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Merging Realms: Classical and Quantum-Enhanced Deep Learning Techniques for CIFAR-10 Classification

Merging Realms: Classical and Quantum-Enhanced Deep Learning...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Bobbili, Sai Jayanth Chandrappa, S. Reddy, V. Keshava Guru Prasad, M.S. Deepa, K. Sharma, Mansi Dept. of Computer Science and Engg. Dehradun India Dept. of Computer Science and Engineering Bengaluru India VTU Department of ECE India NMAMIT Department of ECE Nitte India

ISBN: (纸本)9798331517892

Classical and quantum-enhanced deep learning techniques are revolutionizing areas of artificial intelligence. This leads to substantial innovations in various domains, including the image processing. The CIFAR-10 dataset is a widely used and benchmarked dataset for assessing the performance of deep learning models. In this proposed method, the CIFAR-10 dataset is used for image classification in an effort to improve model performance. The modern and strong models are employed, such as CNN, InceptionV3, DenseNet, and a Hybrid Quantum-DenseNet. The precision, recall, accuracy, loss, and F1-score metrics are used in this analysis to assess these models. From the analysis, it is observed that CNN and Hybrid Quantum-DenseNet attained 77%-78% accuracy. The InceptionV3 reached the maximum accuracy of 90.15%. The performance of the developed hybrid model was exceptionally well across a range of parameters. DenseNet achieved an accuracy of up to 84%. © 2024 IEEE.

关键词： Adversarial machine learning

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Attention-Based AdaptSepCX Network for Effective Student Action Recognition in Online Learning 5

Attention-Based AdaptSepCX Network for Effective Student Act...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Dey, Arnab Anand, Anubhav Samanta, Subhajit Sah, Bijay Kumar Biswas, Samit Dept. of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India

In the realm of online learning and distance education, the issue of inadequate supervision looms large, posing a significant obstacle. This paper delves into the challenges posed by the lack of supervision in online learning environments and proposes an innovative solution to understand and recognize students' behaviors. This study's primary objective is to detect and recognize students' actions in images captured through webcam. This task distinguishes itself from the well-established video-based student action recognition domain, which relies on temporal cues. Recognizing student actions from images intensifies the complexity of the problem. To meet this challenge, a novel deep learning model named AdaptSepCX Attention, specifically designed for student action recognition in online learning environments, is introduced. The proposed method exhibits exceptional performance with 92.73% validation accuracy on the Student Online Action Image dataset (SOAId), a carefully curated collection comprising 2029 student-centric images. The proposed model outperforms well-established models such as DenseNet121, NASNet Mobile, ConvXNet, DELVS1 and MobileNetV2 in student action recognition. Action recognition for students has broader implications beyond the online classroom. It has the potential to revolutionize educational technology, making online learning more interactive and engaging. Enabling machines to understand and respond to student actions enhances education, personalizes learning, and supports students' academic success and well-being. This research enhances the understanding of student involvement in online learning and offers an effective solution for recognizing actions from images. © 2024 The Authors. Published by Elsevier B.V.

关键词： Students

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Environmental Monitoring with Machine Learning 1

Environmental Monitoring with Machine Learning

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sikarwar, Shailendra Singh Kumar, Sunil Meghwal, Lakshita Varshney, Manoj Roy, Swapnila Kumar, Ravi PGV College Dept. of Computer Science Gwalior India Madhav University Dept. of Pharmacy Pindwara India Mangalayatan University Dept. of Computer Eng. and Applications Aligarh India Lingayas Vidyapeeth Dept. of Chemistry Faridabad India J.S. University Dept. of Agriculture Shikohabad India

ISBN: (纸本)9798350356816

Data science is poised to transform environmental monitoring by boosting the precision, scope, and effectiveness of environmental assessments. Emerging technologies, including powerful machine learning algorithms, big data analytics, and remote sensing, are offering unprecedented capabilities in acquiring and evaluating environmental data. These developments permit more precise projections of ecological changes, real-time monitoring through vast sensor networks, and thorough climate modeling. Looking ahead, data science will drive creative approaches to environmental management, enabling tools for proactive decision-making and more effective resource management. The democratization of data science tools will also broaden stakeholder engagement, enabling communities and policymakers alike. By harnessing these future breakthroughs, data science has the prospect addressing important environmental concerns and creating sustainable solutions for a healthy planet. © 2024 IEEE.

关键词： Environmental monitoring

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IoT-Driven Precision Crop Recommendation System Using LSTM & TSC-NET for Sustainable Agriculture 1

IoT-Driven Precision Crop Recommendation System Using LSTM &...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Satya Sandeep, K. Lakshmi Sahithi, K. Shabana Azmi, Sd. Renuka Devi, J. Nirmala Jyothi, B. Vignan 'S Nirula Institute of Technology and Science for Women Dept. of Computer Science and Engineering Guntur India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384246

Andhra Pradesh is a well-known agricultural state with a major contribution to the production of rice, cotton, and chilies it is also known as the 'Rice Bowl of India'. Unpredictable weather events have been a concern to the state, it is proving to show a negative impact on the state's farming population, nevertheless the state still contributes materially to the country's cultivation production. on thorough research and historical analysis, it is stated that the food diffidence and conservationist problems arise from the established age-old agricultural methods, these methods are being followed without any systematic scientific pursuit. this study pursues to solve the mentioned problem by transitioning agricultural practices with the use of Internet of Things (IoT) technologies. A preferred multi-class classification model leveraging IoT data. the data primarily includes meteorological parameters such as soil NPK values, temperature, humidity, and more. The hypothesized model, a hybrid of Long Short-Term Memory (LSTM) and Time Series - Convolutional Neural Networks (TSC-NET), which recommends suitable crops for the cropland location, it is accomplished by integrating Time-space data. the food grain acreage in Andhra Pradesh is seen dwindling by 4.2 % in 2022-2023, underscoring the susceptibility of farmers to monsoon failures. In addition, macroeconomically speaking, the slowdown in agrarian development disrupts economy's advancement as a whole, advocating for agrarian development. The IoT -driven crop classification model provides the novel approach to enhance crop selection and optimize production potential. therefore, improving the precision and efficacy of crop forecasting while enabling farmers to make well-informed decisions accounting for soil conditions and climate data. It intends to tackle both macro-level economic concerns and micro-level farmer vulnerabilities, this research levels to improve the sustainability and efficiency of agriculture in Andhra Pr

关键词： Internet of things

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Ensembled Obstructive Sleep Apnea Detection Using Extra Tree Ensemble Technique 2

Ensembled Obstructive Sleep Apnea Detection Using Extra Tree...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Khan, Atiya Biswas, Saroj Kr. Chunka, Chukhu Barman, Subhas National Institute of Technolgy Silchar Dept. of Computer Science & Engg. Silchar India Jalpaiguri Government Engineering Collage Dept. of Computer Science & Engg West Bengal India

ISBN: (纸本)9798350371314

Obstructive Sleep Apnea (OSA) is a sleep-related disorder with repetitive episodes of complete or partial upper airway obstruction during sleep. These episodes result in disrupted breathing patterns and reduced oxygen levels in the blood. Electroencephalogram is a valuable tool in detecting and diagnosing sleep apnea as it provides insights into the disruptions of sleep caused by repeated apnea events. This paper proposes an efficient model to detect OSA named Ensembled Expert System for Obstructive Sleep Apnea Detection (EESOSAD) using EEG physiological sensors and an Extra Tree ensemble classifier. The proposed model incorporates Discrete Wavelet Transform with Daubechies order-8 for decomposing EEG signal into multiple sub-bands and extracting statistical features like - Maximum, Minimum, Mean, Standard Deviation, Kurtosis, Power, Energy, Root Mean Square, and Variance. A state-of-the-art data preprocessing pipeline is also designed and developed for treating corrupted, missing, duplicates, and outliers, as medical data are prone to noise, anomalies, and inconsistencies that can distort patterns and compromise analysis validity. Finally, for the classification task, the Extra Tree ensemble classifier is used for OSA detection to comprehensively evaluate the performance of the developed data pre-processing and feature fusion pipeline. Multiple evaluation metrics, including Precision, Recall, Accuracy, F1-score, and AUC-ROC curve, have been employed to compare and assess the model's effectiveness. Furthermore, diverse state-of-the-art ensemble learning models have been incorporated for benchmark comparison and discussion. The proposed EESOSAD model achieved an average accuracy of 59% and outperformed multiple benchmark models with an optimal performance margin. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Optimized 1D CNNs for Enhanced Early Detection and Accurate Prediction of COPD and Other Pulmonary Diseases

Optimized 1D CNNs for Enhanced Early Detection and Accurate ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Sahu, Prakash Jha, Sneha Kumar, Santosh IIIT-Naya Raipur Computer Science & Engineering India Csvtu Dept. of Data Science Chhattisgarh India

ISBN: (纸本)9798350364866

Artificial intelligence (AI) advancements have opened new avenues in medical diagnostics, particularly for the early detection of respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD). COPD, the most common respiratory disease, is marked by breathing difficulties, mucus (sputum) production, coughing, and wheezing. Early diagnosis is crucial for effective management and treatment. A one-dimensional convolutional neural network (1D CNN) model optimized with Adam and RMSprop algorithms is proposed for COPD detection. Using the ICBHI 2017 dataset, the model's performance with each optimizer was evaluated based on training and validation accuracy. The Adam optimizer achieved a training accuracy of 94% and a validation accuracy of 90%. In comparison, the RMSprop optimizer attained a training accuracy of 92% and a validation accuracy of 88%. These findings indicate that the Adam optimizer outperformed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics. © 2024 IEEE.

关键词： Lung cancer

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A Proposed Model for Machine Learning-Based Early Chronic Kidney Disease Detection 3

A Proposed Model for Machine Learning-Based Early Chronic Ki...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Bhandurge, Sonam Sambrekar, Kuldeep Allagi, Shridhar Rajpurohit, Vijay S. Sannakki, Sanjeev Angadi Institute of Technology and Management Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Gogte Institute of Technology Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Kle Institute of Technology Hubli Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering India

ISBN: (纸本)9798350354171

Chronic kidney disease (CKD) is a sluggish and cumulative decline of kidney characteristics over the years. Early detection of CKD is essential when you consider that delayed detection and hospital treatment of the situation might eventually bring about the final stage of CKD and the patient's death. The prediction of diseases has grown to be appreciably impacted by means of the utilization of machine learning (ML) in healthcare. Thus, ML methods are the simplest in CKD prediction. The major goal is to use distinct supervised ML techniques, such as Support Vector Machine, Random Forest, Artificial Neural Network, and Logistic Regression, to examine and verify how correct they may be at detecting CKD. The proposed model provides various strategies for detecting CKD using medical facts which incorporates data preprocessing, strategies for handling missing values, and data reduction. It is noted that the clinical data has been classified with an overall accuracy of 100% by the Random Forest algorithm. The suggested ML model will help doctors identify chronic kidney ailments early on, which will be an effective manner of preventing the illness. © 2024 IEEE.

关键词： Support vector regression

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Enhancing Dietary Monitoring using Deep Learning: Food Recognition and Calorie Estimation 15

Enhancing Dietary Monitoring using Deep Learning: Food Recog...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suneela, M. Sahithi, Mandadi Vamsi, Nuthalapati Naik, Nunsavathu Balaji Chowdary, Narra Sujitha Vignan's Lara Institute of Technology & Science College Dept. of Computer Science & Engineering Guntur India

ISBN: (纸本)9798350370249

The project 'Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation' introduces an innovative method aimed at improving dietary tracking and fostering healthier eating habits. By employing cutting-edge deep learning techniques, its core objective is to precisely identify various food items and rapidly estimate their caloric content, offering users advanced and personalized monitoring capabilities. Utilizing the Python programming language alongside the MobileNet architecture model, the project underwent rigorous training and evaluation using the expansive Food 101 dataset, comprising 37,046 food images distributed across 101 distinct classes. Notably, the model demonstrated exceptional performance, achieving a training accuracy of 97.02% and a validation accuracy of 98.17%, highlighting the efficacy of this approach in accurately categorizing a wide array of food items. This system provides users with several crucial functionalities. Furthermore, it furnishes users with essential insights by estimating the caloric content of recognized foods, facilitating effective monitoring of dietary intake. The intelligent diet monitoring capabilities enabled by Food Recognition and Calorie Estimation empower users to make informed choices regarding their dietary preferences. Through the continuous tracking and analysis of their daily food consumption, users can glean valuable insights into their nutritional habits, set personalized goals, and make necessary adjustments to achieve a well-balanced and healthy diet. Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation stands as a prominent illustration of successful implementation of deep learning techniques, particularly with the utilization of the MobileNet architecture, for food recognition and calorie estimation. With its remarkable accuracy, real-time processing capabilities, and intelligent monitoring features, this project has the potential to revolutioni

关键词： Convolutional Neural Network (CNN) algorithm Datasets MobileNet architecture Web application

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A Lightweight-CNN Model for Efficient Lung Cancer Detection and Grad-CAM Visualization 2

A Lightweight-CNN Model for Efficient Lung Cancer Detection ...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Tasmim Bakchy, Sagor Chandro Peyal, Hasibul Islam Islam, Md. Injamul Yeamin, Golam Kibria Miraz, Shafiun Abdal, Md. Nazmul Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Electrical & Electronic Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350358667

The lungs' abnormal cell growth leads to the development of lung cancer. Early cancer identification could make treatment easier, potentially saving millions of lives annually. This study's main goal is to more rapidly and effectively classify various types of lung cancer by employing a lightweight, computationally efficient convolutional neural network (CNN) model to categorize three different types of lung cancer. With an outstanding validation accuracy of 99.48%, the suggested model surpasses the achievements of previous works. The 15,000 CT scan images in our dataset include three different forms of lung cancer. The proposed model performs exceptionally well, as evidenced by its astounding precision, recall, and F1-score, all above 99%, and by its flawless Area Under Curve (AUC) score of 100%. The proposed model has fewer parameters than the existing transfer learning models. Gradient Weighted Class Activation Mapping (Grad-CAM) was used to create class activation maps, which were then used to create a heatmap to display the classification zone. © 2023 IEEE.

关键词： Neural network models

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SDN-based Mobile Edge Computing with Vacation of Virtual Machines 3

SDN-based Mobile Edge Computing with Vacation of Virtual Mac...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Rout, Suchismita Jaiswal, Ankita Bagade, Monika Keshao Patra, Sudhansu Shekhar Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Shri Ramdeobaba College of Engineering and Management Dept. of Computer Sc. & Engineering Nagpur India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350381931

A vast array of IoT devices have been implemented worldwide as cloud computing and virtualization have become more and more commonplace. Because of this, processing of computationally demanding tasks is moved from the edge to the centralised cloud, increasing energy consumption in cloud data centres and causing a noticeable delay for vital applications. The paradigm for cloud computing has shifted in recent years from centralised to mobile edge computing (MEC), where computational workloads are delegated to edge servers close to user equipment (UE). This epitome results in minimal latency for UE, effective resource utilisation at the edge, and decreased energy consumption in cloud data centres. Given this, developing optimal energy-saving solutions for resource allocation and computing job offloading in MEC networks has become a challenging challenge due to the rapidly growing size and complexity of these networks. The vacation policy is a suggested power-saving traffic management strategy for edge servers. Software-defined network (SDN) architecture allows virtual machines (VMs) to take vacations. The goal of QoS optimisation is to decrease the active servers serving the tasks while maintaining QoS criteria. Numerous performance metrics of the system are calculated, including average system size, sojourn duration, percentage of client requests fulfilled, and frequency of desertion. Lastly, many numerical results are given to demonstrate the influence of certain particular factors on the MEC performance metrics. © 2024 IEEE.

关键词： Mobile edge computing

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