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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics 7

Utilizing Random Forests for High-Accuracy Classification in...

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

作者： Maindola, Meenakshi Al-Fatlawy, Ramy Riad Kumar, Rakesh Boob, Nandini Shirish Sreeja, S.P. Sirisha, N. Srivastava, Abhinav Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering & Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Master of Computer Application Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

In recent years, medical diagnostics has increasingly relied on machine learning techniques to improve accuracy and efficiency. Among these, the Random Forest algorithm has emerged as a powerful tool for classification tasks due to its robustness and ability to handle complex datasets. This paper explores the application of Random Forests in medical diagnostics, specifically focusing on enhancing the accuracy of disease classification. By analyzing large, heterogeneous medical datasets, we demonstrate how Random Forests can outperform traditional classification algorithms, offering high precision in identifying and classifying diseases. Key features, such as decision tree ensemble methods and bootstrap aggregation, are utilized to minimize overfitting and improve model generalization. The research compares Random Forests to other machine learning models, such as Neural Networks and Support Vector Machines, and finds that Random Forests perform better in terms of dependability and classification accuracy. We further emphasise that the technique can scale to big datasets and that it can manage missing data. The findings show that Random Forests might be a useful tool for accurate and early diagnosis when incorporated into clinical procedures. The findings of this study highlight the promise of machine learning, and Random Forests in particular, to revolutionise healthcare diagnostics and enhance patient results. © 2024 IEEE.

关键词： Random forests

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A Mobile App for Seed Grading by Incorporating Deep Learning Approach with Bio Inspired Optimization Technique

A Mobile App for Seed Grading by Incorporating Deep Learning...

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International Conference on Computation System and Information technology for Sustainable Solutions (CSITSS)

作者： Monica R. Mundada Sowmya B. J G Anurag G M Mani Satya Narayana Department of Master of Computer Applications Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India Department of Computer Science Ramaiah Institute of Technology Bengaluru India

One of the main production stumbling blocks that hinders the growth of healthy crops is seed quality. The ability to cultivate more nutrient-dense, higher-yielding crops that can help feed a growing population is made possible by high-quality seeds, which are crucial for ensuring food security. As a result, it is crucial to quickly and accurately identify high-quality seeds. Manually identifying the type of wheat requires skill and time. It gets difficult to manually differentiate seeds from an array when they all seem so identical. This prompted us to develop an app that would automate the process of determining seed quality. With the corn seed data set, a convolutional neural network is trained. By eliminating the thick layers, the characteristics of this neural network are recovered. The moth flame optimization technique, a bio-inspired algorithm, is used to reduce noise and extraneous data. The KNN classifier is used to assess the fitness of the features that have been chosen. Lastly, the KNN classifier is used to categorize the seeds based on the fewest attributes

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To understand the relationship between Machine learning and Artificial intelligence in large and diversified business organisations

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Materials Today: Proceedings 2022年 56卷 2082-2086页

作者： Sandeep, S.R. Ahamad, Shahanawaj Saxena, Divya Srivastava, Kingshuk Jaiswal, Sushma Bora, Ashim Department of Electrical and Electronics Engineering SJB Institute of Technology Karnataka Bengaluru India Affiliated to Visvesvaraya Technological University Karnataka Belagavi590018 India College of Computer Science and Engineering University of Hail Saudi Arabia Master of Science in Computer Science University of Texas at Arlington ArlingtonTX76019 United States Department of Informatics School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India Koni CG Bilaspur India Department of Mathematics. Diphu Government College Assam Diphu India

In the modern era, Machine learning and Artificial intelligence remain the most remarkable IT application, a technology that has seen unprecedented progress in recent decades. AI and machine learning are all closely linked with using a computer to simulate intelligent behaviour with little human interaction. It has been noticed that many large organisations are implementing technologies such as ML and AI to improve their performance and productivity in the business. Companies can create better, more customised and engaging campaigns as AI becomes increasingly adept at collecting massive data faster than ever before. As AI and ML technologies become more widely used, being a technology-powered company will undoubtedly be a must for survival. AI and ML are guiding on everything from production to delivering products and services to customers. Machine translation, chatbots, and self-learning algorithms are examples of AI technology that may help people better comprehend their surroundings and respond appropriately. Organisations have been embracing artificial intelligence technology advancements to create and maximising their strategic and competitive advantages. Hence, businesses rely heavily on AI to enhance their performance and develop new services to boost productivity and generate new offerings. In addition, they are fundamentally reshaping companies' business and operation processes to better meet customers’ requirements and expectations, resulting in increased efficiency. Machine learning and artificial intelligence are broad concepts that will be examined further in this article. The report aims to understand the relationship between Machine Learning and Artificial Intelligence in large and diversified business organisations. The researcher used a study methodology oriented toward a more inclusive and comprehensive approach to better account for the intangible aids of ML and AI within organisations. © 2021

关键词： Efficiency

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An Overview of Video Tampering Detection Techniques: State-of-the-Art and Future Directions

An Overview of Video Tampering Detection Techniques: State-o...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Pandey, Saroj Kumar Kumar, Lalan Kumar, Gaurav Kumar, Ankit Singh, Kamred Udham Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

In the realm of video processing and forensics, finding video tampering is a critical challenge. The purposeful change or manipulation of video material to conceal or falsely depict the original information is referred to as 'video tampering.' A significant demand for efficient video tampering detection methods has emerged in recent years as a result of the rise in false news, cyberbullying, and other types of criminal behavior. Modern approaches for detecting video manipulation may be generally divided into two categories: passive methods and aggressive methods. Whereas active methods entail putting digital watermarks or other identifiers on the video clip to aid tampering detection, passive methods rely on evaluating the video content for inconsistencies or artifacts generated during tampering. Techniques like sensor noise analysis, splicing detection, and forgery detection based on compression artifacts are examples of passive approaches. To find any discrepancies produced by manipulation, sensor noise analysis examines the noise patterns in the video. Splicing detection entails identifying the borders between several video segments and inspecting the content of each segment for irregularities. Analyzing the compressed video stream for irregularities caused during tampering is required for detecting forgeries based on compression artifacts. Techniques like digital watermarking, digital signatures, and fingerprinting are examples of active procedures. To detect any manipulation efforts, digital watermarking entails putting a digital watermark into the video clip. To confirm the legitimacy of the video clip, a digital signature is applied to it. To do fingerprinting, special characteristics from the video footage are extracted and saved in a database. In addition to the aforementioned methods, machine learning-based systems have also demonstrated promising outcomes in the identification of video manipulation. Using these methods, any tampering attempts in fresh mov

关键词： Authentication

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Vision-Based Locomotion Control for Humanoid Robots: A Study on Vision-Guided Walking Strategies

Vision-Based Locomotion Control for Humanoid Robots: A Study...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

ISBN: (纸本)9798350323917

Recently, there has been a lot of research on using vision to direct humanoid robots as they walk. Focusing on the major methodologies, difficulties, and prospects, this review article presents an overview of the current state of study in the vision-guided walking of humanoid robots. In the first part of the study, we introduce vision-guided walking and go through the several methods that have been developed to execute it, such as visual serving, visual odometer, and visual simultaneous localization and mapping. The difficulties of vision-guided walking are then discussed;these include, but are not limited to, the necessity for accurate and strong computer vision algorithms, the robot's need to have a solid awareness of its own body and how it moves, and the need to maintain balance while walking. This paper provides a detailed review of the research on humanoid robots that use vision to direct their gait. Deep learning for vision-guided walking, tactile and proprioceptive sensing combined with vision, and reinforcement learning for adaptive walking are only a few of the issues covered in this overview. Future directions in vision-guided walking research are discussed in this paper. These include the development of more advanced and reliable computer vision algorithms, the incorporation of other modalities like tactile and auditory sensing, and the investigation of new applications for vision-guided walking in fields like healthcare and rehabilitation. © 2023 IEEE.

关键词： Anthropomorphic robots

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Intelligent Road Crack Detection and Analysis Based on Improved YOLOv8

Intelligent Road Crack Detection and Analysis Based on Impro...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Haomin Zuo Zhengyang Li Jiangchuan Gong Zhen Tian School of Electronics and Communication Engineering Sun Yat-Sen University Guangzhou China Master of Science in Computer Science DigiPen Institute of Technology Redmond WA USA Vocational and Technical College Washington University in St. Louis Hebei Normal University Shijiazhuang Hebei China James Watt School of Engineering University of Glasgow Glasgow UK

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

As urbanization speeds up and traffic flow increases, the issue of pavement distress is becoming increasingly pronounced, posing a severe threat to road safety and service life. Traditional methods of pothole detection rely on manual inspection, which is not only inefficient but also costly. This paper proposes an intelligent road crack detection and analysis system, based on the enhanced YOLOv8 deep learning framework. A target segmentation model has been developed through the training of 4029 images, capable of efficiently and accurately recognizing and segmenting crack regions in roads. The model also analyzes the segmented regions to precisely calculate the maximum and minimum widths of cracks and their exact locations. Experimental results indicate that the incorporation of ECA and CBAM attention mechanisms substantially enhances the model's detection accuracy and efficiency, offering a novel solution for road maintenance and safety monitoring.

关键词： Deep learning Training Image segmentation Attention mechanisms Accuracy Target recognition Urban areas Robustness Road safety Safety

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RS-S3VM: Combining Rough Sets with S3VM for Image Classification

RS-S3VM: Combining Rough Sets with S3VM for Image Classifica...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Fariha Tasnim Anannya Chowdhury M. Jamshed Alam Patwary Department of Computer Science & Engineering Chittagong University of Engineering & Technology Master of Information Technology Central Queensland University Australia Institute of Information & Communication Technology Chittagong University of Engineering & Technology

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In the landscape of data mining applications, the difficult task of manually labeling extensive datasets presents formidable challenges owing to its inherent difficulties, costliness, and time consuming nature. To address the limitations associated with manual labeling, semi-supervised learning emerges as a promising paradigm, harnessing the potential of both Data with and without labels in the course of training. SVM based semi-supervised learning (S3VM), one of the several semi-supervised learning approaches, has shown potential in improving performance of classification. This research paper introduces a novel approach that combine rough sets with SVM based Semi-supervised learning for image classification. This innovative methodology involves employing a Convolutional Neural Network (CNN) model to undergo training on labeled data, make predictions on unlabeled data, and subsequently undergo retraining. Feature extraction is then executed for classification. Following this, Rough Set theory based feature selection technique is used, generating a reduct that functions as a rule for SVM. This comprehensive method not only improves the field of image classification but also offering a novel and pragmatic solution to problems in semi-supervised learning. This procedure concludes in an evaluation of the models final out come using experiments on the CIFAR-10 benchmark dataset, where it achieved an impressive accuracy of 97.30%.

关键词： Support vector machines Training Accuracy Rough sets Semisupervised learning Benchmark testing Feature extraction Convolutional neural networks Labeling Image classification

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0

Machine Learning Classifiers for Predicting the Risk of Card...

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Communication, Control and Intelligent Systems (CCIS)

作者： Palak Kumari Ashish Kumar Kakali Chatterjee Master of Computer Applications Patna Women’s College Patna India Dept. of Computer Science and Engineering National Institute of Technology Patna Patna India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body’s, brain’s, or heart’s blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives.

关键词： Support vector machines Accuracy Scalability Machine learning Medical services Predictive models Stroke (medical condition) Prediction algorithms Reliability Blood flow

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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning 3

Predictive Design for Quality Assessment Employing Cloud Com...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jindal, Gaurav Tiwari, Vidhika Mahomad, Riyaz Gehlot, Anita Jindal, Mimoh Bordoloi, Dibyhash G L Bajaj College of Technology and Management Department of Master of Computer Applications Gr. Noida India DPGITM Mechanical Engineering Department Gurugram India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India National Informatics Centre Delhi India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure. © 2023 IEEE.

关键词： Cloud computing

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