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SPEAR: Design and Implementation of an Advanced Virtual Assistant

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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Sustainable Expert Systems (ICSES), international conference on

作者： Garima Jain Amita Shukla Nitesh Kumar Bairwa Anamika Chaudhary Ashish Patel Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence and Machine Learning Dronacharya Group of Institutions Greater Noida India Department of Computer Science and Enginnering Netaji Subhash University of Technology Delhi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

this research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. these findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services.

关键词： Accuracy Personal voice assistants machine learning Information retrieval Natural language processing Real-time systems Robustness Text to speech Time factors Web search

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An unsupervised discriminative random vector functional link network for efficient data clustering 4

An unsupervised discriminative random vector functional link...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Zhang, Yikai Zhu, Qi Peng, Yong Kong, Wanzeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Provincial Key Lab. for Computer Information Processing Technology Soochow University Suzhou215123 China

ISBN: (纸本)9781665413220

Random Vector Functional Link (RVFL) is a single hidden layer feed forward network which can be trained with non-iterative learning methods. Concretely, the input weights and hidden biases in RVFL can be randomly generated and then the calculation of the output weights can be reduced to solve a regularized least squares regression formula. though RVFL network obtained promising performance in diverse applications, most of its current variants work mainly for supervised tasks such as classification and regression while less effort was made to utilize the unlabeled data. In this paper, we propose an unsupervised RVFL model by simultaneously considering the local manifold structure and the global discriminative information of data. the newly formulated unsupervised discriminative RVFL (UDRVFL) model can be efficiently solved by handling an eigen-value decomposition operation. We conduct experiments by comparing UDRVFL with several state-of-the-arts on representative benchmark data sets and the results demonstrate that it has very competitive performance in data clustering. © 2021 IEEE.

关键词： Iterative methods

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Classification of Flower dataset using machine learning Models

Classification of Flower Dataset using Machine Learning Mode...

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Artificial Intelligence and Speech Technology (AIST), international conference on

作者： Tina Gupta Puja Arora Ritu Rani Garima Jaiswal Poonam Bansal Amita Dev Computer Science Engineering Indira Gandhi Delhi Technical University for Women Delhi India Center of Excellence - Artificial Intelligence Indira Gandhi Delhi Technical University for Women Delhi India Computer Science Engineering Bennet University Noida Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India

Modern day machine learning aims to categorize data based on developed models and predict future outcomes according to these models. Today machine learning finds its application in various fields such as facial recognition, speech recognition, medical diagnosis for example predicting potential heart failure, sentiment analysis, product recommendations etc. this paper proposes 3 classification models to efficiently predict the Iris flower species. the proposed model uses Exploratory data Analysis (EDA) to analyse and pre-process the dataset and the prediction is performed by the three classification models namely- "Logistic Regression, Support Vector machine (SVM) and K-Nearest Neighbours (KNN)". All the proposed models are tested on Iris dataset and achieved maximum accuracy of 96.43, 98.21 and 94.64 percent respectively. this paper provides a thorough analysis of the various supervised machine learning models that are suitable for predicting the species of Iris flower based on the various attributes like sepal width, sepal length, petal width and petal length.

关键词： Analytical models Sentiment analysis data analysis Support vector machine classification machine learning Speech recognition Predictive models

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Web Services Recommendation system using machine learning Algorithms

Web Services Recommendation system using Machine Learning Al...

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Emerging Technology (INCET), international conference for

作者： Shrikant Dnyandeo Bhopale Alok Sahu Kishor K. Pandyaji Kalinga University Raipur India PVPIT Budhgaon India

the ability to forecast and propose the most relevant digital things to the end users is a key use of data analytics. Product, book, movie, news, and other types of recommendation systems are only a few examples. Recommending websites is a particularly difficult task in the field of service computing. In today's information-rich online environment, web sites are suggested based on the aggregated hit rate at which they were accessed by various people. It's possible this won't be the best fit for the user's query. thus, sophisticated recommendation engines are created, which utilise machine learning algorithms to foresee the user's interests. Hence, recommendation process-related web page customization has emerged. To accomplish customization, it is necessary to do research centred on the user's navigational habits and access pathways as they have been used before. this information is used to build a profile of the user, which is then used to provide more tailored recommendations. Innovative algorithms are being developed to enhance the effectiveness of recommender systems. Collaborative filtering, Content-based approach, Association rule mining, Clustering, Classification, Hybrid systems, Evolutionary and Swarm intelligence based algorithms, and so on are all examples of the types of algorithms used by pre-existing recommendation models. Cold start problem, popularity bias, sparsity, time consumption, inability to handle dynamic web sites, and lack of personalisation is only some of the issues with current models.

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machine learning-Based Research on Reserve Prediction of Natural-Gas-Hydrates 23

Machine Learning-Based Research on Reserve Prediction of Nat...

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Proceedings of the 4th international conference on Artificial Intelligence and Computer Engineering

作者： Sumin Ye Shuxian Mai Wenbo Niu Tao Yang Lei Ye Kun Zhang Huimin Tang Chenglong Li Boyang Peng Qiannan Yu College of Energy and Power Engineering Guangdong University of Petrochemical Technology China School of mechanical sctence and Engineering Northeast Petroleum University China and College of Energy and Power Engineering Guangdong University of Petrochemical Technology China Hainan Branch CNOOC (China) Co. Ltd. China Exploration and Development Research institute Daqing Oilfield Co. Ltd. China and Heilongjiang Key Laboratory of Reservoir Physics and Fluid Mechanics in Porous Medium China Geological Research Institute No. 4 Oil Production Plant of Daqing Oilfield Co. Ltd. China

ISBN: (纸本)9798400708831

Currently, there are challenges in reserve prediction of natural-gas-hydrates, including difficulties in obtaining parameters, limited calculation accuracy, and complex processes. machine learning is a data-driven predictive method with strong generalization capabilities, capable of abstracting and inducing knowledge and rules from basic data witch has made significant advancements in various fields and has been preliminarily applied in fields of earth sciences and energy exploration and development. Various machine learning algorithms for determining the parameters of gas hydrate reserves were introduced in this paper. the support vector machine algorithm was employed to calculate effective area and thickness, while neural network algorithm was introduced to calculate porosity, saturation, and decomposition coefficient for the determination of parameters of natural-gas-hydrates reserves in Shenhu area of China's offshore, and then the feasibility and accuracy of the method was verified. Compared to evaluation results based on trial mining or numerical simulation, the machine learning-based reserve prediction method demonstrates high predictive accuracy, clear processes, and simple structure, making it highly valuable for future research on storage and transportation of natural-gas-hydrates and provides foundation for future industrial applications.

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Degradation Characteristics Analysis and Fault Prediction of Switching Power Supply Based on data mining 21

Degradation Characteristics Analysis and Fault Prediction of...

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4th international conference on machine learning and machine Intelligence, MLMI 2021

作者： Shi, Linlin Yu, Pengfei He, Shilie Zhou, Zhenwei Meng, Linghui Liu, Junbin The Fifth Electronics Research Institute of Ministry of Industry and Information Technology China

ISBN: (纸本)9781450384247

Fault prediction and health monitoring of DC-DC switching power supply plays an important role in the safe and reliable operation of power electronic equipment. In this paper, a long-term high temperature degradation test was carried out for DC-DC power supply, and the characteristic parameters related to device health, such as input current, input voltage, output current and output voltage, were collected during the test. through data mining technology, we carry out data preprocessing, feature analysis, health index modeling and fault prediction analysis on the samples collected in the power supply test, so as to study the degradation and fault predictor of the power supply from the real power supply test degradation data. the research results of this paper have important engineering significance for the monitoring and health prediction of power supply. © 2021 ACM.

关键词： Health

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Predictive Student Performance Analysis Using machine learning and Student Assistance System 4th

Predictive Student Performance Analysis Using Machine Learni...

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4th international conference on Communications and Cyber-Physical Engineering, ICCCE 2021

作者： Sai Charan, N. Ali Hussain, Md. Vineela, P. Vamsi Adi Tilak, M. Chandu Siva Shankar, T. Department of ECM Koneru Lakshamiah Education Foundation Vaddeswaram India

ISBN: (纸本)9789811679841

In this, we mainly focus on the analysis of the student’s performance in academics based not only on external exams, but also taking into consideration the internal marks of each and every student. this work is useful for the users either for just getting an idea on the performance of the student or in situations where the institutions are unable to conduct the examinations and they wanted to promote the students based on their previous performance. We segregate and calculate the performance of students using data (Kaunang and Rotikan, Students academic performance prediction using data mining. IEEE, pp 18–10, 2018 [1]). We then predict the performance of the students who are going to pass and fail based on previous result and also the predicted marks of a student using a new algorithm namely Student Predictive Analysis algorithm (SPA algorithm) with the help of already existing algorithms SVM and linear regression algorithms. these two algorithms are the supervised machine learning algorithms. the support vector classifier is take from the SVM algorithm in order to classify the students as Pass/Fail. In this project, we mainly focus on the analysis of the student’s performance and then predict the results through them using training data and then test data of academics not only external exams, but also the overall academic performance of each and every student. In addition to that we have added some more features like student faculty interaction for clarifying doubts or getting faculty assistance, placement prediction and giving suggestions to the to get placed in the companies, OTP login validation and facilitate faculty to give overall feedback on students. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Voice Sentiment Analysis System Using Librosa

Voice Sentiment Analysis System Using Librosa

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： Ashok Reddy Kandula Rayala Nathaniel thota Bhanu Vissamsetti Sai Vignesh Meduri Leela Naga Sai Dept of AI&DS Seshadri Rao Gudlavalleru Engineering College Gudlvalleru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

the Voice Sentiment Analysis System is an innovative solution that can analyze the emotional content of spoken audio data in real-time. By using advanced machine learning algorithms and signal processing techniques, the system can accurately detect and categorize the emotions expressed by speakers. this provides valuable insights into the sentiment conveyed in conversations, speeches, or customer interactions. By processing streaming audio data, the system allows for immediate feedback and actionable insights in various applications. In business settings, it can be used to analyze customer service calls or feedback recordings in real-time to assess satisfaction levels, identify areas for improvement, and prioritize follow-up actions. Integration with voice-enabled assistants enhances user interactions by interpreting emotional cues and personalizing responses. In healthcare, the system can assist in monitoring patients' emotional well-being during therapy sessions or remote consultations. this enables healthcare professionals to assess progress, intervene when necessary, and customize treatment plans effectively. With its easy-to-use interface and strong performance, the Voice Sentiment Analysis System offers a versatile solution for understanding and interpreting human emotions through voice communication channels

关键词： Sentiment analysis Emotion recognition Speech analysis Customer services machine learning Feature extraction Ubiquitous computing Real-time systems Monitoring Spectrogram

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A Review of Advancements in Driver Emotion Detection: Deep learning Approaches and dataset Analysis

A Review of Advancements in Driver Emotion Detection: Deep L...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Mohammed Shukur Alfaras Oğuz Karan Electrical and Computer Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

this review paper delves into the rapidly evolving field of driver emotion detection, with a specific focus on the contributions of deep learning methodologies and the diverse datasets that facilitate this research. Facial emotion detection and recognition is a highly dynamic and challenging field in machine learning (ML) and Artificial Intelligence (AI). It has gained attraction for several decades, but it is extremely challenging due to the intrinsic complexities associated with understanding and interpreting human emotions. Understanding and responding to a driver's emotions is increasingly important when autonomous or assisted driving becomes common. To ensure safety, comfort, and optimal interaction between the driver and the car's systems, predicting the emotional state of the driver is essential, also of great significance in practical applications. We give a comprehensive survey of the state-of-the-art driver emotion recognition works that can effectively make use of the recent deep-learning approaches to identify complex emotional cues. Moreover, we explain a variety of datasets that play a vital role in flourishing this field along with the analysis of their effect, like AffectNet, CK+, and EMOTIC. Via this survey we try to investigate the challenges authors are faced with involving this field, e.g., concerns about data privacy, real-time processing demands, and the need for interdisciplinary collaboration. More importantly, the potential of these technologies to improve the driving experience and road safety has been highlighted. We hope this survey can benefit researchers and practitioners to have more insights and provide directions for advancing drivers’ emotions.

关键词： Deep learning Surveys Emotion recognition Protocols Reviews Road safety Safety

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Wood Species recognition Using machine learning 4

Wood Species Recognition Using Machine Learning

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4th international conference on Microelectronics, Signals and Systems, ICMSS 2021

作者： Neethu, S. Baby Syla, L. A P J Abdul Kalam Technical University Department of Computer Applications CET Trivandrum India

ISBN: (纸本)9781665448857

Although a computer vision-based automated wood species detection system is not extensively employed today, it is in high demand in a range of industries. Because each species of wood has distinct features and traits that determine how it is used and implemented. Human experts have traditionally been used nearly entirely in the identifying process, which is based on a variety of features of wood, such as texture, structure, and colour. Even competent scientists have difficulties distinguishing between different species of wood found in nature. the most difficult aspect of wood recognition is that some wood species have a texture that is identical to that of others, hence feature extraction is the most important step in the process. So in this work, two statistical feature extraction techniques are used 1) Local Binary pattern (LBP) 2) Gray Level Co-Occurrence Matrix (GLCM). Features extracted from both methods are combined to see whether they increase classification accuracy or not. Multi-SVM is used for classification. For this study, a new dataset is created which includes the three most common tree species in Indian territory. this study found that integrating LBP and GLCM features enhanced classification accuracy. Proposed system produces a result of 97.2%, which is better than individual approaches. © 2021 IEEE.

关键词： Local binary pattern

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