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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, SPML 2020"

共 729 条记录，以下是81-90 订阅

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An Integrated Approach of MCDM Methods and machine learning Algorithms for Employees' Churn Prediction 3

An Integrated Approach of MCDM Methods and Machine Learning ...

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3rd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Chowdhury, Shefayatuj Johara Mahi, Mainul Islam Saimon, Sadiqul Alam Urme, Aynur Nahar Nabil, Rashidul Hasan International Islamic University Chittagong Department of Cse Chittagong Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh International Islamic University Chittagong Department of Eee Chittagong Bangladesh

ISBN: (纸本)9798350346435

Employee churn is a notable nuisance for organizations to maintain a cost-effective position and brand strategy. This research has proposed an integrated approach to overcome this issue systematically. All employees in this study were divided into three categories using a combination of machine learning algorithms and Multi-Criteria Decision Making (MCDM) techniques. The techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytical Hierarchy Process (AHP) have been consolidated. The dataset's feature importance was retrieved, and AHP was used to determine the criteria that caused the most turnover. TOPSIS was given the derived criterion weights from AHP to rank every personnel according to their propensity to depart the organization. To estimate staff turnover, seven machine learning algorithms were applied. After comparing the results, the Random Forest algorithm produces the best accuracy for assessing employee churn. © 2023 IEEE.

关键词： Decision making

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A machine learning Model on Healthcare Based Chatbot and Appointment System 3

A Machine Learning Model on Healthcare Based Chatbot and App...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Srivastava, Kushagra Pandey, Trilok Nath Roy, Debaraj Sahoo, Sambit Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350320749

This paper discusses the clinical chatbot which could examine the contamination and deliver essential insights regarding the contamination previous to counseling a specialist. To lower the healthcare charges and similarly increase openness to clinical facts the clinical chatbot is constructed. Certain chatbots pass approximately as clinical reference books, which facilitates the affected person to discover approximately their contamination and assists with running on their fitness. The person can accomplish the real gain of a chatbot simply while it is able to examine all forms of infections and deliver essential data. In later sections of the paper, a doctor appointment system software is also discussed that is integrated with the chatbot so that the specialist which is recommended by medical chatbot can be easily visited at the mutually agreed time of both specialist and patient. Such an appointment system has its own advantages like it reduces the waiting time of the patient, patients can choose the appointment time in keeping with their desire additionally to be had and booked slots are proven in powerful graphical person interface. © 2023 IEEE.

关键词： Health care

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Advancements in Plant Leaf Disease Classification: Integrating machine learning and Graph Convolutional Networks for Sustainable Agriculture 3

Advancements in Plant Leaf Disease Classification: Integrati...

引用

3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Rahul Chiranjeevi, V. Dhanasekaran, S. Murugan, B.S. Senthil Pandi, S. Rajalakshmi Engineering College Department of Cse Chennai India Kalasalingam Academy of Research and Education Department of It Krishnankoil India M.Kumarasamy College of Engineering Department of Cse Karur India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361186

In order to support life on Earth and preserve ecological equilibrium, plants are essential. However, a variety of biotic and abiotic elements constantly pose a threat to them;plant leaves are especially susceptible to illness. The ethology of plant leaf diseases, their consequences on plant health, and mitigation strategies are mined in this research. The categorization of plant leaf diseases both infectious and noninfectious the main topic of discussion. These illnesses threaten global foo d security and ecosystems because of their sensitivity to infections, the environment, and genetics. The significance of effective disease management is emphasized in the article, which takes into account elements like sustainable practices, data driven disease forecasting, and precision agriculture. The application of Artificial Intelligence (AI) and machine learning (ML) is touted as a potent technique for classifying plant leaf diseases. The proposed approach, which makes use of graph convolutional networks, offers a fresh way to get around current problems. The importance of high-quality datasets for creating precise decision support systems is covered in the study. It is emphasized how adaptable ML models are, enabling ongoing learning and the identification of novel illnesses. Notwithstanding the progress made, obstacles including class imbalance and massive data problems are recognized, highlighting the necessity of meticulous experimental planning and model training. © 2024 IEEE.

关键词： Convolution

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3rd international conference on machine learning, Image processing, Network Security and Data Sciences, MIND 2021

3rd International Conference on Machine Learning, Image Proc...

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3rd international conference on machine learning, Image processing, Network Security and Data Sciences, MIND 2021

ISBN: (纸本)9789811958670

The proceedings contain 68 papers. The special focus in this conference is on machine learning, Image processing, Network Security and Data Sciences. The topics include: Analyzing Wearable Data for Diagnosing COVID-19 Using machine learning Model;comparative Analysis of Classification Methods to Predict Diabetes Mellitus on Noisy Data;a Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print;region Classification for Air Quality Estimation Using Deep learning and machine learning Approach;neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early Warning;detection of Credit Card Fraud by Applying Genetic Algorithm and Particle Swarm Optimization;traditional Indian Textile Designs Classification Using Transfer learning;classification of Electrocardiogram signal Using Hybrid Deep learning Techniques;automated Detection of Type 2 Diabetes with Imbalanced and machine learning Methods;fault Diagnosis in Wind Turbine Blades Using machine learning Techniques;real-Time Detection of Vehicles on South Asian Roads;stock Market Prediction Using Ensemble learning and Sentimental Analysis;Multiple Feature-Based Tomato Plant Leaf Disease Classification Using SVM Classifier;a Methodological Review of Time Series Forecasting with Deep learning Model: A Case Study on Electricity Load and Price Prediction;unexpected Alliance of Cardiovascular Diseases and Artificial Intelligence in Health Care;a Novel Smartphone-Based Human Activity Recognition Using Deep learning in Health care;An Enhanced Deep learning Approach for Smartphone-Based Human Activity Recognition in IoHT;classification of Indoor–Outdoor Scene Using Deep learning Techniques;prediction of the Reference Evapotranspiration Data from Raipur Weather Station in Chhattisgarh using Decision Tree-Based machine learning Techniques;deep Transfer learning and Intelligent Item Packing in Retail Management;preface.

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A signal processing and machine learning Framework for Enhanced TDS Simulation 3

A Signal Processing and Machine Learning Framework for Enhan...

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3rd IEEE international conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Shuvo, Shuvendu Pal Pal Shibazee, Shirshendu Khuki, Arifa Akter Das, Chaitee Paul, Goutam Mita, Mitaly Paul Khulna University of Engineering and Technology Dept. of CE Khulna9203 Bangladesh Bangladesh University of Engineering and Technology Dept. of EEE Dhaka1000 Bangladesh Khulna University of Engineering and Technology Dept. of MSE Khulna9203 Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Dept. of Pharmacy Gopalganj8100 Bangladesh Khulna University Dept. of Economics Khulna9208 Bangladesh

ISBN: (纸本)9798331534400

Total dissolved solids (TDS) is one of the most significant indicators of regulated wastewater effluents, and it is critical in assessing and improving the performance of any wastewater treatment facility. The literature review reveals that the pattern of TDS and other water quality parameters generally shows noisy and complicated features;hence, capturing their pattern is tough. This paper proposes a new approach to noise reduction by Ensemble Empirical Mode Decomposition (EEMD), incorporating machine learning algorithms like Artificial Neural Network (ANN) and Support Vector machine (SVM) to improve the accuracy of TDS prediction. The data on temperature, pH, turbidity, and TDS were collected from the Bangabandhu Water Treatment Plant (BWTP) in Khulna, Bangladesh. In the current work, traditional and EEMD-based approaches were developed, and the performances of these were evaluated based on MSE, RMSE, and MAE. It is presented that the EEMD-based approach reduced the error considerably over the traditional models, while a maximum reduction of 89.46% and 91.04% in MSE are achieved in ANN and SVM, respectively. It further suggested that the proposed approaches could be applied to other wastewater treatment plants. Practical deployment of this model will help the operators make real-time decisions in optimized treatment processes. © 2024 IEEE.

关键词： Empirical mode decomposition

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Diagnosing Faults of Reciprocating Air Compressor (RAC) Setup Using signal processing Technique and machine learning Approach 3rd

Diagnosing Faults of Reciprocating Air Compressor (RAC) Setu...

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Dhakar, Atul Singh, Bhagat Gupta, Pankaj Jaypee University of Engineering and Technology Madhya Pradesh Guna India

ISBN: (纸本)9789819749461

This paper presents a method for identifying RAC faults using acoustic signals obtained from both healthy and unhealthy conditions. The entire procedure is carried out with microphones. Accumulated one healthy and seven unhealthy signals of RAC setup processed using unconventional method of signal processing called "local mean decomposition" (LMD). Additionally, the ‘6’ statistical properties (SP) have been evaluated in order to extract features: mean (US), variance (σs2), root square of mean (Mrms), root amplitude of mean (Mrma), absolute amplitude of mean (Mama), and kurtosis index (Ki). Extracted fault features are classified using lazy learning-based (LLB) classifiers. It has been observed that from different types of LLB classifiers, K-star classifier is quite accurate and has higher accuracy (93.05%) as compared to the other two classifiers accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reciprocating compressors

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Real-time Audio Recognition for Hearing Impaired 3

Real-time Audio Recognition for Hearing Impaired

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Peddi, Muralidhar Vardhan, S H V Harsha Peddi, Giridhar Ponnam, Satwik Rani, C. Rajesh Kumar, M. Vellore Institute of Technology School of Electronics Engineering Vellore India University of California Riverside Electrical Engineering Riverside United States Arizona State University Computer Science Tempe United States Vellore Institute of Technology School of Electrical Engineering Vellore India

ISBN: (纸本)9798350372120

Audio recognition technology has seen significant advancements in recent years, offering promising solutions to improve communication and accessibility for deaf individuals. This paper explores the landscape of audio recognition technologies tailored specifically for the deaf community. It delves into the challenges faced by deaf individuals in everyday communication scenarios and how innovative audio recognition systems can mitigate these challenges. The abstract discusses various approaches, including speech-to-text systems, sound recognition algorithms, and real-time captioning tools, highlighting their effectiveness and limitations in facilitating seamless communication for deaf individuals. Furthermore, it examines the integration of machine learning and artificial intelligence techniques in enhancing the accuracy and adaptability of audio recognition systems for diverse environments and user preferences. Additionally, the abstract explores the potential socio-economic impacts of the widespread adoption of audio recognition technologies in empowering deaf individuals, fostering inclusion, and promoting equal opportunities in education, employment, and social interactions. © 2024 IEEE.

关键词： Audio signal processing

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Predicting Phishing Emails and Websites to Fight Cybersecurity Threats Using machine learning Algorithms 3

Predicting Phishing Emails and Websites to Fight Cybersecuri...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Divakarla, Usha Chandrasekaran, K. N M A M Institute of Technology Karkala Nitte India National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9798350319125

Phishing attempts, which try to fool people into giving attackers valuable information like login credentials, credit card numbers, and personal data, have grown more frequent and sophisticated over time. According to the 2021 Verizon Data Breach Investigations Report, phishing attempts were the cause of 36% of data breaches in 2020, up from 25% in 2019, and 96% of these assaults were sent by email. A mix of user education, technical controls, and automated detection systems can be used to prevent phishing attempts, which are crucial for preserving cybersecurity. Since phishing efforts are continually changing and growing more complex, machine learning and deep learning techniques are very useful for detecting them. Extreme gradient boosting (XGBoost) and Random Forest algorithms were utilised in this work to construct automated models for spotting phishing emails and links. These algorithms had accuracy rates of 98.57% and 96.7%, respectively. These models enable organisations and people to proactively recognise and stop phishing assaults, lowering the risk of monetary losses and data breaches. © 2023 IEEE.

关键词： Deep learning GAP analysis Information security machine learning algorithms Phishing Email detection Phishing websites Natural language processing

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Enhanced Pneumonia Detection Through Cough Analysis Techniques 3

Enhanced Pneumonia Detection Through Cough Analysis Techniqu...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Ramesh Babu, A. Sathvika, V. Junaid, Abdul Mannan Abhi Ram, S. Reddy, V.Akhila Shiva Shankar Reddy, K. SR University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350343632

Millions of individuals worldwide are afflicted with pneumonia, a potentially fatal respiratory infection that is an inflammatory disease that affects the air sacs in the lungs. For prompt intervention and successful treatment, pneumonia must be identified early. The proposed approach analyzes cough sounds and recognizes patterns linked to pneumonia by using 1D-CNN and sophisticated signal processing techniques. One promising non-invasive, low-cost method for detecting pneumonia may be cough sound analysis. A plethora of information about the respiratory system can be found in cough sounds, and recent advances in machine learning have made it possible to create accurate and trustworthy frameworks for the detection of pneumonia from cough sounds. Our results propose that cough sound analysis has the potential to be a valuable apparatus for pneumonia detection in resource-limited settings. Our framework is simple to utilize and does not require any specialized equipment, making it perfect for utilize in community health clinics and other inaccessible areas. The algorithm showed 91.35% test accuracy for the 82 cases of cough sound in pneumonia group and 91 cases in non-pneumonia group. © 2023 IEEE.

关键词： Audio processing Cough Analysis Deep learning Feature Extraction machine learning Pneumonia Detection

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signal processing Grand Challenge 2023 - E-Prevention: Sleep Behavior as an Indicator of Relapses in Psychotic Patients 48

Signal Processing Grand Challenge 2023 - E-Prevention: Sleep...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Avramidis, Kleanthis Adsul, Kranti Bose, Digbalay Narayanan, Shrikanth University of Southern California Signal Analysis and Interpretation Lab Los AngelesCA90089 United States

ISBN: (纸本)9781728163277

This paper presents the approach and results of USC SAIL's submission to the signal processing Grand Challenge 2023 - e-Prevention (Task 2), on detecting relapses in psychotic patients. Relapse prediction has proven to be challenging, primarily due to the heterogeneity of symptoms and responses to treatment between individuals. We address these challenges by investigating the use of sleep behavior features to estimate relapse days as outliers in an unsupervised machine learning setting. We extract informative features from human activity and heart rate data collected in the wild, and evaluate various combinations of feature types and time resolutions. We found that short-time sleep behavior features outperformed their awake counterparts and larger time intervals. Our submission was ranked 3rd in the Task's official leaderboard, demonstrating the potential of such features as an objective and non-invasive predictor of psychotic relapses. © 2023 IEEE.

关键词： Anomaly Detection Biosignals Psychotic Relapses Sleep Behavior Unsupervised learning

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