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Sparsely Supervised learning for Medical Image Classification on Noisy Heterogeneous Data 3

Sparsely Supervised Learning for Medical Image Classificatio...

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3rd international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2023

作者： Thillai Manjari, T. Chakravarthi, S. Sreenivasa Sountharrajan, S. Rao, B. Narendra Kumar Suganya, E. Nivaashini, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Cse Chennai India Mohan Babu University Aiml School of Computing Tirupati India Ssn College of Engineering Dept. of Information Technology Chennai India Sri Ramakrishna Engineering College Dept. of Cse Coimbatore India

ISBN: (纸本)9798350399264

In a number of tasks for assessing natural and medical prints, deep neural networks have bettered humans. These achievements, however, are solely reliant on properly labelled training data. When presented with a few samples of noisy-labelled images, the network training strategy might encounter difficulties, resulting in a suboptimal classification models. The quality of medical picture annotations strongly depends on the knowledge and experience of the annotators, which makes this difficulty more acute in the context of medical image analysis. Such problem arises due to a number of reasons ranging from human error, inexperience or even misreading of the images. But at the same time, proper labels are exceptionally useful in training models, while improperly labelled data actually hampers the efficiency of the model. Label for a lot of the image dataset out there is actually already generated, but when we think about the noisy labelled data and go for an unsupervised learning model instead of a supervised model the labels that are correct are also going to waste. To solve this problem without wasting labels, a sparsely supervised learning strategy based on transfer learning is constructed with the aid of the keras Xception model. This paper compares the efficiency of a sparsely supervised learning model employing transfer learning to that of other traditional CNN models based on their performance metrics. © 2023 IEEE.

关键词： Image classification

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Informer_Casual_LSTM: Causal Characteristics and Non-Stationary Time Series Prediction 7

Informer_Casual_LSTM: Causal Characteristics and Non-Station...

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7th international conference on Machine learning and Natural Language Processing, MLNLP 2024

作者： Yang, Yuxia Dong, Yilin Shanghai Maritime University College of Information Engineering Shanghai China

ISBN: (纸本)9798350354973

Many existing forecasting models tend to work well when forecasting short-sequence time series. However, when working with long sequence time series, the performance suff ers significantly. Recently, there has been more intense research in this direction, and Informer is currently the most efficient predicting model. Informer's main drawback is that it does not allow for incremental learning. This paper proposes an improved multi-scenario time series forecasting model-Informer CasualConv_GRU, which captures causal relationships at different time scales through multi-scale causal convolution and gated recurrent units (GRU) to address strong nonlinearity and high noise in the data. Validation on datasets from the energy and financial sectors shows that the proposed model performs excellently in short-term and mid-term forecasting. © 2024 IEEE.

关键词： Prediction models

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A Survey Paper on Smart-Farm: Machine learning and IoT-based Agriculture Decision Support Portal 15

A Survey Paper on Smart-Farm: Machine Learning and IoT-based...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Haripriya, K. Chand, Yeluru Vinay Nallamothu, Bhuvan Mahadhyut, S. Sumanth, D. Sree Sangem, Anish Dept of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331300579

Smart Farm, an innovative agricultural platform, is at the forefront of revolutionizing traditional farming practices by integrating machine learning, deep learning algorithms, and IoT technologies seamlessly. This trailblazing technology goes above and beyond traditional approaches by properly estimating crop yields and analyzing seed quality, giving farmers critical insights for optimal agricultural planning. Smart-Farm, by leveraging the power of machine learning algorithms, becomes a trustworthy ally for farmers, providing precise and fast information critical for agricultural decision-making. The addition of IoT technology expands its capabilities even further, allowing for real-time soil quality evaluations via strategically positioned sensors in the soil. These sensors monitor crucial characteristics like as moisture levels, nutrient content, and pH levels, allowing farmers to perform targeted soil management using a data-driven approach. This not only optimizes crop farming practices but also increases overall productivity tremendously. The Agriculture Portal, a key component of Smart-Farm, exemplifies agricultural empowerment by empowering farmers to make well-informed decisions that result in better outcomes, thereby creating a more sustainable and efficient approach to contemporary farming. © Grenze Scientific Society, 2024.

关键词： Smart agriculture

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Latex Equation Sequencer using Vector Mask Overlay and Localized Composition Analysis 15

Latex Equation Sequencer using Vector Mask Overlay and Local...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deore, Shalaka Mishra, Ranu Mapar, Aishwarya Shitole, Sakshi Mandani, Pranav Kulkarni, Shriniwas Department of Computer Engineering Modern Education Society's Wadia College of Engineering Maharashtra Pune India TextMover Labs LLC MesaAZ United States

ISBN: (纸本)9798331300579

LaTeX finds extensive application in report generation across diverse sectors. Predominantly, LaTeX code is incomprehensible without extensive training and compilation into vector or raster images. Complex LaTeX code blocks are somewhat difficult to interpret without code level comments at a glance. To address this, step-by-step animations of LaTeX components offer an interactive experience for readers, facilitating a more intuitive approach to learning different aspects of LaTeX syntax. By dissecting labyrinthine code segments, these animations resemble a debugging process in programming languages, allowing users to grasp each step more effectively. Thus, the proposed system overcomes the disadvantage of the existing system, which is that it can animate latex code. © Grenze Scientific Society, 2024.

关键词： Animation

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Mastering Super Mario Bros.: Overcoming Implementation Challenges in Reinforcement learning with Stable- Baselines3

Mastering Super Mario Bros.: Overcoming Implementation Chall...

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2024 international conference on Advances in computing Research on Science engineering and Technology, ACROSET 2024

作者： Khambhayata, Manav Singh, Shivansh Bala, Neetu Bhattacharyya, Arya Computer Science Chandigarh University Mohali India Chandigarh University Mohali India

ISBN: (纸本)9798350388800

In the ever-evolving landscape of artificial intelligence, the application of reinforcement learning (RL) techniques to game playing has emerged as a captivating frontier, showcasing the capacity of intelligent agents to master complex environments and tasks autonomously. This research paper tackles the intricate process of implementing Reinforcement learning (RL) algorithms for training agents in playing 'Super Mario Bros.' within the OpenAI Gym environment, amidst challenges posed by version inconsistencies in libraries. We offer a focused approach, emphasizing the utilization of the latest versions of libraries such as OpenAI Gym and Stable-Baselines3 in PyTorch. By meticulously addressing errors stemming from version disparities, we provide a systematic guide to navigate through the implementation process successfully. Our methodology centers on the Proximal Policy Optimization (PPO) algorithm, known for its efficiency and reliability. Through iterative training, RL agents adeptly learn to maneuver the complexities of the game, optimizing strategies while surmounting obstacles. This paper presents key insights garnered throughout the implementation journey, including strategies for troubleshooting version inconsistencies and effectively mitigating errors. Serving as a practical resource for researchers and practitioners, our work aims to facilitate advancements in RL-based gaming AI research by offering a functional solution to address version inconsistencies and propel further development in the field. © 2024 IEEE.

关键词： Intelligent agents

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EDNCL: An Efficient Deep learning Approach to Identify Cardiovascular Diseases Based on Enhanced Deep Neural Classifier Logic

EDNCL: An Efficient Deep Learning Approach to Identify Cardi...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sabarivani, A. Arulmozhiselvan, L. Nirmala, P. Dhanalakshmi, G. Alok, N. Al-Safarini, M.Y. Sathyabama Institute of Science and Technology Department of Biomedical Engineering Chennai India Meenakshi Sundararajan Engineering College Department of Information Technology Chennai India Hindustan College of Arts & Science Department of Microbiology Padur Chennai India Panimalar Engineering College Department of Information Technology Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

In light of the detrimental consequences of heart disease on society, there has been a concerted effort to improve its diagnosis and treatment. Data mining and the storage of medical information can enhance patient management opportunities as technology and medical diagnostics become more synergistic. Consequently, it is imperative to investigate the interdependence of the risk factors in patients' medical histories and to understand their respective contributions to the prognosis of cardiac disease. The objective of this research is to conduct an analysis of the numerous components in patient data in order to accurately predict cardiac disease. The proposed model, which is referred to as Enhanced Deep Neural Classifier Logic (EDNCL), has been employed to identify the most critical attributes for the prediction of heart disease. This model is designed to accurately predict cardiovascular disease using deep learning principles. It is necessary to cross-validate the suggested scheme with the more traditional Random Forest Classifier (RFC) learning strategy in order to determine its effectiveness. The efficacy of vision transformer models is contrasted to the state-of-the-art methodology RFC in order to illustrate the framework's contribution. The proposed framework demonstrates notable classification results, as evidenced by the experiment results. The potential of this methodology to accurately detect cardiac disease from acoustic signals is illustrated in this study. These discoveries offer promising prospects for improving patient care and medical diagnosis. © 2024 IEEE.

关键词： Cardiovascular Disease Deep learning Deep Neural Classifier EDNCL Random Forest Classifier RFC

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A Comprehensive Review on Cotton Leaf Disease Detection using Machine learning Method 15

A Comprehensive Review on Cotton Leaf Disease Detection usin...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chopkar, Preeti Wanjari, Minakshi Jumle, Pranjali Chandankhede, Pankaj Mungale, Sheetal Shaikh, Mohammad Shahnawaz Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur India

ISBN: (纸本)9798331300579

In India, cotton holds a significant position as a key cash crop. Despite its economic importance, cotton crops are subject to a variety of illnesses that can reduce output and quality. Early detection of these diseases is crucial for minimizing damage and preserving the crop. Cotton is vulnerable to diverse ailments, includes powdery mildew, leaf curl, bacterial blight, target spot, leaf spot, and nutrient deficits. To put effective mitigation methods into place, it is imperative that one correctly recognizes these illnesses. The prevalence of cotton leaf diseases poses a substantial challenge for Indian farmers. This paper proposes the application of machine learning, specifically Convolutional Neural Networks (CNN), for disease detection as a promising solution. Leveraging technology for disease identification can empower farmers to take prompt actions, mitigating the risk of significant crop losses. Machine learning, with its demonstrated success in various domains, including agriculture, offers valuable tools for disease detection and classification. CNNs, known for their proficiency in image-related tasks, can learn to recognize disease-specific patterns in plant leaves. This paper contributes by conducting a comprehensive review of prior research on crop disease detection and introducing an innovative approach that makes use of artificial intelligence techniques to detect illnesses in cotton leaves. © Grenze Scientific Society, 2024.

关键词： Cotton

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Auto Text Correction using NLP Techniques 6

Auto Text Correction using NLP Techniques

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Srivarsha, Nalla Nithin, Gummadi Hariharan, Shanmugasundaram Dash, Bibhuti Bhusan Chowdhury, Subrata Patra, Sudhansu Shekhar Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Deemed to be University Bhubaneswar India Andhra Pradesh Chittoor India

ISBN: (纸本)9798331522667

Auto text correction is a fundamental tool in modern Natural Language Processing (NLP) that upgrades user involvement by decreasing typographical and linguistic errors in composed content. This extends leverages progressed NLP procedures to create a strong and proficient auto-correction framework that precisely predicts and corrects content input errors in real-time. The framework utilizes a combination of deep learning models, such as sequence-to-sequence models and transformers, in conjunction with conventional strategies like n-grams and edit distance algorithms. By analyzing the context and structure of input sentences, the model is able to offer relevant rectifications, making it versatile to different dialects and user composing styles. Moreover, we investigate diverse assessment measurements and compare different calculations to optimize the precision and productivity of the correction system. The arrangement has potential applications in word processors, mobile keyboards, and other content input platforms where high accuracy and low latency are crucial. © 2025 IEEE.

关键词： Deep learning

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Review on Automated Brain Tumor Segmentation using Advanced Deep learning Techniques: Enhancing Precision and Clinical Applicability 3

Review on Automated Brain Tumor Segmentation using Advanced ...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Vishalakshi, V. Arunprasath, T. Rajasekaran, Pallikonda M. Ramaraj, Kottaimalai Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnan Koil India Department of Biomedical Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India

ISBN: (纸本)9798331532420

The incidence of brain tumors has emerged as a significant health concern in contemporary society, primarily due to the production of atypical cells within the brain that can lead to cancerous growths. Among the several types of BT, gliomas represent the most common form found in adults. Traditionally, the diagnosis of such tumors has relied on imaging techniques such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, early detection and accurate classification of tumors are vital for improving survival rates, as conventional methods can be time-consuming. In response to this challenge, researchers have been developing advanced segmentation and identification techniques for BT utilizing Machine learning (ML) and Deep learning (DL) methodologies, particularly through the application of Convolutional Neural network (CNN). These innovative approaches enhance diagnostic accuracy and facilitate earlier identification, thereby enabling patients to pursue appropriate treatment options, including chemotherapy, radiation, or surgical intervention, based on medical advice. Various algorithm and models were studied for BT classification and segmentation. We aim to design unused hybrid model for BT segmentation. © 2024 IEEE.

关键词： Chemotherapy

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Machine learning Approach for Detection of Speech Emotions for RAVDESS Audio Dataset 4

Machine Learning Approach for Detection of Speech Emotions f...

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rochlani, Yogesh R. Raut, Anjali B. H. V. P. m'S College of Engineering and Technology Department of Computer Science and Engineering Maharashtra Amravati India

ISBN: (纸本)9798350343670

The most effective technique to convey one's thoughts and actions to another is through the use of emotion. Emotion recognition from one's own voice is the most urgently needed technology right now. Emotions are attached to everything that a person owns. Every customer's feelings might provide light on what they really need from the customer service professional. Since human communication relies heavily on the ability to read one another's emotions, speech emotion recognition is crucial for understanding another person's inner workings. The time and frequency-based acoustic characteristics integrated with machine learning classifiers that can categorize emotions to enhance the performance of the proposed intelligent system. This research makes use of the audio records available in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Emotion is gleaned from speech via temporal and spectral characteristics. Three machine learning classifiers, the SVM, DT, and RF classifiers, are then trained on the retrieved data. The RF classifier achieved 88.78% higher accuracy rate than the state-of-the-art approaches. © 2024 IEEE.

关键词： Classification Decision Tree Emotion recognition Mel frequency cepstral coefficients Pitch Random Forest Classifier Short term energy Support Vector Machine

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