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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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Domain-Specific Lexicon Customization for Enhanced Real-Time Sentiment Analysis

Domain-Specific Lexicon Customization for Enhanced Real-Time...

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

作者： Vishal Awasthi Atul Kumar Agnihotri Preeti Singh Ajay Tiwari Dept. of ECE UIET C.S.J.M. University Kanpur

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

This study contributes to the improvement of sentiment analysis by optimizing VADER in a domain-specific lexicon and overcomes the limitations of past rule-based methods as well as ML techniques. The traditional VADER models are fast and work surprisingly well on sentiment properties, but still lag behind human-level performance to capture nuanced sentiments present in several real-world applications, such as tweets or posts. The method of integrating machine learning applications such as BERT has approximately 99% accuracy and is computationally intensive for these applications. In this work, a domain-specific lexicon adjustment approach based on normalization and customization sentiment score has been proposed, which keeps the simplicity of VADER with an improved accuracy of 88.7% and precision of 90 % respectively. This hybrid approach is a tradeoff between efficiency and adaptability that proves efficient both in terms of real-time sentiment analysis for diverse situations while providing more accurate and reliable information about the sentiment classes.

关键词： Sentiment analysis Analytical models Accuracy Social networking (online) Computational modeling Lexicon signal processing Stability analysis Real-time systems Reliability

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Advancing Techniques for Deepfake Detection and Evaluation: Challenges and Innovations

Advancing Techniques for Deepfake Detection and Evaluation: ...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Pradum Kumar Singh Sourav Sharma Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The proliferation of deepfakes-also termed artificial intelligence-generated synthetic media poses unprecedented challenges to the digital authenticity of media, for media integrity, and to societies’ trust in its credibility. Despite such significant technological advancements, current methodologies for detecting deepfakes remain somewhat fragmented, reactive, and often unable to keep pace with rapidly evolving technologies in generative AI. This article considers a comprehensive multi-modal approach toward deepfake detection by integrating an advanced machine learning algorithm, novel statistical correlation techniques, and forensic image analysis that would fill in the critical gaps in the existing frameworks. By using an extensive dataset of 10,000 synthetic and authentic media samples from diverse domains, we developed a hybrid neural network architecture that attained 94.3% accuracy on identifying AI-generated content. Our methodology utilized ensemble learning by combining spatial-temporal inconsistency detection, biological signal analysis, and advanced feature extraction algorithms. Key findings demonstrate significant vulnerabilities in current deepfake generation models, with our proposed technique successfully identifying subtle artifacts and inconsistencies across multiple generative AI platforms. This research conclusively demonstrates that proactive, adaptive detection strategies are critical to mitigating the potential risks associated with synthetic media, providing a robust framework for future technological interventions in digital forensics and media authentication.

关键词： Deepfakes machine learning algorithms Generative AI Neural networks Digital forensics signal processing algorithms Authentication Media Feature extraction Information integrity

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Sleep Disorder Detection using machine learning Method

Sleep Disorder Detection using Machine Learning Method

引用

Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Puneet Kumar Yadav Uday Kumar Singh J. Judeson Antony Kovilpiaali R Tamilarasi School of Engineering and Design Alliance University Bangalore

Evidence is mounting quickly that multifactorial nocturnal surveillance, when combined with wearable technology and deep learning, may be problematic for the early detection and evaluation of sleep problems. Data of Numerous sleep disorders, such as insomnia, are growing increasingly widespread and severe, according to the World Health Organization (WHO) and hospitals that conduct medical research. This dynamic is associated with high levels of daily worry, stress, and depressive diseases. The use of ruing is used to forecast various sleep disorders. It contrasts the numerous strategies employed by various researcher did work in signal processing methodologies, as well as their benefits and shortcomings. The crucial element is sleep. It is essential for the normal maintenance of one’s bodily and mental health, just as crucial as breathing, eating, and drinking. Numerous obstacles prevent AI from being widely used and generalizable in therapeutic contexts. Nevertheless, AI has the potential to be a strong tool in the healthcare industry since it can improve patient diagnostic capacities, the management of sleep disorders. However, before incorporating existing algorithms for deep learning and machine learning into sleep clinics, it is essential to regulate and standardize them. In this research our model got highest accuracy of 93% which was highest as compare to other models.

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Deep learning-Based Intrusion Detection Model for Internet of Things Environment

Deep Learning-Based Intrusion Detection Model for Internet o...

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Innovations in High Speed Communication and signal processing (IHCSP), international conference on

作者： Shakh Md Shakib Hasan Ismail Keshta Dhruv Thakkar Leeladhar Gudala Mukesh Soni Renato R. Maaliw Westcliff University Los Angeles USA CSIS Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Computer & Information Sciences Temple University Schaumburg IL USA Department of Computer Science and Engineering Valparaiso University India Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab India College of Engineering Southern Luzon State University Philippines

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

The proposed IoT intrusion detection model uses machine learning to compensate for data loss in convolutional neural networks' initial pooling layer and the gradient vanishing problem that recurrent neural networks face when processing long sequential input. Time series data for intrusion detection models is optimized using Gramme matrix field visualization. To reduce data loss, the SoftPool pooling layer replaced the original. The recurrent neural network uses a bidirectional gated recurrent unit with fewer parameters and a multi-head structure modeled after the self-attention mechanism. Baseline data will be TON-IoT data. The number of heads multi-head bidirectional gated recurrent unit, pooling layer topologies, and visualization approaches are experimental parameters. The simulation results show that a three-head structure, SoftPool for layer pooling, and for accuracy, precision, detection rate, F1 score, and false positive rate, multi-class classification works well with field and Gramme matrix visualization. The suggested model outperforms state-of-the-art models in accuracy, detection rate, F1 score, and false positive rate by 0.14, 0.09, 0.13, and 0.08. Modern machine learning improves IoT intrusion detection.

关键词： Performance evaluation Technological innovation Accuracy Time series analysis Intrusion detection Data visualization machine learning Data models Topology Internet of Things

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Real-Time Data Analytics and Decision-Making in Modern Sensor Networks

Real-Time Data Analytics and Decision-Making in Modern Senso...

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Innovations in High Speed Communication and signal processing (IHCSP), international conference on

作者： D. Anandhasilambarasan Rahul S.Devi Shivam Khurana Vaibhav Goutham Suresh M.P. Karnik Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Punjab India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

This study introduces a novel technique to analyze data and make real-time judgments in sensor networks. This system handles several sensor data issues using innovative machine learning, secure computing, flexible filtering, anomaly detection, and blockchain technology. We evaluated the framework's efficacy by comparing it comprehensively with existing methodologies and finding that it enhanced performance across several performance indicators. The approach is accurate, quick, resource-efficient, error-tolerant, and data-safe. Complex machine learning algorithms improve data analytics accuracy and prediction. This enables rapid, precise judgments in shifting scenarios. For data-sensitive applications that must fulfill tight regulatory criteria, the technique uses safe multi-party computer technology to safeguard and secure data. Due to noise reduction and signal clarity, adaptive filtering improves data quality. However, anomaly detection systems find and resolve unusual data patterns, making them more dependable. Blockchain-based data security ensures unambiguous, unchangeable records. This prevents unauthorized data access and changes. For critical applications to perform properly, sensor data must be accurate and trustworthy. An ablation study details how each item fits within the framework and how vital it is to improve the total system. The recommended technique, with its holistic approach, represents a significant technological breakthrough in sensor networks, enabling healthcare monitoring, environmental sensing, and manufacturing automation.

关键词： Accuracy Data analysis machine learning algorithms Data security Decision making machine learning Medical services Real-time systems Blockchains Anomaly detection

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Comprehensive machine learning Framework for Heart Disease, Hypertension, and Diabetes Assessment

Comprehensive Machine Learning Framework for Heart Disease, ...

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Innovations in High Speed Communication and signal processing (IHCSP), international conference on

作者： Ayaan Faiz Roopashree R Rupali A. Mahajan M. Vigenesh Sanjay Bhatnagar Santhosh Kumar. C Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India COMP Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Civil Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

There are more illnesses every day, and detection takes too long. Nowadays, unhealthy lifestyles, junk food, and poor habits lead to many issues and potentially fatal illnesses. Following the COVID-19 pandemic in India, illness outbreaks such as heart disease, diabetes, and hypertension have impacted individuals globally. Currently, these illnesses are the leading cause of mortality for all ages. The health industry needs to improve illness prediction using machine learning (ML) approaches. machine learning (ML) within Artificial intellect may mimic human intellect and handle enormous amounts of data. machine learning single classifiers are insufficient for accurate and efficient classification nowadays. Hybrid machine learning Models are used to combine several classifiers. Prior study compared classifier ensembles for disease prediction. No universal model was created for varied illnesses. In the Covid-19 epidemic, people with hypertension, heart, or diabetes had a poor survival rate and were the main cause of mortality. Classifiers may classify or forecast illnesses. By using historical data, we were able to make predictions for cardiovascular condition, diabetes, and high blood pressure by using the Hybrid Ensemble Common Model. Using LightGBM, Random Forest, and KNN as Ensemble Classifiers, the Voting classifier is utilised for final output. The final output is captured after cross-validation.

关键词： Hypertension Heart Accuracy Computational modeling Mortality Predictive models Nearest neighbor methods Diabetes Random forests Diseases

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Identification of Retinal Ischemia for Medical Pronouncement Through Fundus Images and machine learning Techniques

Identification of Retinal Ischemia for Medical Pronouncement...

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

作者： V. Indu Srinivas Pasupula Gill Santosh Kumar Cheekatla Swapnapriya S NagaMallik Raj T. Ravibabu Dept of CSE(AIML) BVRIT College of Engineering for Women Hyderabad Telengana Department of CSE (AI&ML) Vignana Bharathi Institute of Technology Hyderabad Telengana Dept.of Mechanical Engineering Vignan's institute of information technology (A) Visakhapatnam AP India Dept of CSE Vignan's Institute of Information Technology (A) Visakhapatnam AP India Department of ECE Miracle Educational Society Vizianagaram AP India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Retinal ischemia is caused by diabetes which affects small arteries including retinopathy. It is a condition marked by the deterioration of the retina's *** worse progression of Retinal ischemia may lead to the complete blindness. In this case, early detection of diabetes and retinal ischemia is more crucial to detect the lesions affected and provide the proper guidance and treatment to the patients suffering from diabetes. The identification of RI can be done manually and automatically. In the manual process, analysis and explanation is given by ophthalmologists and opticians. It is a time taking process and expensive. In case of automation process, early detection of RI can be done by automated systems using Artificial intelligence and deep learning over traditional methods. Most of the research work has done based on early detection of RI by taking two major facets fundus images and RI detection techniques.

关键词： Photography Biomedical optical imaging signal processing algorithms Retina Optical imaging Real-time systems Diabetes Optical sensors Optical signal processing Arteries

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signal processing technology curriculum reform for short-semester professional postgraduates 2

Signal processing technology curriculum reform for short-sem...

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2nd international conference on Information Science and Education, ICISE-IE 2021

作者： Gao, Zhiyuan Wang, Yiru Zan, Peng Li, Li School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9781665438292

This paper introduces the curriculum reform achievements of signal processing technology course for short-semester professional postgraduate students majored in electronic information in Shanghai University. In order to cultivate the innovation and active learning ability, the latest academic research papers are used to carry out case and project based teaching by combining industry expert lectures and MOOC video lectures, with signal processing theory lectures. By reforming objectives of the practical teaching system and implementing complex modern signal processing algorithms for several actual engineering cases, a new blending learning system is constructed with course algorithm implementation, personal project practice, and group project practice. A teaching case on the application of adaptive filtering in active vibration control is taken as an example to introduce the specific process of the course teaching of how to carry out the teaching activities in the short semester system of Shanghai University. © 2021 IEEE

关键词： learning systems

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Fusion Methods of Deep learning for Surveillance and Harvest Prediction of Plants

Fusion Methods of Deep Learning for Surveillance and Harvest...

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

作者： Devi, M. Vasumathi Priyanka, Rupireddy Kumar, V.N.S. Vijay Sagar, M. Swapnapriya, Cheekatla Kumar, Kopparty Vinay Vignan's Nirula Institute of Technology and Science for Women Department of Cse AP Guntur India Telangana India Vardhaman college of Engineering Department of Cse Telangana Hyderabad India Avanthi Institute of Engineering and Engineering Department of Cse Makavarapalem A.P. Vizag India Department of Cse Duvvada AP Visakhapatnam India Cmr institute of technology Department of Data Science Telangana India

ISBN: (纸本)9798331506452

Farmers have the opportunity to make harvest for 4-5 times more farm output per acre. Today, more than ever, technology is enabling farmers to make significant progress, greatly enhancing their quality of life. Seen through the eyes of a farmer, it is a significant miracle occurring on their modest farm land. In order to evaluate data and improve farming techniques, Technologies- based rooftop farming models can include AI and machine learning algorithms. We may anticipate the development of even more sophisticated algorithms in the future, which will enable farming to become more productive and efficient. AI algorithms play apivotal role in converting this data into actionable insights. AI introduces a level of sophistication and efficiency to agriculture that was previously unattainable, from the creation of decision support systems to predictive modeling for estimating crop harvest. Conventional methods of crop monitoring, which typically depend on visual assessments or basic remote sensing techniques, may fail to detect the subtle variations in plant health that indicate stress or potential harvest problems. Recent advancements in remote sensing technology, especially hyper-spectral imaging, have facilitated the acquisition of intricate spectral data, enabling more precise evaluations of crop health. © 2024 IEEE.

关键词： Classification (of information)

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Twitter Spam Detection Using Different machine learning Techniques 1

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

作者： Venkatesh, G. Reddy, D. Santosh Kavitha, C. Jinila, Y. Bevish Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Information Technology Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9789819914104

ISBN: (纸本)9789819914098

Vehicle reconciliation strategies have been involved a few times in spam channels to incorporate approaching/active messages, for example, spam and spam bunches. This technique expresses that each bunch contains little miniature groups, and each miniature group is dispersed. Notwithstanding, this thought ought not be trifled with, and the miniature group might have a lopsided dispersion. To build the respectability of the main strategy for appropriating the Internet class, we suggest supplanting the Euclidean space with a succession of models that incorporate into the miniature bunch connected with the circulation. Here, the Naïve Bayes (INB) classification has been carried out to carry out miniature bunches across the line. While these INBs can decide the distance and limits of micro clusters, Euclidean space considers the overall worth of the group and misdirects the bigger micro cluster. In this report, DenStream is upheld by a committed framework called INB DenStream. To represent the presentation of INB-DenStream, current techniques, like DenStream, StreamKM + +, and CluStream, have been utilized in the Twitter chronicle, and their exhibition has been estimated as far as quality, honesty as a general rule, memory as a rule, F1 measurements., markers, and complex estimations. The relatively close outcomes show that our techniques outflank its rivals in the set figures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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