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2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2021

作者： Carvalho, Rodrigo Al-Tam, Faroq Correia, Noelia CEOT University of Algarve Faro Portugal Aberystwyth University Deparment of Computer Science Aberystwyth United Kingdom Faculty of Science and Technology CEOT University of Algarve Faro Portugal

ISBN: (纸本)9781665444941

LoRaWAN has emerged as one of the most popular technologies in the LPWAN industry due to its low cost and straightforward management. Despite its relatively simple architecture, LoRaWAN is able to optimize energy, data rate, and time on-air by means of an adaptive data rate mechanism. In this paper, a reinforcement learning agent is designed to contrast with the central ADR component. This new agent operates seamlessly to all end nodes while still reacting quickly to changes. A comparative analysis between the classic ADR and the proposed RL-based ADR agent is done using discrete event simulation. Results show that the new ADR mechanism can determine the best configuration and that the proposed reward function fits the intended learning process. © 2021 IEEE.

关键词： Discrete event simulation

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Whether Prediction using Auto-correlation and Machine Learning Algorithms

Whether Prediction using Auto-correlation and Machine Learni...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shruti V. Bharati Sanjay S. Shitole Shakti Sharma Digambar V. Puri Department of Information Technology SNDT University Mumbai India School of Computer Science Engineering and Technology Bennett University Greater Noida U.P India Deparment of Electronics and Telecommunication DYP University Navi-Mumbai India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

As economic expansion accelerates, the deterioration of air quality persists. The primary catalysts for the initiation and progression of intense air pollution processes are the emissions of high pollution intensity and adverse weather conditions. The current scenario of rapid climate change has rendered traditional weather prediction methods ineffective and more challenging. Consequently, there is a pressing need for enhanced and dependable weather forecasting techniques. These forecasts have significant implications for a country’s economy and the well-being of its populace. The central goal of present study is assess and predict the air quality index (AQI) for Mumbai city of Maharashtra in India based on seasonal auto-regressive moving average (SARMA) with machine learning models. To validate the proposed technique, a six month dataset were utilized that were collected at the Shreemati Nathibai Damodar Thackersey Women’s University, Santacruz (w), Mumbai. This study provides the minimum root mean square error (RMSE) between predicted and recorded AQI. The performance comparative analysis of different kernels of support vector machine (SVM) with K-Nearest Neighbour (KNN) has been evaluated to validate the proposed method.

关键词： Support vector machines Biological system modeling Urban areas Weather forecasting Pressing Machine learning Predictive models

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications 7

Next-Generation Computational Models for Enhanced Interpreta...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gowtham Raj, R. Sreekanth, S. Nijhawan, Ginni Gupta, Shubhi Sharma, Saurabh Mounika, T. New Horizon College of Engineering Deparment of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lloyd Law College Plot No. 11 Knowledge Park II Greater Uttar Pradesh Noida201314 India Lovely Professional University Phagwara India Amity University Amity School of Engineering & Technology Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts. © 2024 IEEE.

关键词： Frequency domain analysis

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A Ready-to-Use Beam Steering Application Using a 3D-Printed System Integrated with a Dielectric Lens and Ultra-Wideband Antenna

A Ready-to-Use Beam Steering Application Using a 3D-Printed ...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Patchadaporn Sangpet Virote Pirajnanchai Suramate Chalermwisutkul Nonchanutt Chudpooti Deparment of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

The study introduces a 3D-printed lens assembly that has been rotated and equipped with an ultrawideband (UWB) antenna, designed specifically for beam-steering applications. The primary objective is to achieve beam steering by integrating the rotating 3D-printed lens assembly with a hemispherical lens and UWB antenna. The UWB antenna is constructed on an FR-4 substrate, while the hemispherical lens and the rotating 3D-printed lens assembly are composed of Acrylonitrile Butadiene Styrene (ABS). To allow the hemispherical lens to rotate to a certain angle, a collection of 3D-printed lenses that can be rotated was created. These lenses were marked with five predetermined angles: 300 degrees, 330 degrees, 0 degrees, 30 degrees, and 60 degrees. Both the modeling and experimental findings demonstrate that the UWB antenna with the 3D-printed rotatable hemispherical lens assembly is unable to guide the beam at a low frequency of 3.1 GHz. Nevertheless, at frequencies of 7.0 GHz and 10.6 GHz for intermediate and high frequencies, respectively, the beam can be directed based on the rotation of the hemispherical lens.

关键词： computers Beam steering Ultra wideband antennas Frequency measurement Dielectrics High frequency Assembly

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The Novel Protocol Uses Low Computational Complexity to Authenticate Edge Devices on Smart Grid Systems

The Novel Protocol Uses Low Computational Complexity to Auth...

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India International Conference on Power Electronics (IICPE)

作者： Prakash Kuppuswamy Sayed QY Al Khalidi Purushottam Kumar Ahmed Ali Shaik Meeran Sonia Vijaya Varshini Computer Science Engineering SRM University Delhi-NCR Haryana India Department of Information Technology King Khalid University Abha KSA Department of Information Technology Jazan University Jazan KSA Computer Science Deparment Bharathiyar University Coimbatore Tamil Nadu India

Grid computing has emerged as a powerful paradigm for harnessing distributed computing resources to solve complex problems. In grid computing, various devices across different administrative domains collaborate to provide computational power and storage capacity. Sensor agent devices have become the backbone of modern technological systems such as Internet of Things (IoT), smart homes, industrial automation, and autonomous vehicles. These devices collect and process real-time data from the surrounding environment, providing crucial insights for decision-making, monitoring, and control purposes. However, the performance of these devices depends on space complexity and time complexity. Achieving optimum performance in sensor agent devices requires a delicate balance between space complexity and time complexity. Reducing one complexity may increase the other, and vice versa. Light weight authentication device-based algorithms play a vital role in verifying the identity of devices and ensuring secure communication within the grid and that needs to satisfy complexity. These protocols verify the identity of devices and users, ensuring that only authorized entities can access and interact with the data. In this article, we will explore a new light weight authentication protocol based on a block cipher i.e ELBC (Extended Linear Block cipher algorithm). The new authentication protocols are easy to implement and deploy in any kind of environment. The algorithm divides the authentication message into blocks and encrypts them using matrix multiplication. By generating an invertible key matrix and applying encryption and decryption equations, the proposed algorithm ensures secure communication.

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Fuzzy random proof of work for consensus algorithm in blockchain

Fuzzy random proof of work for consensus algorithm in blockc...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Kumar, Akhilesh Kareen, Z.H. Mudhafar, Mustafa Arnone, Gioia Umaralievich, Mekhmonov Sultonali Bhowmick, Avijit PG Department of Information Technology Gaya College Bihar Gaya India Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Department of Anesthesia and Health Care Faculty of Altuff College University Iraq Disaq - Università Degli Studi Di Napoli "Parthenope" Italy Tashkent Institute of Finance Tashkent Uzbekistan Deparment of Computer Science and Engineering BBIT Kolkata India

ISBN: (纸本)9781032669663

The consensus method, being one of the most important aspects of blockchain, differs depending on the sector. The commonly used proof-of-work (PoW) consensus technique for public chain application scenarios still has challenges that are difficult to handle, such as security and high computer power. As a result, the PoW method is investigated in terms of enlarging the solution space and improving the adjustment mechanism. A consensus technique based on fuzzy random proof of work (FRMH) is presented. The FRMH algorithm improves the security of the blockchain consensus mechanism by increasing the solution space of the consensus algorithm by incorporating technologies such as a fuzzy transitive closure matrix in fuzzy mathematics. In addition, the FRMH algorithm uses a dual adjustment method to cope with machines with high computational power and thus solves the issue of high computing power being difficult to regulate on the blockchain. Through mathematics, it has been shown that the FRMH algorithm has greatly enhanced solution space and greater processing power control. © 2024 The Author(s).

关键词： Computing power

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The Role of Bio Informatics in Identifying Cancer Informatics

The Role of Bio Informatics in Identifying Cancer Informatic...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Harshada Gole Subodh Daronde Deparment of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Enginering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

With the advent of bioinformatics, cancer research has entered a new age wherein vast amounts of complex biological data can be interpreted to create powerful treatments. This approach has exceptionally improved the identification of biomarkers, deciphering hidden patterns contained within data, and developing personalized treatment modalities by incorporating bioinformatics-based strategies with artificial intelligence and machine learning. Advances in bioinformatics will soon give insight into the cancer process at cellular levels and offer a better understanding of tumor heterogeneity and evolution. Increased reliance on bioinformatics has directly affected the development of newer immunotherapy forms. In the last year, there have been 13 new FDA-approved immunotherapy treatments for 10 different drugs and 11 types of cancers specific to each one. The future of bioinformatics in cancer research is bright, and the field is likely to be further revolutionized with the continued progress in artificial intelligence, machine learning, and single-cell analysis techniques

关键词： Protocols Precision medicine Immunotherapy Machine learning Proteomics Biomarkers Bioinformatics Prognostics and health management Cancer Tumors

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Social Service Robot using Gesture recognition technique 4

Social Service Robot using Gesture recognition technique

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4th National Conference on Communication Systems, NCOCS 2022

作者： Jessintha, D. Kumar, P. Praveen Jaisiva, S. Kumar, T. Ananth Ananth, Christo Electronics and Communication Engineering Easwari Engineering College Chennai India Deparment of Information Technology Sri Manakulavinayagar Engineering College Puducherry India Department of EEE M.Kumarasamy College of Engineering Tamilnadu Karur India Computer Science and Engineering IFET College of Engineering Tamilnadu India Department of Natural and Exact Sciences Samarkand State University Uzbekistan

A robot is a machine that can automatically do a task or a series of tasks based on its programming and environment. They are artificially built machines or devices that can perform activities with utmost accuracy and precision minimizing time constraints. Service robots are technologically advanced machines deployed to service and maintain certain activities. Research findings convey the essential fact that serving robots are now being deployed worldwide. Social robotics is one such field that heavily involves an interaction between humans and an artificially built machine. These man-built machines interact with humans and can also understand social terms and words. Modernization has bought changes in design and mechanisms due to this ever-lasting growth in technology and innovation. Therefore, food industries are also dynamically adapting to the new changes in the field of automation to reduce human workload and increase the quality of service. Deployment of a robot in the food industries which help to aid deaf and mute people who face social constraints is an ever-growing challenge faced by engineers for the last few decades. Moreover, a contactless form of speedy service system which accomplishes its task with at most precision and reduced complexity is a feat yet to be perfected. Preservation of personal hygiene, a better quality of service, and reduced labour costs is achieved. © Published under licence by IOP Publishing Ltd.

关键词： Quality of service

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Enhanced Seizure Prediction Using XGBoost and EEG Data: A Machine Learning Approach with increasing Model Metrics

Enhanced Seizure Prediction Using XGBoost and EEG Data: A Ma...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Sanghamitra Panda Sushree Sasmita Dash Rakesh Pandey Swapnajit Sahoo Annimesh Sasmal Debasish Swapnesh Kumar Nayak Department of Computer Science and Engineering Centurion University of Technology and management Bhubaneswar Odisha India Deparment of CSE KIIT University Bhubaneswar India Department of AI & DS IIMT College of Engineering Greater Noida India School of Computer Science and Engineering IILM University Greater Noida Uttar Pradesh INDIA

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Epilepsy is a neurological disorder marked by abnormal brain activity. Normally, brain cells (neurons) communicate with each other through electrical signals. In epilepsy, these signals become disrupted, leading to excessive and synchronized firing, resulting in seizures. But, if it can be predicted early, then it may be prevented or made to progress slowly. For prediction, machine learning has played a great role nowadays. In this study, we have used models like Random Forest, Decision Tree, Extra-trees classifier, Gradient Boost (XGBoost) has given better result for prediction. In this paper we have taken total 500 individuals EEG data with each has 4097 data points. The EEG scans provided detailed images of brain structures, allowing for precise analysis of epileptic activity using EEG data. The prediction of seizures has proven to be a formidable challenge, with existing studies failing to yield consistently accurate results. Our methodology involved a 60/40 train-test split and again 20/20 for test-validation split ensuring the model was trained on the majority of the data. By utilizing the XGBoost, we achieved an accuracy of 98%, demonstrating its effectiveness in handling complex spatial data inherent in EEG scans. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. This localization capability represents a significant advancement in epilepsy research, enabling more targeted treatment strategies. The researches already performed on prediction of Epilepsy disease has given poor performance of 95% classification accuracy in comparison to our model.

关键词： Location awareness Surveys Accuracy Epilepsy Predictive models Brain modeling Electroencephalography Data models Spatial databases Synchronization

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Generalized Leverage Scores: Geometric Interpretation and Applications

arXiv

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arXiv 2022年

作者： Ordozgoiti, Bruno Matakos, Antonis Gionis, Aristides School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Deparment of Computer Science Aalto University Finland Division of Theoretical Computer Science KTH Royal Institute of Technology Sweden

In problems involving matrix computations, the concept of leverage has found a large number of applications. In particular, leverage scores, which relate the columns of a matrix to the subspaces spanned by its leading singular vectors, are helpful in revealing column subsets to approximately factorize a matrix with quality guarantees. As such, they provide a solid foundation for a variety of machine-learning methods. In this paper we extend the definition of leverage scores to relate the columns of a matrix to arbitrary subsets of singular vectors. We establish a precise connection between column and singular-vector subsets, by relating the concepts of leverage scores and principal angles between subspaces. We employ this result to design approximation algorithms with provable guarantees for two well-known problems: generalized column subset selection and sparse canonical correlation analysis. We run numerical experiments to provide further insight on the proposed methods. The novel bounds we derive improve our understanding of fundamental concepts in matrix approximations. In addition, our insights may serve as building blocks for further contributions. © 2022, CC BY.

关键词： Approximation algorithms

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