Worldwide, driving when fatigued is still a major factor in traffic accidents. Our solution to this problem is a complete slumbering detection system that uses real-time monitoring, machine learning algorithms, and co...
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Machine Learning is one of the most popular advancements in technology which is being widely used in various domains, including healthcare, avionics, automotive, business, education, etc. A Machine learning approach w...
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ISBN:
(纸本)9798350381689
Machine Learning is one of the most popular advancements in technology which is being widely used in various domains, including healthcare, avionics, automotive, business, education, etc. A Machine learning approach works by learning the required knowledge from the data to be supplied by a client system. In a typical scenario, a client entrusts a third-party agency to develop a machine learning application and shares the data to the developer to enable the development of a Machine learning application. This is badly affecting the privacy of data as a third-party is getting access to sensitive data from a client system. Therefore, an effective data encoding technique is required to ensure the privacy of sensitive data while enabling a third-party agency to develop a Machine learning application on the encoded data. Existing data masking/encoding techniques such as pseudonymization, anonymization and substitution are badly affecting the machine learning process as the modification they do for masking data is preventing a machine learning approach to learn the required knowledge. Another approach known in the literature is Fully Homomorphic Encryption. But, there is no tool available based on this technique which enables a client system to mask sensitive data before giving it to a third-party Machine Learning developer. We failed to obtain the expected outcome from an off-the-shelf Machine Learning classifier when we tried to classify a benchmark dataset masked using an available implementation of Fully Homomorphic Encryption. Since enough details about the implementation is not available, we could not find and fix the issues which is causing this problem. We propose a technique based on which we implemented a tool which provides an easy-to-use Graphical User Interface enabling the client to mask sensitive data before giving it to a third-party agency for developing a Machine Learning Application. Our tool enables a client to mask sensitive data through few mouse click
The proposed work addresses the escalating concern of crimes against women in contemporary society. This comprehensive endeavor employs a data-driven approach to tackle this critical issue. The primary objectives enco...
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Supermarkets and grocery stores aim to improve customer experience and maximize sales. Recommending frequently bought items and optimizing the layout of products can enhance customer satisfaction and sales revenue. Th...
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A Twitter account was made with the efficient use of a conversation bot as its main goal, and it has since accumulated followers consistently and automatically. Software systems can be interfaced with using a wide var...
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The growing volume of healthcare data necessitates advanced data mining techniques to extract meaningful patterns and insights. This paper introduces 'RX Assist,' an Intelligent Disease Prediction and Drug Rec...
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This study explores how advanced technology has revolutionized the use of drones for forest fire surveillance. This research study explores the historical development of Unmanned Aerial Vehicles (UAVs) in forest monit...
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A biometric detection system named finger vein recognition studies the vein patterns in human fingers that are concealed under the skin's surface through pattern recognition algorithms. It involves comparing the v...
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Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several *** all autistic children remain undiagnosed before the age of *** problems affecting face features are oft...
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Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several *** all autistic children remain undiagnosed before the age of *** problems affecting face features are often associated with fundamental brain *** facial evolution of newborns with ASD is quite different from that of typically developing *** recognition is very significant to aid families and parents in superstition and *** facial features from typically developing children is an evident manner to detect children analyzed with ***,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic *** study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)*** overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal *** the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed *** the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)*** extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI *** simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.
In today's highly competitive business environment, the rise of new competitors and entrepreneurial ventures has intensified the race to attract new customers and retain existing ones. There has never been a great...
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