’s contents. In the proposed system, a variety of classifiers including Multinomial Naive Bayes (MNB), Gradient Boosting (GB), and Random Forest (RF) are employed to categorize the book’s genre. According to the findings, the Naive Bayes Classifier surpasses other two techniques in terms of accuracy, while the other two classifiers provide comparable results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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A Comparative Study of Detecting Violation of Face Mask and Social Distancing using Different Deep Learning Models 7

A Comparative Study of Detecting Violation of Face Mask and ...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rahman, Ashikur Hasib, Abdullah Al Khabir, Mahpara Sourov, Murshid Bin Masud Shahriar, Shoeb Mohammad Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350397253

The growing pandemic caused by COVID-19 has been making headlines recently, causing a global disaster with an unavoidable spread. The WHO recommends wearing a face mask and maintaining physical distance. This research can assist in keeping track of social distance and it can also spot face masks. The entire system is set up in public settings. Images and videos are used by this system to identify persons. Frame by frame processing is done on the video. People are recognized by image processing with TensorFlow and OpenCV. MobileNetV2 and ResNet50 are used for detecting the face mask violation. It first recognizes the crowd's presence before calculating the centroid distances between individuals using an object tracking algorithm and a distance algorithm. In order to assess the social distance between people, the Euclidean distance is typically employed to calculate how far off each pair of centroids of the bounding box is identified from one another. At a distance of six feet, the program can determine the number of pixels in an image. The detection models MobileNetV2 and ResNet50, respectively, obtained accuracy of 97% and 98% with the large dataset, which contains 12000 samples with and without masked faces. © 2023 IEEE.

关键词： Large dataset

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Statistical Testing & TOPSIS Analysis of Acute Sinusitis Interventions

Statistical Testing & TOPSIS Analysis of Acute Sinusitis Int...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Pisal, Tejas Kumar, Hemant Kumar, Praveen Gote, Pradanyawant Faculty of Engineering & Technology Department of Computer Science & Design Wardha Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Basic Sciences and Humanities Wardha Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Computer Science & Medical Engineering Wardha Maharashtra Sawangi442001 India

ISBN: (纸本)9798350361537

Acute sinusitis, often occurring as a consequence of recovering from a cold, presents a significant health concern due to its impact on the interconnected hollow spaces located behind the cheekbones, forehead, and nose. These sinus passages play a crucial role in the respiratory process by facilitating the smooth flow of air towards the lungs. Given the discomfort and potential complications associated with acute sinusitis, there is a pressing need for effective pharmaceutical interventions. This paper embarks on a comprehensive analysis of such interventions, leveraging real-world data subjected to rigorous examination through the Kruskal-Wallis statistical testing and the TOPSIS decision-making method. Our study is dedicated to discerning the most effective drug for acute sinusitis, considering a spectrum of factors including efficacy, ease of use, user satisfaction, and cost-effectiveness. By synthesizing empirical evidence and statistical analyses, we aim to provide valuable insights that can inform clinical decision-making and optimize patient care in the management of acute sinusitis. This research holds promise in guiding healthcare providers towards evidence-based prescribing practices, enhancing treatment outcomes, and ultimately improving the quality of life for individuals suffering from acute sinusitis. © 2024 IEEE.

关键词： Decision making

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