A comprehensive analysis was performed using the AIDS Clinical Trials Group 175 dataset to improve the accuracy of predicting AIDS disease progression. The primary objective was to integrate machine learning technique...
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ISBN:
(数字)9798350367904
ISBN:
(纸本)9798350367911
A comprehensive analysis was performed using the AIDS Clinical Trials Group 175 dataset to improve the accuracy of predicting AIDS disease progression. The primary objective was to integrate machine learning techniques to predict AIDS disease outcomes based on clinical, demographic, and treatmentrelated variables. Several machine learning models, including advanced neural network architectures, were developed and thoroughly evaluated. The study highlighted the ability of machine learning to accurately identify patterns and risk factors associated with AIDS disease progression, thereby improving treatment strategies and patient management. Extensive comparisons of several machine learning models were performed to evaluate their performance and robustness. The aim is to demonstrate the important role of artificial intelligence in predicting and diagnosing AIDS early, thereby contributing to better healthcare outcomes. The comprehensive evaluation of model performance in this study is expected to support future advances in prediction models for AIDS and other chronic diseases.
Skin cancer is a dangerous and widespread conditionthat requires early and accurate detection for effective treatment. Recent advancements in deep learning have demonstrated promise in the detection of skin cancer fro...
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ISBN:
(数字)9798350394474
ISBN:
(纸本)9798350394481
Skin cancer is a dangerous and widespread conditionthat requires early and accurate detection for effective treatment. Recent advancements in deep learning have demonstrated promise in the detection of skin cancer from image datasets. This research aims to analyze the effectiveness of different models in detecting skin cancer, including DenseNet, CNN, and ResNet. This study evaluates the metrics like accuracy, precision, recall, and F1-score in identifying skin cancer. Additionally, this study investigates the important features in the images that lead to the model prediction using Explainable AI - LIME and SHAP. The ultimate aim is to discover clever and accurate methods for identifying skin cancer early. This helps patients get treatment quickly when it matters most.
Diabetic retinopathy(DR) has become a major issue among ophthalmologists worldwide, with a majority of type 2 diabetes patients suffering from this disease. Currently, medical diagnosis is primarily performed through ...
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ISBN:
(数字)9798350354171
ISBN:
(纸本)9798350354188
Diabetic retinopathy(DR) has become a major issue among ophthalmologists worldwide, with a majority of type 2 diabetes patients suffering from this disease. Currently, medical diagnosis is primarily performed through manual examination using an ophthalmoscope, which requires trained doctors, or other imaging devices. The scarcity of experts and the large population affected by chronic diabetes highlight the need for automated diagnostic tools—software that can provide accurate diagnostics and work effectively even with limited data. The study leveraged a pre-trained EfficientNetB0 model, which was fine-tuned with DR-specific data to address the global issue of DR. Due to a limited dataset, advanced data augmentation techniques were implemented to enhance the model’s robustness. Additionally, a customized dense layer was integrated for the precise classification of DR from levels 0 to 4. The use of Canny edge-based detection accurately segmented retinal vascular blood vessels, resulting in significantly improved classification accuracy. The methodology achieved an impressive accuracy of 87.73% across DR stages and an outstanding $95.77 \%$ precision for the ‘No-DR’ class, highlighting high recall metrics for each category. This work effectively integrated state-of-the-art data augmentation and edge detection techniques into a unified system, providing a superior approach for diagnosing eye diseases such as DR that may surpass current automation standards.
In the current educational landscape, the transition towards digitalization has become crucial. However, the manual entry of data from traditional physical marksheets into digital systems remains a significant bottlen...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
In the current educational landscape, the transition towards digitalization has become crucial. However, the manual entry of data from traditional physical marksheets into digital systems remains a significant bottleneck in the process. Educators often spend significant amount of time and effort entering data into electronic systems, which could be better utilized for more productive *** this paper, we offer a solution to this problem. We propose an automated system that leverages Optical Character Recognition (OCR) Technology to streamline the extraction of essential information from marksheets. We discuss about the Dataset, preprocessing on the input image, Multi Modal OCR system Structure and other implementation details. We conclude the paper by a comparative study of our Multi Modal OCR system with other Commercially available OCRs like EasyOCR, PaddleOCR and PyTesseract.
The ambitious task of implementing the popular Temple Run game in a new approach that is used to create a version of the Temple Run game that is represented in Non-Deterministic Finite Automata (NFA) and Deterministic...
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ISBN:
(数字)9798350364569
ISBN:
(纸本)9798350364576
The ambitious task of implementing the popular Temple Run game in a new approach that is used to create a version of the Temple Run game that is represented in Non-Deterministic Finite Automata (NFA) and Deterministic Finite Automata (DFA). Temple Run, celebrated for its dynamic gameplay and constantly changing obstacles, offers an ideal platform for these automata models in gaming mechanics. The initiative focuses on using Non-Deterministic Finite Automata to generate diverse and evolving pathways within the game environment, ensuring players encounter a variety of challenges and opportunities. Conversely, Deterministic Finite Automata will regulate the behavior of in game elements like obstacles, abilities, and enemies, establishing clear rules and patterns for their movements and interactions. Through the integration of both NFA and DFA models, an immersive Temple Run experience is aimed to be created, seamlessly combining unpredictability with structured gameplay, ensuring each gaming session is both engaging and rewarding for players.
The safety of kids who play outside while travelling in rural areas is a big concern. Safecomm’s new technical fix combines LoRa, NFC, as well as UVC cam systems. Thereby, the gadget has been engineered by merging Lo...
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ISBN:
(数字)9798350388916
ISBN:
(纸本)9798350388923
The safety of kids who play outside while travelling in rural areas is a big concern. Safecomm’s new technical fix combines LoRa, NFC, as well as UVC cam systems. Thereby, the gadget has been engineered by merging LoRa modules, NFC tags, GSM module and ESP cams for real-time tracking of where their children are and instant alerting their parents or schools. Besides, when there is an emergency, the wearer can choose to press the emergency button for the SOS family contacts and the emergency services organisations to be alerted in case the battery level is still good. If required, images can also be captured and saved on the cloud storage. This IoT product leverages LoRa, NFC, and an UVC cam to cater for safety issues relating to modes of transport for rural schools. Every day when children mobile around, this machine secures them. The attendance is made easy and quick response in cases of emergency is also possible through this technology so the risks are averted. This device ensures that children are safe when they move about every day. These technologies make keeping track of attendance easier and being able to quickly respond during emergencies so that dangers are mitigated, therefore parents as well as teachers can be less worried. This undertaking is seen as one important stride toward using internet of things (IoT) technology for ensuring children from outside urban areas are safe and boosting safety inside schools or while commuting using linked transportation means.
Smoking and excessive alcohol consumption remain pressing public health issues. This research aimed to develop Machine Learning (ML) models leveraging sensor and biomarker data to predict smoking and drinking behavior...
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ISBN:
(数字)9798350395808
ISBN:
(纸本)9798350395815
Smoking and excessive alcohol consumption remain pressing public health issues. This research aimed to develop Machine Learning (ML) models leveraging sensor and biomarker data to predict smoking and drinking behaviors for targeted interventions. The Smoking and Drinking dataset (sourced from Kaggle) created from wearable sensor readings, blood tests, and lifestyle self-reports is used to train the 10 Machine Learning (ML) algorithms and validated for smoking and drinking prediction. The Random Forest model outperformed others, reaching an accuracy of 79.65% in predicting smoking, while the XGBoost model achieved a notable 73.96% accuracy in predicting drinking status. Overall, the ML models demonstrate strong capabilities for real-time smoking and drinking prediction using biological data. Such predictive analytics hold promise for early risk detection, personalized care, and nuanced interventions by healthcare systems. This research investigated ML algorithms to uncover and analyze patterns in lifestyle behaviors and associated health outcomes. Additionally, this work unlocks the Blackbox nature of ML models using Explainable AI tools “SHAP (SHapley Additive exPlanations)” and “LIME (Local interpretable model-agnostic explanations)”.
In the emerging field of aquaculture, the necessity for precise environmental monitoring is paramount for the sustainability and profitability of shrimp farming. This study explores the deployment of an IoT-based syst...
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ISBN:
(数字)9798331505462
ISBN:
(纸本)9798331505479
In the emerging field of aquaculture, the necessity for precise environmental monitoring is paramount for the sustainability and profitability of shrimp farming. This study explores the deployment of an IoT-based system to rigorously monitor water quality parameters such as temperature, pH, turbidity, and total dissolved solids in shrimp tanks. Employing a suite of sensors integrated with an ESP32 microcontroller and real-time data transmission to the Blynk platform, the methodology allows for continuous monitoring and immediate adjustment of tank conditions to minimize loss and optimize yield. The study highlights significant challenges such as high stocking densities, manual feeding practices that lead to growth irregularities, and the impact of erratic power supply and fixed market prices on operational costs. The results demonstrate that our advanced IoT solution not only enhances operational efficiency and shrimp health but also contributes to reducing resource wastage and increasing shrimp yield, thereby supporting the argument that technology-driven approaches can substantially improve the stability and control of farming conditions, fostering sustainable development in aquaculture.
Poor air quality due to industrialization and urbanization causes respiratory problems and health concerns. Nearly 6.67 million people had died due to air pollution globally. To reduce we need to bring a new regulatio...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Poor air quality due to industrialization and urbanization causes respiratory problems and health concerns. Nearly 6.67 million people had died due to air pollution globally. To reduce we need to bring a new regulations as well as device for air pollutant purification. The proposed system has the stability of purification. Continuous monitoring and filtering are essential. This system integrates sensors and filters to provide real-time air quality data, using machine learning for accuracy. SMS alerts provide early warnings for timely action, increasing personal health awareness and regulatory compliance. Overall, it promotes better settings by reducing the negative effects of air pollution on public health and the environment.
Facial emotion recognition is a critical component in the development of advanced human-computer interaction systems, with applications spanning security, healthcare, and social robotics. This study explores the effic...
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ISBN:
(数字)9798350376685
ISBN:
(纸本)9798350376692
Facial emotion recognition is a critical component in the development of advanced human-computer interaction systems, with applications spanning security, healthcare, and social robotics. This study explores the efficacy of advanced deep learning models, specifically EfficientNetB5 model in identifying emotions from facial images. Leveraging with augmented data, our approach integrates attention mechanisms, batch normalization, convolutional layers, global average pooling layers, and dropout techniques to enhance model performance. The proposed model demonstrated significant performance accuracy of 87.5%, highlighting the importance of emphasizing relevant image regions for better emotion detection. Additionally, we conducted a comparative analysis with other prominent deep learning architectures, including AlexNet, XceptionNet, ResNet, MobileNet and EfficientNetV2M and proved the superiority of the proposed model against them.
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