The rapid advancement of machine learning (ML) and the growing need for computational power have led to the exploration of quantum computing, which offers significant potential for faster complex calculations. However...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
The rapid advancement of machine learning (ML) and the growing need for computational power have led to the exploration of quantum computing, which offers significant potential for faster complex calculations. However, Quantum Machine Learning (QML) faces challenges due to the limited number of qubits and noise of quantum circuits, particularly with Noisy Intermediate-Scale Quantum (NISQ) devices. These challenges severely limit the current capacity to train accurate and stable Quantum Machine Learning Models. In this paper, we propose a novel framework for QML that employs the knowledge distillation method to harness the power of well-trained classical machine learning (CML) models and enhance the training performance of QML models. In this framework, we utilize the well-trained CML as a teacher model to assist the training of the student QML model using the knowledge distillation method. By distilling knowledge from the robust CML model, our framework can potentially address the problem of the barren plateau which hinders effective model training. Knowledge distillation is well suited for this framework through the transfer of knowledge without parameter sharing. Through empirical tests, our framework has demonstrated not only an increase in the accuracy of QML models but also a notable improvement in training stability.
Leukemia, a cancer characterized by the rapid overproduction of abnormal blood cells, presents notable diagnostic and treatment challenges. Identifying leukemia subtypes promptly and accurately is vital for devising e...
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ISBN:
(数字)9798350378047
ISBN:
(纸本)9798350378054
Leukemia, a cancer characterized by the rapid overproduction of abnormal blood cells, presents notable diagnostic and treatment challenges. Identifying leukemia subtypes promptly and accurately is vital for devising effective treatments and improving patient outcomes. This study proposes an advanced Inception V3 model optimized for the automatic identification of leukemia from microscopic images of blood smears. By integrating cutting-edge deep learning methods and a customized model architecture, this system differentiates between Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML), and healthy samples. The model benefits from preprocessing techniques like image resizing, normalization, and augmentation to enhance its performance. Through transfer learning, the Inception V3 model is fine-tuned on a rich dataset covering various leukemia subtypes. Experimental results demonstrate the model's high effectiveness, achieving 100% accuracy during training and 99.72% accuracy in testing, proving the system's reliability for automated leukemia detection.
The face of a person is his identity. Most of his emotions, issues can be derived from his face. Face is the window to the soul which was said by famous French doctor Duchenne de Boulogne. He used several techniques t...
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The face of a person is his identity. Most of his emotions, issues can be derived from his face. Face is the window to the soul which was said by famous French doctor Duchenne de Boulogne. He used several techniques to support his theory like giving shocks of electric impulses to understand how a person reacts to muscular contractions. He also tried to induce some of the expressions of bizarre looking. The ultimate aim is to analyze how much muscles contribute to emotion. He successfully derived lots of human emotions which is hidden. After 200 years this field is still active as it requires more experiments to extract invisible truths of human truths. Lots of Emotion Recognition which is automatic have been seen in the field of Marketing and Advertising. It is also seen in the field of Medical, Law and order$\ldots$etc. The main fundamental question arrives whether it is good to enter into our personal space. This a question which has lots of dimension. Those who are against this practice is claiming that this is a violation of human rights by which we can use the stored data to harm the society in future. Even though it is a major concern if we can rectify the above the scope in this area is phenomenal. Lots of researchers are still working on this area because of mainly one reason that is its wide application in the area of Medical science. It can be used by a doctor for diagnosing a person with certain psychological disorders, children with Autism, person with Parkinson’s disease. It can be widely used to diagnose children with Autism so that particular child can be motivated at early stage leads to his success in future and thus provide a good citizen to the society. In our research we are using several datasets such as FER2013, CK+$\ldots$etc. These datasets have been given to a model which includes Deep Convolutional Neural Network. Here input image is given to a model using camera. The particular image has to done preprocessing to extract fine features in th
The increasing reliance on computers and the internet has increased the need for protection against attacks and security threats. A network-based intrusion detection system (NIDS) is one of the promising research fiel...
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Accurate task execution time estimation is vital for efficient and dependable operation of safety-critical systems. However, modern automotive functions' complexity challenges conventional estimation methods. To a...
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Optimizing renewable energy systems in healthcare facilities via use of modern ML algorithms to improve energy efficiency along with sustainability is goal of this research. Integrating machine learning provides a pot...
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This document provides an in-dept. analysis of how blockchain technology can greatly improve transparency, traceability, and accountability in fish and livestock supply chains, presenting the potential to transform th...
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ISBN:
(数字)9798331522100
ISBN:
(纸本)9798331522117
This document provides an in-dept. analysis of how blockchain technology can greatly improve transparency, traceability, and accountability in fish and livestock supply chains, presenting the potential to transform this industry. Utilizing blockchain’s decentralized framework, which guarantees a secure and unchangeable record of transactions, problems associated with inefficiencies, fraud, and data manipulation in conventional supply chains can be efficiently resolved. This survey analyzes the present condition of blockchain applications in fish and livestock management, emphasizing their advantages and drawbacks. Furthermore, it highlights significant technical and regulatory challenges that impede wider acceptance, while recognizing potential future research avenues that could enhance processes from the production stage to the retail stage. The results intend to assist stakeholders in developing supply chains that are more sustainable, efficient, and transparent, fostering trust and complying with regulatory requirements.
In the face of escalating air pollution in Patancheru, Hyderabad, the 'EcoAI Forecast' project presents a pioneering approach. It transcends the limitations of traditional linear models, which fail to effectiv...
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Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence...
Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model’s performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested.
In recent days, DeFi tokens have gained popularity as an investment option in the pandemic period and has gained a significant amount of investment. Cryptocurrency trading is a type of DeFi that has gained a lot of at...
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