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International Conference on Data Analytics and Management, ICDAM 2023

作者： Mahdi, Mohammed A. Ahamad, Shahanawaj Department of Information and Computer Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia

ISBN: (纸本)9789819965465

The convergence of health care and technology has led to the emergence of numerous inventive solutions, among which the Internet of Things (IoT) in tandem with Big Data Analytics and Machine Learning appears to hold significant potential. In the previous research that has been conducted on the Internet of Things (IoT), a variety of big data solutions has been presented for the purpose of evaluating massive amounts of data that have been obtained from a wide variety of sources in the field of smart health care. This evaluation is intended to take place to provide better care for the patients. This has been done with the goal of enhancing the quality of medical treatment offered to patients. Machine learning (ML) is a cutting-edge technology in the industry that has the capacity to analyze huge amounts of data and come to judgements based on those analyses in unique ways. This ability makes ML one of the most important developments in this field in recent years. In this article, we propose to use three distinct machine learning algorithms, each of which serves as a learning engine for big data analytics. Our goal is to investigate how quickly data is processed and how well it can be used to make predictions. Python is used throughout the whole training and testing process, including the simulation that is used to determine whether the model is effective. The findings of the simulation indicate that the suggested technique has a high degree of accuracy in determining the medical histories of patients based on the data provided by IoT devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Internet of things

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Efficient Mobility in Next Generation Heterogeneous Wireless Networks Based on Enhanced Vertical Handoff Approach

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 128-139页

作者： Kaliappan, Vishnu Kumar Padmapriya, T. Manikanthan, S.V. Ilavarasi, S. Suresh Kumar, B. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Arasur Tamil Nadu Coimbatore India Melange Publications Puducherry India Melange Academic Research Associates Puducherry India Tamil Nadu Tiruchengode India Department of Electronics and Communication Engineering The Kavery Engineering College Mecheri Tamil Nadu Salem India

The progression of mobile technologies from first generation (1G) to fifth generation (5G) networks has brought about noteworthy advancement. The present network environment is characterized by the coexistence of various mobile networks and technologies, leading to overlapping or non-overlapping coverage areas, creating a next-generation heterogeneous landscape. Handoffs within the same network (i.e., GSM to GSM or vice versa) are known as horizontal or homogeneous handoffs. Conversely, when handoffs entail a transition to a different network type, such as moving from GSM to CDMA or vice versa, they are categorized as vertical or heterogeneous handoffs. Multiple link triggers, such as link down (LD), link going down (LGD), link detected, and link up, are employed to initiate and execute vertical handoff (VHO) when needed. We have determined the thresholds for vehicular speed and link triggers through extensive simulations, explicitly focusing on high-demand traffic types like video. This paper proposed an enhanced VHO approach based on IEEE 802.21, commonly called media independent handover (MIH), to improve Quality of Service (QoS) parameters, specifically mitigating packet loss (PL), and handoff delay and enhancing the user experience. © 2025 by the authors of this article.

关键词： 5G mobile communication systems

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FarmO'Cart: Multilingual Voice-Assisted Machine Learning Based real-time price Prediction to Enhance Agricultural Income 4

FarmO'Cart: Multilingual Voice-Assisted Machine Learning Bas...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Patel, Aastha Khedikar, Lina Lokakshi, Manasi Khandelwal, Sarika College of Engineering Computer Science Department Maharashtra Nagpur India

ISBN: (纸本)9798350300093

The objective of this work is to propose the use of FarmO'Cart, a cutting-edge online marketing platform, as an effective solution to modernize conventional agricultural trading practices by facilitating an electronic exchange that links farmers, retailers, and consumers. The platform provides an option to directly sell or buy agricultural products without the involvement of any middlemen, thus allowing farmers to benefit from their crop production by generating 15-20% returns and reducing the debt ratio among farmers and their suicide rate. This proposed solution, FarmO'Cart, integrates a range of innovative features designed like a multilingual voice assistant, powered by advanced ALAN AI (Actionable Artificial Intelligence) technology, enabling farmers to interact with the platform in their native language and revolutionize traditional agricultural trading practices. Farmers can put their queries or ask for assistance by simply speaking out in their native languages. The platform is also accessible in 130+ languages through the integration of the Google Translate API (Application Programming Interface), ensuring a truly global reach. These features make the proposed solution more usable for the farmer community who may not be able to understand international languages or English in general. The Bcrypt's hashing algorithm was leveraged to provide enhanced security for user data and passwords and the incorporation of salted hashing and a variable cost factor adds robustness to thwart brute-force attacks and password-cracking attempts. By employing these cryptographic techniques, the platform ensures effective protection of sensitive information. The FarmO'Cart also offers a community platform for farmers to connect and collaborate with Agri-experts & fellow farmers to improve productivity and profitability. © 2023 IEEE.

关键词： Decision trees

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Analysis of Machine Learning Algorithms for Digit and Alphabet Recognition: A Review

Analysis of Machine Learning Algorithms for Digit and Alphab...

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2023 International Conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Juneja, Sonia Srivastva, Shagun Tomer, Sonia Gangwar, Pallavi Tyagi, Yashika Ims Engineering College Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350302820

Human intelligence is incomparable because of its ability to learn and explore. People's ability to make wise decisions at the right time becomes hallmark of intelligence. The concept of machine learning is based on introducing intelligence into machines to improve their decision-making abilities. Recognition of handwritten numbers and alphabets can be counted as machine's intelligent ability to classify. In the proposed review of recognition system, the task is to identify human written characters such as digits and alphabets and convert them into digital text which can be further used for commercial applications like banking and healthcare. Recent research in this area used various methods, classifiers, and functions to make handwritten digits and alphabets easier to recognize. In current research, a comprehensive overview of handwritten digit and alphabet recognition algorithms is presented. Conclusions have been drawn from different machine learning algorithms on the basis of various parameters like accuracy, training, and testing rate. © 2023 IEEE.

关键词： Character recognition

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Text Filtering Insignificant Word (TFIW) Readability Framework Model to Explain NLP in Sentiment Analysis Using Reinforcement Machine Learning 2

Text Filtering Insignificant Word (TFIW) Readability Framewo...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kandakatla, Rajesh Kumar, V Dhilip Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Dept. Computer Science and Engineering Chennai India BVRIT HYDERABAD College of Engineering for Women Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350366570

Reading of a text in a given sentence have lot of parameters to consider for expressing an opinion on a given text to conclude nature of the text. The data which need to be formed in the form of data sets based on specific constrains. The Natural Language Processing (NLP) will always presume that Text readability made as the considerable measure for better round spread of any newspaper. The data related to these documents must be collected and store them in data sets. During this process of measuring text readability at the granular level of the text reading is a cumbersome task. This will always implicate a problem of machine readability of the text and explainability of the text taken from the documents mentioned above. In this context human readability will act an important role in predicting the text reading at granular level where the text-based sentiment analysis have been predicted through a machine learning model. The text reading has been evaluated in order to improve the readability through explainable system. The explainable systems will capture and explain the predictions of text reading. In this technical work an explainable system Text Filtering Insignificant Word (TFIW) is introduced where it improves the textual reading at the granule level based on sentiments by prediction the words which need to be filtered. This will improve the explainability of text and text reading and improve the fast text and explainability of the document. © 2024 IEEE.

关键词： Reinforcement learning

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Visibility restoration for real-world hazy images via improved physical model and Gaussian total variation

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Frontiers of computer science 2024年第1期18卷 267-269页

作者： Chuan LI Enping HU Xinyu ZHANG Hao ZHOU Hailing XIONG Yun LIU College of Computer and Information Science Southwest UniversityChongqing 400715China School of Big Data and Intelligent Engineering Chongqing College of International Business and EconomicsChongqing 401520China College of Artificial Intelligence Southwest UniversityChongqing 400715China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China

1 Introduction Under real-world haze conditions,the existence of haze particles in the atmosphere reduces the visibility of captured ***,the noise is inevitably introduced into the degraded image,which further deteriorates the visual quality of the *** enhance the visibility and quality of outdoor real-world hazy images,numerous algorithms have been proposed to remove haze from a single input *** existing methods are broadly lumped into two categories:prior-based methods[1,2]and learning-based methods[3–6].Unfortunately,the widely used atmospheric scattering model and the corresponding haze removal methods fail to take the noise interference into account,which may result in poor visibility restoration performance.

关键词： visibility remove noise

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A Novel Adjustable Constant-Force Mechanism Based on Spring and Gear Transmission 6th

A Novel Adjustable Constant-Force Mechanism Based on Spring ...

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6th IFToMM International Conference on Mechanisms, Transmissions, and Applications, MeTrApp 2023

作者： Nguyen, Vu Linh College of Engineering and Computer Science VinUniversity Hanoi Viet Nam

ISBN: (纸本)9783031298141

This paper presents a novel adjustable constant-force mechanism (ACFM) based on spring and gear transmission. The mechanism is constructed by combining two gear-spring units and a Sarrus linkage. The significance of this design is that it can provide a constant force, which can be adjustable, over a large displacement. The adjustment of the output force is energy-free through a change in the spring position without spring preload. This work first explains the geometric constraints and desired parameters of the ACFM. Then, a numerical example is provided to illustrate the performance of the mechanism. Moreover, this paper also shows a prototype and experiments with an ACFM. It was found that the ACFM could provide a constant force of up to 20 N within a range of 140 mm, and the force error is less than 6%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Power transmission

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Prediction of Insulin-Dependent T2DM in Patients Using Machine Learning Model 2

Prediction of Insulin-Dependent T2DM in Patients Using Machi...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gowthami, S. Venkata Siva Reddy, R. Ahmed, Mohammed Riyaz School of Electronics and Communication Engineering REVA University Bangalore560064 India HKBK College of Engineering Department of Computer Science and Engineering Bangalore560045 India

ISBN: (纸本)9798350327533

Diabetes, a widespread and enduring chronic ailment, exerts its impact on millions of people across the globe. Individuals with diabetes need regular insulin injections to sustain optimal blood sugar levels. Insulin is indispensable for persons with type 1 diabetes, although persons with type 2 diabetes may require insulin at a later stage. However, the current approaches for suggesting insulin dosage frequently prove inadequate, exhibiting either imprecision or complex procedures. Certain approaches require individuals to bear the responsibility, necessitating the manual calculation of insulin dosage using blood sugar levels and other factors - a complex and prone-to-error undertaking. Some individuals require closed-loop insulin administration devices, which are expensive and include wearing a continuous glucose monitoring (CGM) system. Recent estimates indicate that over 40% of patients who are suffering from (T2DM) in India and Gulf countries are utilizing insulin either on its own or in conjunction with Oral Antidiabetic Drugs (OADs) at any one moment. Lack of sufficient understanding regarding the use of insulin is expected to impact the acceptance, adherence, and effectiveness of therapy. This highlights the urgent requirement to examine the knowledge, and attitude, and suggest the usage of insulin to patients with both juvenile and type 2 diabetes. Insulin therapy is a crucial component of diabetes treatment;individuals with juvenile diabetes and the majority of those with type 2 diabetes will need insulin at some point. Proper injection technique is crucial for achieving glycemic control when using injectable medications such as human insulin, insulin analog, and glucagon-like peptide-l receptor agonists to manage diabetes. The proposed study focuses on determining the subset of individuals with diabetes who are taking both insulin and Oral Antidiabetic Drugs (OAD), and to assess the influence of insulin on glycemic control, ultimately resulting in improved

关键词： Insulin pens Machine learning algorithms Oral Antidiabetic drugs Precision medicine Type 2 Diabetes Mellitus

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A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

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computers, Materials & Continua 2024年第2期78卷 1631-1648页

作者： Lanyao Zhang Shichao Kan Yigang Cen Xiaoling Chen Linna Zhang Yansen Huang School of Mechanical Engineering Guizhou UniversityGuiyang550025China School of Computer Science and Engineering Central South UniversityChangsha410083China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing100044China College of Civil Engineering Guizhou UniversityGuiyang550025China Guizhou Lianjian Civil Engineering Quality Inspection Monitoring Center Co. Ltd.Guiyang550025China

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be ***,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside ***,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature ***,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly *** two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection *** experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior *** the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 ***,it achieves 100%optimal detection performance in five *** the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

关键词： Anomaly detection normalizing flow source domain feature space target domain feature space bidirectional mapping residual network

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An Intelligent Self-Driving Car’s Design and Development, Including Lane Detection Using ROS and Machine Vision Algorithms 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Sujatha, E. Sundar, J. Sathiya Jeba Raju, D. Naveen Lakshminarayanan, S. Suganthi, N. Thandalam Chennai India Department of Computer Science and Engineering Velammal Institute of Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India Sri Sairam Engineering College Chennai India SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

It is challenging to find a solution for lane detection. It has aroused the curiosity of the computer vision field for many years. It has been found that computer vision and machine learning algorithms struggle to tackle the multi-feature identification problem known as lane detection. Even though there are a few different machine learning approaches that may be used for lane identification, these approaches are often employed for classification rather than feature development. On the other hand, contemporary techniques of machine learning may be used to discover features that have a high recognition value, and they have shown success in feature identification tests. These strategies haven’t been applied correctly, which compromises their efficiency and accuracy when it comes to lane recognition. In this study, we provide a fresh approach to solving the problem. A brand-new preprocessing and Region of Interest (ROI) selection method is presented in this article. The major objective is to extract white features by making use of the HSV color transformation, adding preliminary edge feature detection while doing preprocessing, and then selecting ROI based on the preprocessing that was proposed. With the help of this cutting-edge preprocessing strategy, the lane may be found. The integrated autonomous vehicle that we envision is one that is controlled by a Robotic Operating System and that is capable of making intelligent driving choices. The unique filtering and noise reduction techniques that were used on the visual feedback by means of the processing unit served as the basis for the digital image-processing algorithm that was responsible for the greatest performance achieved by the autonomous vehicle. Within the control system, we used two separate control units, one of which was a master and the other of which was a slave. The master control unit is in charge of the visual processing and filtering, while the slave control unit is in charge of the vehicle’s propulsio

关键词： Autonomous vehicles

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