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Analyzing Acute Lymphoblastic Leukemia Across Multiple Classes Using an Enhanced Deep Convolutional Neural Network on Blood Smear

Analyzing Acute Lymphoblastic Leukemia Across Multiple Class...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Pawar, Janardan Bhosle, Amol A. Gupta, Priyanka Mehta, Hemant Borate, Vishal Kisan Mali, Yogesh Kisan Indira College of Commerce & Science Department of Computer Science India Mit Art Design and Technology University Department of Computer Science and Engineering Pune India Dr. D y Patil Institute of Technology Pune Pimpri India School of Engineering & Technology Ambi Pune India Dr D y Patil College of Engineering & Innovation Pune Talegaon India G.H Raisoni Institute of Engineering and Management Pune Wagholi India

ISBN: (纸本)9798350382693

Bone marrow cancer, commonly referred to as Acute Lymphoblastic Leukemia (ALL), is marked by the rapid proliferation of immature lymphoid cells. This condition affects both children and adults, standing as the most common type of cancer in children. Recent years have seen considerable advancements in diagnosing and treating ALL. Precise risk assessment and the formulation of an effective treatment plan hinge on a comprehensive diagnostic approach that incorporates clinical, morphological, cytogenetic, and molecular data. Nevertheless, to improve survival rates and the quality of life for individuals battling this aggressive blood cancer, further research and clinical trials are imperative to overcome challenges related to drug resistance, relapse, and long-term side effects. Accordingly, this research introduces a highly optimized deep learning model, specifically a deep optimized convolutional neural network (CNN), for the early detection and classification of ALL. The advanced CNN architecture features five convolutional blocks, including 11 convolutional layers and 4 max pooling layers. The optimization of this deep CNN model involved tuning hyper parameters such as epochs, batch size, and the use of specific optimizers, namely Adam and Adamax. Among these, the model achieved superior performance with the Adam optimizer, demonstrating high levels of accuracy and precision, at 0.93 and 0.91 respectively. © 2024 IEEE.

关键词： Lung cancer

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Photovoltaic Power Forecasting Using Support Vector Machine and Adaptive Learning Factor Ant Colony Optimization 3

Photovoltaic Power Forecasting Using Support Vector Machine ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Alabdeli, Haider Rafi, Shaik Naveen, I.G. Rao, Deepak Dasaratha Nagendar, Y. College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Qis College Of Engineering And Technology Department Of Mechanical Engineering Ongole India Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India Indian Institute Of Technology Department Of Cse Patna India School Of Computer Science And Engineering Sr University Telangana Warangal India

ISBN: (纸本)9798350318609

Due to its flexible and clean nature, distributed Photovoltaic (PV) power is essential for solving energy and operational coordination challenges in integrating solar energy with other power sources. However, with rapid development complexities have emerged including in climate prediction, resulting in longer training times than those for the signal model. The Adaptive Leaning Factor Ant Colony Optimization (ALFACO) is used to optimize the ability to predict PV power values by linearly decreasing the weight and involved an adaptive learning factor to enhance the optimizing capability of the ACO. The proposed method, a combination of ALFACO and Support Vector Machine (SVM) can improve the robustness of the model, making it less likely to get stuck in local optima and making the SVM more capable of handling noisy or complex datasets. The proposed method, ALFACO-SVM achieves superior performance metrics including Root Mean Square Error (RMSE) of 0.0635, Coefficient of Determination R2 of 0.8920, Mean Absolute Error (MAE) of 0.0091, Median Absolute Error (MedAE) of 0.5620, Mean Squared Error (MSE) of 0.0045 and Explain Variance Score (EVS) of 0.0095, compared to existing methods such as Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), LSTM and Bias Compensation LSTM (BCLSTM). © 2024 IEEE.

关键词： Support vector machines

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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Towards Precision Medicine: Nature-Inspired Optimization Algorithms for Personalized Cholesterol Disease Prediction

Towards Precision Medicine: Nature-Inspired Optimization Alg...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Datta, Parul Maram, Dr Balajee Sravani, Meesala Das, Smritilekha University Institute of Engineering Chandigarh University Computer Science and Engineering Department Punjab India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India GMR Institute of Technology CSE Department Rajam India Koneru Lakshmaiah Education Foundation Department of CSE AP Vaddeswaram India

ISBN: (纸本)9798331539948

This survey article presents a number of optimization algorithms based on the natural selection with the help of collecting data on the precision medicine and cholesterol disease prediction. Of course, it refers to the fact that traditional measures go only down to individual differences. This looks into contemporary methods for enhancing the predictive capacity;these include ant colony optimization algorithm, simulated annealing, particle swarm optimization and genetic optimization. This paper examines current strategies for cholesterol levels prediction with emphasis having been placed on creating individualized treatments based on patient parameters. The comparison helps to identify that algorithms derived from nature are more effective compared to the other approaches and methodologies. The study is useful for the researchers and healthcare practitioners who are looking for the use of computational approaches in designing individual cholesterol treatment. Therefore it carries out the possible ethical problems as well as the limitation particularly the data security concern and the future recommendation for the research. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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An Approach to Generate Music with LSTMN

An Approach to Generate Music with LSTMN

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Bhutani, Sparsh Gupta, Sumita Gupta, Dhruv Sehgal, Harshit Singh Amity University Amity School of Engineering & Technology Department of Computer Science &engineering Noida India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Music was generally considered as a simple sign that was physically produced. Music has become progressively apparent to innovation lately, which can now make a suite out of music with no necessity for human interest. It should beat different specialized obstacles to finish this work, which are portrayed exhaustively in this paper. The paper incorporates a short prologue to music and its parts, as well as references and investigations of significant work by different researchers in the field. The fundamental objective of this examination is to bid a technique for producing melodic notes with Reccurent Neural Networks (RNN's), explicitly Long Short-Term Memory Network (LSTMN) organizations. This procedure is executed involving a model where information is addressed utilizing the ABC representation text record design for more straightforward access and understanding. Preprocessing information before it is likewise depicted to take care of it into the model. Over a solitary layered LSTMN organization, the model utilized in this study is utilized to grasp chorus melodic note successions. For more noteworthy learning, the algo should have the option to hold past subtleties of a melodic grouping and its construction. This paper presents a portrayal of the layered engineering utilized in the LSTMN model, in addition to its intertwining associations for fostering a brain organization. This study gives a slip look into weight and predisposition dispersions in each slab of the model, in addition to a point-by-point portrayal of misfortunes and exactness at each progression and group. After a careful assessment, the model delivered magnificent outcomes in the formation of new songs © 2022 IEEE.

关键词： Music

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Sentiment Analysis in Social Media Using Deep Learning Techniques

Sentiment Analysis in Social Media Using Deep Learning Techn...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Babu, M. Shinde, Pravin Vishnu Agrawal, Neha Bhaskar, M. Vijaya Hemabala, K. Priya, Sweta Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai600124 India Shah and Anchor Kutchhi Engineering College Affiliated to University of Mumbai Department of Artificial Intelligence & Data Science Maharashtra India Maharaja Agrasen Institute of Technology Department of It New Delhi110085 India SriVenkateswara Institute of Technology Department of Computer Science & Engineering Andhra Pradesh Anantapur India Department of Freshmen Tiruvathi517502 India Amity University Department of Amity School of Communication University Patna801503 India

ISBN: (纸本)9798331543617

This work offers a thorough investigation of sentiment analysis in social media using deep learning techniques. Understanding stoner sentiment has become essential for colourful operations like marketing, public opinion research, and recommendation algorithms due to the rapid expansion of social media networks. Deep learning approaches have emerged as crucial tools for sentiment analysis tasks because of their capacity to extract intricate patterns from massive datasets. The usefulness of deep learning models, such as convolutional neural networks (RNNs), and intermittent neural networks (RNNs), and their variations, in extracting complex sentiment information from social media textbooks is investigated in this study. Additionally, the study looks at model infrastructures, point representations, and pre-processing techniques to improve sentiment analysis performance. The results show how effective deep learning techniques are in extracting sentiment directly from various social media posts, opening the door for more sophisticated sentiment-apprehensive digital processes © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Vision Transformers for Edge Devices: Pruning and Quantization Approaches 4

Efficient Vision Transformers for Edge Devices: Pruning and ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Pareek, Shashank Saleh Al-Samalek, Ahmed Alkhayyat, Ahmed Singh, Sandeep Singh, Amrita Dasi, Shivakrishna Chandigarh University Department of Computer Science and Engineering Mohali India Al-Zahrawi University College Department of Medical Laboratory Technics Karbala Iraq College of Technical Engineering The Islamic University Najaf Iraq School of Engineering and Technology Bahra University Department of Civil Engineering Waknaghat HP Solan India Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science Engineering Jhanjeri Punjab Mohali40307 India Nims School of Computing Science and Artificial Intelligence Nims University Rajasthan Jaipur India

ISBN: (纸本)9798350387490

In this paper, background of pruning and quan-tization are discussed as well as carrying out the methods on Vision Transformers (ViT) with aim of improving efficiency and readiness for use on edge devices. The study considers the effects of these optimizations for model accuracy, size, time for inference, and energy expenditure. Reduction in size and computation demand is considerably achieved through pruning but this is at the expense of accuracy of the model. Quantization of models is beneficial as it reduces the size of the model and the time taken during inference, these are ideal for environments that have limited resources but it comes with drawbacks that are associated with quantization. Using both yields to the model as compact and efficient as possible but with a small trade off of accuracy. Some of the issues we consider are the question of how to op-timize trade-off between precision and speed, methods to reduce quantization effects, and techniques to make models conform to a given hardware system. The study determines that although existing techniques explain ways of advancing efficiency, they bring in new challenges and constraints. Further, there is a need to learn more about more sophisticated techniques concerning pruning and combinations of quantization approaches, as well as hardware wit tailored to perform low-precision calculations. Besides, the above approaches also need to be generalized into various application fields and enhance the model's practicality and stability. The study enriches the current knowledge base concerning the improvements of Vision Transformers for application on edge equipment while underlining the significance of the further work on model effectiveness and hardware implementation. This work is beneficial as it prepares the ground for innovations in applying state-of-art deep learning models in environments that are limited by resource availability in order to design better practical solutions in AI. © 2024 IEEE.

关键词： Distribution transformers

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COVID-19 Classification using CNN, Inception V3 and Transfer Learning

COVID-19 Classification using CNN, Inception V3 and Transfer...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Mohammed Iqbal, M. Tamilmani, G. Tej Pavan, Caserker Hema Sri Pranav, Pendyala VIT-AP University School of Computer Science and Engineering Amaravati India SRM Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (纸本)9798350319200

The outbreak of the COVID-19 pandemic has posed unprecedented challenges for global healthcare systems, neces-sitating accurate and rapid diagnostic solutions. The suggested method utilizes the capabilities of Convolutional Neural Networks (CNN) and the Inception V3 module to provide a novel deep learning strategy for COVID-19 identification. The capacity of the Inception V3 architecture to extract nuanced features from challenging medical imaging data is famous. This work evaluates the performance of the CNN-Inception V3 model in differentiating COVID-19 instances from other respiratory illnesses, such as pneumonia, fibrosis, tuberculosis, and normal lung scans, through a thorough examination employing a broad dataset of chest X-ray and CT images. Important parameters like accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) are used to assess the model's performance. The study also explores the interpretability of CNN-Inception V3 model predictions, providing information on possible clinical uses. The results of this study add to the corpus of research on deep learning-based COVID-19 diagnostics and show the value of cutting-edge technology in the fight against infectious diseases. © 2023 IEEE.

关键词： CNN classification COVID-19 deep learning fibrosis Inception V3 pneumo-nia tuberculosis X-ray

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Long-Term Water Quality Prediction with Patch Savitsky-Golay Filtering and Transformer

Long-Term Water Quality Prediction with Patch Savitsky-Golay...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Yongze Qiao, Junfei Bi, Jing Yuan, Haitao Zhai, Jiahui Zhou, MengChu Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Information Science and Technology Beijing University of Technology Beijing100124 China College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark07102 United States

ISBN: (纸本)9781665410205

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction is based on historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from time series data and capture complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called PSGT for short, which integrates Patch Savitsky-Golay filtering and Transformer. First, this work adopts a Patching method to embed sub-time series data and obtains the trends and semantic information of the time series. Second, it uses the Savitsky-Golay filtering to effectively remove the noise data in the patch and improve the prediction accuracy. Third, it uses a Transformer mechanism to address the nonlinear problem of water quality time series and improve long-term prediction capability. Two real-world datasets are utilized to evaluate the proposed PSGT, and experiments prove that PSGT performs better than other benchmark models by at least 6%. © 2024 IEEE.

关键词： Self-supervised learning

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Systematic Structures and Silicon Stories Shaping the Saga of Software Solutions 6th

Systematic Structures and Silicon Stories Shaping the Saga o...

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6th International Conference on Information Systems and Management science, ISMS 2023

作者： Bhuvana, J. Vekariya, Daxa Singh, Shweta Sharma, Seema Karnataka Bangalore India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Commerce and Management Assam Down Town University Assam Guwahati India

ISBN: (纸本)9783031664090

Examining the complex interplay between software engineering and silicon technology, "Systematic Structures and Silicon Stories Shaping the Saga of Software Solutions" is an interdisciplinary investigation that combines historical study, current research, and practical application. This research gives a holistic viewpoint on the development of software solutions, using state-of-the-art algorithms to evaluate the effects of the past, enhance the present, and forecast the future of silicon performance. The relevance of the suggested approach is further emphasized by a comparison to seven other methods, each of which has a distinct name. In this study, we compared six key features across all three approaches by giving fictitious numerical values to measure how well each one performed. The results of the evaluations demonstrate that the suggested technique excels in every respect;this is evidence of the comprehensiveness of the approach used and the depth to which the software-hardware interaction is understood. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Application programs

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