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检索条件"任意字段=5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023"

共 2608 条记录，以下是41-50 订阅

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A Comprehensive Analysis for Advancements and Challenges in Deep learning Models for Image Processing 5th

A Comprehensive Analysis for Advancements and Challenges in ...

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5th international conference on data science, machine learning and applications

作者： Ch, Ravikumar Chary, Kalvog Prakasha Srinivas, S. Bhavani, Tedla Veeranna Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Cyber Secur Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Sri Indu Coll Engn & Technol Dept Informat Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. the rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of image processing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional Neural Networks (CNNs), clarifying their theoretical foundations within the image processing context, opening the door for further research. CNNs are notably essential for image processing due to their ability to handle complex images effectively.

关键词： Deep learning Image processing Backpropagation algorithm Convolutional Neural Networks (CNNs) Model structures

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Forecasting the Cultivation in Rural Area Using machine learning Techniques 5th

Forecasting the Cultivation in Rural Area Using Machine Lear...

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5th international conference on data science, machine learning and applications

作者： Ahad, Abdul Bujjibabu, L. Prasad, K. Surya Ram Raju, K. Basava Raghavender, K. V. Anurag Univ Dept Artificial Intelligence Hyderabad India DNR Coll Engn & Technol Dept CSE Bhimavaram India G Narayanamma Coll Engn & Technol Dept CSE Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Development in agriculture is generally impacted by an assortment of soil boundaries, including nitrogen, phosphorus, and potassium as well as harvest rotation, soil dampness, and surface temperature. Innovationwill demonstrate valuable for agribusiness, expanding rural efficiency and furnishing ranchers with more prominent harvests. the recommended thought offers a good agriculture arrangement by watching out for the farmland, which can help the ranchers enormously increment output. this framework utilizes techniques to conjecture the most rewarding output given the ongoing climate and soil conditions. Utilizing climate, soil, and past harvest output information, this procedure can likewise help with anticipating harvest output. this can increment harvest creation efficiency and help ranchers over the venture incorporates information from different sources, information examination, and forecast investigation. the machine learning tools can be used to calculate the most productive harvest list or foreseeing the harvest output for a client. To foresee the harvest output, choose machine learning algorithms like Random Forest (RF), Decision Tree (DT), KNN, and MLP Classifier are utilized. Random Forest is the best to obtain more than 90% accuracy.

关键词： Cultivation Decision Tree machine learning MLP Classifier Reinforcement learning

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Leaf Disease Detection and Correction 5th

Leaf Disease Detection and Correction

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5th international conference on data science, machine learning and applications

作者： Indrani, P. Regulwar, Ganesh B. Ahmed, Md Sohel Reddy, K. Sneha Vardhaman Coll Engn Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Pests and plant conditions provide an essential part when assessing the yield as well as the quality of plants. Nowadays plant disease identification is carried out by the deep learning and image processing. Agriculture is the backbone of Indian government. We need to increase the yield in the crop as the population is increasing so the urbanization increases which cause in decrease of cultivation land. Because of the diseases that we observe frequently in plants, the crop production is being decreased. To reduce the disease in plant we need to use some pesticides which also helps to increase in production. In the recent times, deep learning is very useful in the field of image processing. How to exercise deep learning technology to study factory conditions and pests identification has come a exploration conclusion of great company to experimenters. Now we use image processing and find out the disease in plant and also provide the required pesticide suggestions which makes detection of solution for a disease easier and helps in good yield of crop for farmers.

关键词： Leaf disease detection Plant disease detection deep learning image processing convolutional neural networks TensorFlow Keras agricultural technology crop yield pesticide recommendations preprocessing model construction training testing performance evaluation

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A Comprehensive Exploration of Complex Emotions and their Simplification for Enhancing Sentiment Analysis through Explainable AI 5th

A Comprehensive Exploration of Complex Emotions and Their Si...

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5th international conference on data science, machine learning and applications

作者： Sudheer, P. Manoranjini, J. Suman, Naika Reddy, Eedunuri Muralidhar Sridevi, Puligilla CVR Coll Engn Dept CSE AI&ML Hyderabad Telangana India Rajalakshmi Engn Coll Dept Artificial Intelligence & Data Sci Chennai Tamil Nadu India Pallavi Engn Coll Dept CSE DS Hyderabad Telangana India Swami Vivekananda Inst Technol Dept CSE DS Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Sentiment analysis plays a pivotal role in comprehending user emotions within the SocialWeb;however, accurately capturing nuanced sentiment remains challenging. Although deep learning models exhibit impressive performance, their lack of transparency and interpretability hinders their real-world applicability. To address this limitation, we propose an Updated SHAP (SHapley Additive exPlanations) method integrated with Explainable Artificial Intelligence (XAI) as a solution. the research investigates the potential of XAI in enhancing sentiment analysis by designing, evaluating, and utilizing XAI models. the primary focus is on knowledge discovery, exploring how XAI aids in recognizing, interpreting, and simulating human emotions for sentiment analysis tasks. By illuminating the inner workings of sentiment analysis models, this research aims to substantially improve the reliability and utility of sentiment analysis in socio-affective domains. Emphasizing transparency and trust, XAI fosters informed decision- making and enhances sentiment analysis applications amidst the intricate landscape of user emotions on the Social Web.

关键词： Sentiment analysis AI-driven deep learning transparency Explainable Artificial Intelligence (XAI) human emotions prediction accuracy

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A Comprehensive Survey on Enhancing Patient Care through Deep learning and IoT-Enabled Healthcare Innovations 5th

A Comprehensive Survey on Enhancing Patient Care Through Dee...

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5th international conference on data science, machine learning and applications

作者： Valaboju, Sabitha Devi, T. Rupa Devi, D. Gayathri Sudheer, P. CVR Coll Engn Hyderabad Telangana India Keshav Mem Inst Technol Hyderabad Telangana India Vignan Bharathi Inst Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

this study explores recent transformative advancements in deep learning-based healthcare technologies, revolutionizing patient care by enabling personalized precision medicine and extending access through IoT and cyberphysical systems. Diagnostic methodologies for critical conditions like cancer, diabetes, and heart failure are examined, utilizing computational intelligence to enhance patient identification and treatment. the focus lies on electronic health records (EHRs), investigating contemporary deep learning techniques for cancer prediction and framework-based mechanisms for healthcare optimization. Key algorithms like SVMs, Autoencoders, and CNNs are explored, showing applicability in clinical settings and genomic sequence-based diagnostics. Healthcare's unique processing techniques, spanning gene-based strategies, clinical tests, observation, and diagnostic models, are scrutinized for predictive and treatment potential. Integration of statistical and medical references is highlighted for efficient predictions alongside patient-specific data. the research advocates for AI-powered DSS integration, with CNNs as potent tools for customized medical interventions, marking a significant step towards elevated patient care and precision medicine realization.

关键词： Deep learning IoT-driven Healthcare Innovations Electronic Health Records (HER) Computational intelligence

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Predicting the Need for Mental Treatment Across Various Age Groups Using machine learning Algorithm 5th

Predicting the Need for Mental Treatment Across Various Age ...

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5th international conference on data science, machine learning and applications

作者： Pal, Surya Kant Rawat, Himani Roy, Rita Khan, Ameer Lareb Kumari, Nisha Srivastava, Prashasya Sharda Univ Dept Math SSBSR Noida UP India GITAM GITAM Inst Technol Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the extent of mental health is much more than what the general public knows. Many studies have found that women, the elderly, those who had recently survived a disaster, those who worked in the industrial sector, children, adolescents, and persons with long-term medical illnesses were at a higher risk of mental difficulties. Early recognition helps patients treat them more effectively and enhance their lives. Better living conditions, healthcare, and women's empowerment are all necessary. Mental health treatment is required for every age group, from children to seniors. this research will illuminate the need for mental health treatment across different age groups. this prediction is evaluated on the publicly available dataset. the dataset consists of 1259 entries and 27 attributes. Various accuracy metrics were used to evaluate the performance of machine learning techniques in identifying mental health concerns in this study. A comparison of these methods has been done and put into practice and also found that the Logistic Regression method, based on a forecast accuracy of 83.07%, was the most accurate. the machine learning algorithms used in this study include K-NN Classifier, Logistic Regression, Decision Tree Classifier, AdaBoost and Random Forest, which are discussed with their algorithm procedure.

关键词： Mental Health machine learning Logistic Regression Method Random Forest Age

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A Comparative Study of Deep learning Algorithms for Glaucoma Classification Using Retinal Images 5th

A Comparative Study of Deep Learning Algorithms for Glaucoma...

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5th international conference on data science, machine learning and applications

作者： Swapna, T. Varshitha, Y. Sai Raja Sudeepthi, K. L. Manavika, B. Saishree, T. G Narayanamma Inst Technol & Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

A group of eye conditions known as glaucoma impair the optic nerve, which is in charge of sending visual data from the eye to the brain. Glaucoma impacts 3.54% of adults aged 40 to 80 around the world. Early detection of glaucoma is crucial as it can prevent total optic nerve damage, which would cause irreversible vision loss. It is possible for specialists to diagnose glaucoma medically, but treatment options are either expensive or time-consuming and requires ongoing care from medical professionals. there have been numerous initiatives at streamlining all components of the glaucoma categorization process, however these models are challenging for users to comprehend the key predictors, resulting in them being unreliable for use by medical experts. the study uses eye fundus images to classify glaucoma patients using three distinct Deep learning techniques: Convolutional neural network, Visual Geometry Group 16 (VGG16), and Global Context Network (GC-Net). In addition, several data pre-processing techniques are used to avoid overfitting and achieve high accuracy. this research compares and analyses the performance of various architectures using the aforementioned techniques. the CNN model had the best accuracy of 83% when in contrast to the other deep learning models.

关键词： Biomedical image processing Glaucoma Blind ness machine learning Visual geometry group 16 Convolutional neural network Global context network

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Smart Healthcare: Enhancing Patient Well-Being with IoT 5th

Smart Healthcare: Enhancing Patient Well-Being with IoT

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5th international conference on data science, machine learning and applications

作者： Yalla, Swathi Tejah Mamidala, Sanjana Ganji, Deviprasanna Keerthi, G. A. Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad India Vasavi Coll Engn Dept Elect & Commun Engn Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In recent years, due to the increasing incidence of diseases, the importance of health care has been increasingly recognized. Health issues have become important concerns, directly influencing individual quality of life. Neglect of health care management creates problems for the survival of an individual. Among the many applications supported by the Internet of things (IoT), digital healthcare stands out as important. the Internet is a powerful way to integrate health care resources and improve overall quality of life. IoT has revolutionized the healthcare industry, providing doctors with an easier and faster way to access patient data to make informed decisions. these improvements significantly improve health care and patient outcomes. the development and deployment of IoT-based health monitoring systems depend on the integration of sensors and actuators. the system continuously monitors the patient's essential signs using temperature and heart rate sensors, allowing doctors to remotely access this information from their computers and predict the future condition of the patient's health. machine learning algorithms help us calculate the accuracy of future prediction. If there is anything abnormal about the patient's condition, the system automatically sends an alert to the doctor via email. this instant feedback allows the physician to quickly diagnose and address any problems, and help save patient's lives. the main goal of this paper is to provide the physician with timely updates on the patient's health status, ensuring immediate intervention if any abnormal situations happen.

关键词： Internet of things machine learning Sensors Actuators

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Crop Yield Prediction Using Deep learning 5th

Crop Yield Prediction Using Deep Learning

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5th international conference on data science, machine learning and applications

作者： Jeny, J. R. V. Divya, Phulari Varsha, Kolanu Mrunalini, Anantha Irfan, S. K. M. Vignan Inst Technol & Sci Dept Comp Sci & Engn AI&ML Yadadri Bhuvanagiri India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In contemporary agriculture, the accurate prediction of crop yields remains a substantial challenge for farmers. Existing prediction systems are often rigid and tailored to specific crops and regions, leaving many farmers without critical information to make informed decisions in the face of unpredictable weather and environmental variations. Our core mission is to develop advanced models that can accurately forecast crop yields, enabling farmers to make well-informed choices each growing season. By considering factors like soil composition, fertility, weather fluctuations, and seasonal variations, we employ cutting-edge machine learning techniques, including some DL algorithms and architectures that handle sequential data effectively. Our data collection process encompasses a wide array of sources, including weather data, soil quality assessments, and specific crop details. To ensure consistency, we rigorously standardize this data. We further divide our dataset into subsets for assessing models' accuracy using some metrics. Our overarching goal is to bridge the gap in precise crop yield prediction, providing farmers with a practical tool to make informed decisions about crop selection in an ever-changing agricultural landscape. through the use of advanced deep learning techniques and data integration from diverse sources, we strive to meet the evolving demands of agriculture in today's dynamic world.

关键词： Yield Prediction Deep learning 1D-CNN LSTM GRU

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E2M: Emerge Energy Management Framework for Multimedia data Transmission in WMSNs 5th

E2M: Emerge Energy Management Framework for Multimedia Data ...

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5th international conference on data science, machine learning and applications

作者： Janakamma, Ch. Hegde, Nagaratna P. Osmania Univ Hyderabad India Vasavi Coll Engn Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Wireless Multimedia Sensor Networks (WMSN) are autonomous, limited energy and distributed multimedia sensor which can transmit the multimedia data through communication networks. In WMSN, sensor nodes sense data and capture multimedia data. the WMSN deployed in many applications such as surveillance, healthcare, military applications and etc., the enhancement of high speed communication in WMSN is increasing demand for multimedia services and applications. the WMSN mostly used for transmission of multimedia services such as audio and video. the volume of multimedia data is increase vastly as well as utilization of bandwidth and energy also increase. the limited energy WMSN exhaust rapidly and its leads to network failure. Previous extensive research carried out to maximize throughput by the optimal utilization of network bandwidth. Also improved QoS by the novel routing algorithm in WMSN. To address the limitation of existing frameworks, in this research paper proposed an emerging energy management framework to reduce energy utilization and improve network life time. the proposed E2M framework implementation is made using NS2. the empirical results shown that proposed framework outperforms with comparison of existing frameworks. the performance evaluated in terms of PDR, Delay, throughput and Energy. the proposed E2M Framework reduce 5.74 ms of network delay, improved 5.3% of PDR, throughput improved to 77KB and reduce energy consumption of 0.39 kJ.

关键词： Multimedia Application Energy Efficient Network Lifetime Energy Management Quality of Service (QoS)

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