Water pollution, driven by industrialization and urbanization, threatens public health, aquatic ecosystems, and livelihoods worldwide. Rivers, being the primary source of water for drinking, agriculture, and industry,...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
Water pollution, driven by industrialization and urbanization, threatens public health, aquatic ecosystems, and livelihoods worldwide. Rivers, being the primary source of water for drinking, agriculture, and industry, are highly vulnerable to contamination from untreated sewage, industrial discharge, and agricultural runoff. Traditional water quality monitoring methods are expensive, time-consuming, and lack real-time *** study proposes a technology-driven water quality monitoring system capable of processing large data volumes in real-time, identifying pollution patterns, and supporting timely interventions. By integrating advanced monitoring techniques, the system enhances decision-making for water management, ensuring sustainability and ecological balance. The research highlights the critical role of technology in addressing global water pollution balance, further underlining the very important linkage between technology and *** data volumes from source water can be processed by this system, identifying patterns and trends that indicate fluctuation in water quality. this system is particularly beneficial in areas that experience seasonal and man-made causes of change in water quality. Besides its technological achievements, this project is a giant leap toward the management of water *** technology provides authority to decision makers. who together with environmentalists and enables easy accessibility for various tasks to make sensible decisions for drinking water supply as well as irrigation systems and pollution prevention measures for water sources. measures. It has great importance as it combines science and technology in saving vital natural resources and maintaining long-term ecological balance by tackling one of the most urgent worldwide environmental issues.
Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense *** watermarking can be used to protect the copyright of digital media by embedding a uniqu...
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Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense *** watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the *** watermarking can also be used to verify the authenticity of digital media,such as images or videos,by ascertaining the watermark *** this paper,a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform(DWT),chaotic map,and Laplacian *** DWT can be used to decompose the image into its frequency components,chaos is used to provide extra security defense by encrypting the watermark signal,and the Laplacian operator with optimization is applied to the mid-frequency bands to find the sharp areas in the *** mid-frequency bands are used to embed the watermarks by modifying the coefficients in these *** mid-sub-band maintains the invisible property of the watermark,and chaos combined with the second-order derivative Laplacian is vulnerable to *** experiments demonstrate that this approach is effective for common signal processing attacks,i.e.,compression,noise addition,and ***,this approach also maintains image quality through peak signal-to-noise ratio(PSNR)and structural similarity index metrics(SSIM).The highest achieved PSNR and SSIM values are 55.4 dB and *** the same way,normalized correlation(NC)values are almost 10%–20%higher than comparative *** results support assistance in copyright protection in multimedia content.
Each business acknowledges the importance of employees in building and sustaining competitive advantage. The turnover of employees should, therefore, be seen as something that negatively affects business growth. It is...
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ISBN:
(数字)9798331535193
ISBN:
(纸本)9798331535209
Each business acknowledges the importance of employees in building and sustaining competitive advantage. The turnover of employees should, therefore, be seen as something that negatively affects business growth. It is in this process that sound decisions are made regarding management. Its role may very well be of utmost importance in any planning prepared. Worker turnover has been a known factor, and decisions in management call for sound judgment to retain better employees. Today's firms have a significant interest in finding the causes and circumstances behind employee turnover to enable its reduction. Some causes contribute to instability. In this manner, predicting the likelihood of employee turnover and primarily identifying causes of turnover are vital organizational objectives to optimize HR techniques. Interests, artificial intelligence (ai), machine learning, and deep learning have been actively applied to anticipate instability likelihood using automated techniques. This paper aims to utilize machine learning and deep learning models and compare them to attain the highest possible accuracy. We intend to achieve an accuracy of 94%, which is higher than the previous best of 92%, using the ANN and CNN algorithms.
In the financial world, it is impoartant to have a sound understand of systems against fraud, as they help detect activities such as anomalous and malicious transactions, through the analysis of activity engagement. D...
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ISBN:
(数字)9798331512088
ISBN:
(纸本)9798331512095
In the financial world, it is impoartant to have a sound understand of systems against fraud, as they help detect activities such as anomalous and malicious transactions, through the analysis of activity engagement. Decision Tree are the recent form of deep learning models which, in more recent years, have shown advanced capability in the identification of fraudulent transactions, however, sensitivity and accuracy when implemented in real time systems remain an issue. In this research, we provide a new idea of fraud detection of UPI transactions that utilizes a Decision Tree based sequence modeling framework. The model focuses on normalization sets and typical cardholder activities to effectively learn how to detect anomalies in UPI transactions. It then analyzes the sequences of a person’s activities and adds on the probabilities, to then classify if the transaction is fraudulent or not. Several measures are established, however, in order to aid in minimizing false positives, because the aim is to ensure valid transactions are still processed. The experimental results support the proposed fraud detection system’s potential usefulness in monitoring actual UPI transactions and it also shows how it outperforms current systems in related studies. Our model facilitates the UPI transaction environment equally and addresses the fine balance in detection efficiency, accuracy and transaction speed by proposing the integration of deep learning techniques.
Food allergies pose a significant health risk, affecting millions worldwide and potentially leading to severe reactions, including anaphylaxis. Identifying allergens in food products is crucial for individuals with al...
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ISBN:
(数字)9798331537579
ISBN:
(纸本)9798331537586
Food allergies pose a significant health risk, affecting millions worldwide and potentially leading to severe reactions, including anaphylaxis. Identifying allergens in food products is crucial for individuals with allergies. However, the increasing complexity of food labeling and hidden allergens makes this process challenging, time-consuming, and error-prone. This paper presents the ai-powered Ingredient Detector for Allergies, an innovative application designed to streamline allergen detection using Artificial Intelligence (ai). The system enables users to input a food item, scan a barcode, or upload an image for ingredient analysis. By leveraging Natural Language Processing (NLP) and computer vision, it accurately identifies potential allergens and provides real-time alerts. This application is integrated with a comprehensive allergen database, which ensures precise detection and enhances food safety. By offering a user-friendly, intelligent, and efficient solution, this ai-driven system empowers individuals with food allergies to make informed dietary choices confidently and efficiently.
The Industrial Internet of Things' (IIoT) explosive growth has significantly changed industrial environments by linking smart devices via Supervisory Control and Data Acquisition (SCADA) systems. This integration ...
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ISBN:
(数字)9798331512088
ISBN:
(纸本)9798331512095
The Industrial Internet of Things' (IIoT) explosive growth has significantly changed industrial environments by linking smart devices via Supervisory Control and Data Acquisition (SCADA) systems. This integration also introduces significant cyber security vulnerabilities even though it brings about flexibility, resource efficiency, and operational agility. Current IDS using traditional machine learning fail to classify cyber attacks precisely because of complexity in data, limited availability, and mislabeling issues. To overcome these challenges, this study presents a scalable and effective ensemble detection framework that utilizes Pyramidal Recurrent Units (PRUs) and Decision Tree (DT) models. This framework aims to detect and counter cyber attacks across large IIoT networks, especially in SCADA-based environments. The propose of developing this project is to ensure scalable and efficient Deep Learning and Decision Tree based ensemble cyber attack detection framework to resolve trustworthiness issues in the SCADA based IIoT networks. Our proposed detection method can be applied to various IIoT domains. It is easy to implement and deploy, improving efficiency and accuracy while addressing the limitations of earlier *** framework enhances the security of SCADA-based IIoT systems, making industrial networks more reliable, trustworthy, and resilient. The results indicates that the system performs well across different network settings, showcasing its adaptability and robustness in detecting IIoT-based SCADA systems.
The heart is a very important organ in the circulatory system. Heart disease is extremely severe and can even lead to death, therefore it is essential to make an accurate and timely diagnosis. However, health data ana...
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ISBN:
(数字)9798331515720
ISBN:
(纸本)9798331515737
The heart is a very important organ in the circulatory system. Heart disease is extremely severe and can even lead to death, therefore it is essential to make an accurate and timely diagnosis. However, health data analysis may be complicated because there may not be any evident patterns. Cardiovascular diseases (CVDs) are the number one cause of death throughout the world. Early detection of them is vital for successful management. For this purpose, electrocardiograms (ECGs) are used which indicate electrical activities in our hearts; however, computers and machine learning systems are ways by which we could analyze patients’ data more swiftly. It concentrates on using deep as well as machine learning techniques in assisting diagnosing diseases. The purpose was to evaluate how well deep learning could identify four significant heart conditions: arrhythmia, myocardial infarction or heart attacks, history of myocardial infractions (past heart attacks), and normal patterns. We utilized a real-world ECG dataset reflecting actual scenarios and complexities. The Proposed model performed even better than the existing ones with an accuracy of 98.23% implying that it could correctly identify heart issues in ECG images 98.23% times out of 100%. Also recall, precision and F1 score were very high which are other measures about how good a model is performing. By using Machine Learning & Deep learning Algorithms like Support Vector Machine, Efficient Net, Residual Neural Network, and *** Algorithms created their own deep learning model specifically for predicting heart problems.
In the era of data ubiquity, safeguarding privacy while harnessing the power of real-time streaming presents a critical challenge. This study delves into the intersection of privacy preservation, real-time streaming, ...
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Machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In th...
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Sleep disorders are now a prevalent health condition worldwide and are identified as contributory causes of several chronic diseases like cardiovascular diseases, obesity, and reduced intellectual functions. Intempera...
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ISBN:
(数字)9798331535193
ISBN:
(纸本)9798331535209
Sleep disorders are now a prevalent health condition worldwide and are identified as contributory causes of several chronic diseases like cardiovascular diseases, obesity, and reduced intellectual functions. Intemperate rates of sleep disorders such as insomnia and sleep apnea are shown to be related to abnormal sleeping hours, unsatisfactory lifestyles, and demographic variables. Here, we propose a machine learning method to predict sleep disorders with high accuracy on the Sleep Health and Lifestyle dataset, using key features like age, gender, BMI, physical activity, occupational type, sleep duration, and quality. We employ four supervised machine learning models: Logistic Regression, XGBoost, Decision Tree (DT), and Support Vector Machine (SVM). Data preprocessing techniques such as handling missing values, normalization, and label encoding are conducted to ensure data quality. Feature importance analysis is also performed to identify influential attributes such as BMI, blood pressure, and sleep quality in the prediction of sleep disorders. The measurements of precision, accuracy, recall, and F1-score are utilized to evaluate the models' performance. Hyperparameter tuning techniques are used for maximizing model performance. Our results indicate that these machine learning models are capable of classifying sleep disorders with high accuracy, with XGBoost and SVM demonstrating greater prediction accuracy. This study shows how machine learning technology can be utilized in medicine to analyze and treat sleep disorders early, helping medical professionals make better decisions and enhancing patient outcomes. The addition of genetic algorithm-based optimization methods further boosts classification accuracy by optimizing the parameters of the model. Comparative studies find the dominance of ensemble methods over traditional classifiers in solving high-dimensional healthcare data. Additionally, the system's ability to include variable lifestyle-related factors presen
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