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Voice-based Image Captioning using Florence-2

Voice-based Image Captioning using Florence-2

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Devanshi Mittal Prashant Ahlawat Tushar Nag Om Brahma Shrimaroj Computer Science and Engineering Chandigarh University Mohali India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

As visual information becomes the predominant tool for passing information in the society, people with visual impairment have a difficult time in comprehending images. This paper aims to meet this challenge by proposing a machine learning enabled method of producing descriptive text captions for images in a bid to provide a link to comprehend the visual content. This solution will involve processing and translating visual information into meaningful textual accounts in order to offer the visually impaired a way of gaining better understand and a more effective way of participating in the visual environment. The first strategy used a conventional image recognition technique, while the second strategy improved the method to improve captioning accuracy. This process was done not with the help of the old dataset but with the help of a new one in order to get improved results. Future work will concentrate on enhancing the system for special application of real-time captioning so that it can be easily used in real life.

关键词： Visualization Image recognition Translation Visual impairment signal processing Predictive models Multitasking Real-time systems Object recognition Videos

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ICREST 2021 - 2nd international conference on Robotics, Electrical and signal processing Techniques

ICREST 2021 - 2nd International Conference on Robotics, Elec...

引用

2nd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2021

ISBN: (纸本)9781665415767

The proceedings contain 153 papers. The topics discussed include: designing of a handbag for women safety;a web based four-tier architecture using reduced feature based neural network approach for prediction of student performance;voice activated portable braille with audio feedback;transient stability enhancement of power system by virtual synchronous generator control of battery;blockchain-based peer-to-peer sustainable energy trading in microgrid using smart contracts;treatment of water algae by water surface discharge plasma;nicotine sensing by photonic crystal fiber in THz regime;electronic medical record data sharing through authentication and integrity management;improved machine learning based classification model for early autism detection;and an adaptive graph cut algorithm for spammer group detection from weighted one mode projection of bipartite graph.

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Formulation of Sensor Ranking Associated in Categorical Perception: A Pilot Study Using machine learning 6th

Formulation of Sensor Ranking Associated in Categorical Perc...

引用

6th Kuala Lumpur international conference on Biomedical Engineering, BioMed 2021

作者： Bakar, Abdul Rauf A. Lai, Khin Wee Hamzaid, Nur Azah Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9783030907235

The concept of categorical perception has been enormously investigated to digitalize the process of auditory processing when perceiving speech stimulus at higher brain neurological signal. Despite the nature non-stationary property of electroencephalography (EEG) during any task processing, scientists and clinicians find it to be not well fitted in the healthcare setting application without professional monitoring, and even worst, the hearing aid device functionality was also reported at a low success rate. In this study, we embraced machine learning technology and extracting contribution into our auditory research area. The present pilot work aims to create a robust computational framework to formulate the sensor ranking principle in auditory speech perception. The ranking for sensors could facilitate in identifying the minimal set sensor-of-interest (ROI) that are sufficient in specific auditory task processing using an optimally trained model. The trained Support Vector machine (SVM) highest performance reported at random 2 training dataset with scoring of 92.3% using 70% triple-random training dataset. Based on the sensor ranking, the CZ electrode outperformed the other electrodes with scoring of 96.74%, followed by PZ and FPZ for the 2nd and 3rd rank (95.66% and 95.34% respectively). Our pilot study anticipated that the sensor ranking formula able to underline more precise neural correlates based on current auditory categorical perception response. The excellence sensor ranking in delivering a minimal set of sensor-of-interest (SOI) drive the capability of the SVM model in classifying auditory brain response in high-performance prediction metrics and possible reliability in the healthcare setting application. © 2022, Springer Nature Switzerland AG.

关键词： Support vector machines

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Non-Contact Breath Rate Classification Using SVM Model and mmWave Radar Sensor Data

Non-Contact Breath Rate Classification Using SVM Model and m...

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Cyber Physical Systems, Power Electronics and Electric Vehicles (ICPEEV), international conference on

作者： Mohammad Wassaf Ali Ayushi Gupta Mujeev Khan Mohd Wajid Department of Computer Engineering Aligarh Muslim University Aligarh India Department of Electronics Engineering Aligarh Muslim University Aligarh India

ISBN: (数字)9798350388664

ISBN: (纸本)9798350388671

This work presents the use of frequency-modulated continuous wave (FMCW) radar technology combined with a machine learning (ML) model to differentiate between Normal and Abnormal Breath Rates (BR). The recommended system non-contactly collects data (or signals) using FMCW radar, which depends on BR. Various Support Vector machine (SVM) kernels are used to classify the observed data into Normal and Abnormal states. Prolonged experiments show good accuracy in BR classification, confirming the model's efficacy. The best accuracy is 95% with the smallest number of support vectors in the case of the quadratic polynomial kernel.

关键词： Support vector machines Performance evaluation Accuracy Computational modeling Electromagnetic interference Radar machine learning Radar signal processing Polynomials Kernel

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Efficacy of Data Governance a Cutting Edge Approach to Ensuring Data Quality in machine learning for Banking Industry

Efficacy of Data Governance a Cutting Edge Approach to Ensur...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Saisuman Singamsetty Data Management Specialist Americo Life Inc San Antonio TX USA

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Data governance is a key factor that supports data quality in any machine learning application in banking, especially those that involve PCA and DT. We investigate the effectiveness of data governance, a new technique for bolstering the reliability and performance of these models. Strong data governance frameworks allow banks to take a more proactive approach to enhancing data quality via standardization, cleansing, and more dynamic monitoring processes. In turn, this results in more accurate and interpretable machine learning models that are key to decision-making, risk assessment, and regulatory compliance. Too many studies have now demonstrated that institutions that have strong governance practices outperform weak governance institutions in the successful deployment of advanced ML techniques such as PCA & DT by 2.5 times with improvements in model accuracy of up to 15%. Additionally, data governance improves the explained variance of principal components by 25% and decreases false positives in fraud detection models by 35%. Furthermore, it is essential to keep the model rigorous, allowing transparency and interpretability, which helps to provide regulatory compliance. Data governance is only as good as its ability to bring about change. New technologies, such as AI-powered data quality monitoring and blockchainin for data lineage, will drive the need for data governance as machine learning in banking matures.

关键词： Industries Accuracy Data integrity machine learning Banking Data models Data governance Reliability Monitoring Principal component analysis

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Boosting Agricultural Productivity Using IoT and machine learning-Based Crop Recommendation

Boosting Agricultural Productivity Using IoT and Machine Lea...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Asish Kumar Patnaik Neelamadhab Padhy Archana Patnaik Rasmita Panigrahi Department of Computer Science and Engineering GIET University Gunupur Odisha

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Crop production is crucial to the country's economic development by promoting sustainable agriculture. Integrating IoT and machine learning into crop recommendation systems widely contributes towards enhancing product yield, and minimizing farmers' efforts. The correct forecasting of crop recommendation may give a perfect outcome in analyzing the crop features based on its parameters. Objectives: This work aims to improve agricultural productivity using classification models. Our prime objective is to identify the specific parameters like soil productivity, humidity, wind velocity, rainfall, and other climatic conditions that affect the growth of crops. Classification-based prediction provides accurate results based on the optimality of crop characteristics. Materials and Methods: Our hybrid research model shows the efficiency of machine learning and IoT in the domain of crop recommendation systems. We have taken the real-time data of Nitrogen(N), Phosphorus(P), and Potassium(K) values of soil by using the NPK Soil Sensor. The real-time data collection was done at GIET University Agricultural Fields (School of Agriculture), India. The data is further converted into a. csv file for further processing. We have implemented five different types of classification techniques which include Gradient Boosting, Decision Tree, Random Forest, Logistic Regression, and Support Vector machine(SVM). Result Analysis: The effectiveness of all the different kinds of classification algorithms is assessed using the obtained results. We have estimated the performance metrics (accuracy, precision, F-measure, and recall) of all the classifiers.

关键词： Productivity machine learning algorithms Crops signal processing algorithms Soil Boosting Real-time systems Classification algorithms Random forests Recommender systems

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Large Language Model based Personalized learning Assistant for Career-Oriented Skills

Large Language Model based Personalized Learning Assistant f...

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2nd international conference on signal processing, Communication, Power and Embedded Systems, SCOPES 2024

作者： Hemalatha, K. Deepika, V. Mallika, R.M. Babu, Kona Mahesh Madanapalle India Sri Venkateswara College of Engineering Dept. of Cse Tirupati India Siddartha Institue of Engineering and Technology Dept. of Cse Puttur India

ISBN: (纸本)9798331506452

Large Language Models (LLMs) are recently emerged as a variant of Artificial Intelligence algorithms in Natural Language processing task such as analyzing extensive datasets, generating, summarizing content and predicting new information. LLMs fall under Generative AI, designed specifically for text generation. Their exceptional training and numerous model parameters significantly enhance the LLMs capacity to mimic human-like performances in natural language understanding. The advent of LLMs has revolutionized the interaction between humans and machines. In contrast to conventional recommendation systems and search engines, LLMs are the adoptive active user engagement. This interactive capability opens up new possibilities for the users towards personalization, enabling the provision of personalized services based on individualized information. Despite their potential, the applications of LLMs in personalization remain largely unexplored. Consequently, this study explores into the domain of personalization within education, aiming to uncover the potential contributions of Large Language Models (LLMs). In the context of this research, an innovative personalized LLM-based Human machine Interface is crafted to individualize learning experiences for each learner. This system is adept at crafting personalized learning plans and adjusting learning materials accordingly. Additionally, it formulates assessments to analyze user strengths and weaknesses. These assessments undergo automatic evaluation, offering learners instant feedback. This feedback mechanism allows learners to promptly identify and understand their mistakes, fostering a more efficient learning process and aiding them in successfully completing their courses. The proposed personalized LLM based Assistant enhances the learner's unique journey by creating adaptive leaning plans, real time assessments with instant feedback. © 2024 IEEE.

关键词： Search engines

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Integrating FOC and SVPWM for Enhanced Induction Motor Control with machine learning Insights

Integrating FOC and SVPWM for Enhanced Induction Motor Contr...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Subhasri Satpathy Sudhansu Kumar Samal Tanushree Mistry Department of Electrical Engineering Centurion University of Technology and Management Odisha India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

This paper presents the design and implementation of Field-Oriented Control (FOC) integrated with Space Vector Pulse Width Modulation (SVPWM) for managing a 4 kW, 50 Hz squirrel-cage induction motor. The system effectively regulates rotor speed despite load disturbances, with significant responses observed at 3 and 7 seconds, marked by oscillations and instability. The actual $i_{sq}$ current stabilizes over time, closely tracking the reference current, while rotor flux remains stable with spikes reflecting load changes. Torque responses demonstrate oscillations characteristic of an under-damped system but stabilize post-disturbance. Line-to-line voltages confirm efficient PWM signal generation, maximizing DC bus voltage utilization. Performance validation through machine learning models reveals varied accuracy scores: Linear Regression (72%), Polynomial Regression (85%), Decision Trees (83%), Random Forest (92%), Support Vector Regression (88%), and Neural Networks (96%). These findings highlight the effectiveness of FOC and SVPWM, enhanced by machine learning, in improving the operational efficiency of induction motors under dynamic conditions.

关键词： Space vector pulse width modulation Support vector machines Accuracy Induction motors Neural networks Rotors Polynomials Oscillators Regression tree analysis Random forests

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Autologous Blood Transfusion Automated Model Using Semi-intraoperative Cell Salvaging Technique 2nd

Autologous Blood Transfusion Automated Model Using Semi-intr...

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2nd international conference on signal and Data processing, ICSDP 2022

作者： Rushambwa, Munyaradzi Charles Mythili, Asaithambi Palanniappan, Rajkumar Govindaraj, P. Chimonyo, Kudzai Bradley Sensors and Biomedical Engineering Vellore Institute of Technology Vellore India Mechatronic Engineering University of Technology Bahrain Salmabad Bahrain Artificial Intelligence and Machine Learning Vivekanda College of Engineering and Technology Puttur India Electronic Engineering Harare Institute of Technology Harare Zimbabwe

ISBN: (纸本)9789819914098

Inaccessibility to proper healthcare in remote areas is one of the problems faced by patients needing blood during emergency surgeries. Unavailability of blood products and proper blood storage facilities still are among some of the major challenges faced in remote surgical outreach camps. This then needs a model system that can be used to conserve blood oozing out from the patient during surgical procedures. One such method implored in this work is an autologous blood transfusion system that can be used for emergency and trauma operations using Intraoperative Cell Salvage Technique (ICS). ICS is used where it is difficult to get matched blood or when there are high chances of Transfusion Transmittable Infections (TTIs) in using allogenic blood. Blood is collected from a surgical site using an aspirator and passed through various stages of filtering and cleaning where a solution of 3.2% sodium citrate is used as an anticoagulant. Time taken from aspiration to reinfusion through is reduced through an automated process, effectively avoiding storage and centrifugation processes which take more time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Salvaging

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A classification study of pulmonary nodule CT images based on supervised contrast learning 2

A classification study of pulmonary nodule CT images based o...

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2nd international conference on Image processing, Computer Vision and machine learning, ICICML 2023

作者： Wang, Nan Gu, Yu Zhang, Xiangsong Jia, Chengyi He, Qun Inner Mongolia University of Science and Technology School of Information Engineering Inner Mongolia Baotou014010 China BOE Technology Group Co. Ltd Beijing100176 China China Second Metallurgy Group Corporation Limited Inner Mongolia Baotou014010 China

ISBN: (纸本)9798350331417

With the increasing amount of pulmonary odule CT images and the difficulty of labeling categories, traditional deep learning methods that rely on labeled samples may not be able to learn effectively. To address these challenges, this paper proposes a 3D convolutional network for pulmonary nodule feature extraction using contrastive learning. The proposed method consists of two phases: discriminative feature learning and classification. In the discriminative feature learning phase, a supervised contrast learning loss is used to reduce the distance between nodules in the same class while increasing the distance between nodules in different classes, thus improving intra-class diversity and inter-class similarity. In the subsequent classification phase, pre-training weights are fine-tuned to better fit the LUNA16 dataset. The proposed network achieves an accuracy of 86.96, an F1-score of 81.28, and a mean G-mean of 83.86 on the LUNA16 dataset. Compared with existing supervised learning methods, introducing contrastive learning allows for the full use of labeled data and improves the classification accuracy of lung nodules with the same amount of training data. © 2023 IEEE.

关键词： Classification Contrastive learning Convolutional network Supervised learning

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