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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Palash Gourshettiwar Rajesh D Thakare Prapti Duddalwar Rushikesh Shete Swapnil Gundewar Prateek Verma Department of Computer Science and Medical Engineering Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India Department of Electronics Engineering Yashavantrao Chavan College of Engineering Nagpur Maharashtra India Department of Electronics Engineering Vishwakarma Institute of Technology Pune Maharashtra India Department of Computer Science and Business Systems St. Vincent Pallotti College of Engineering and Technology Nagpur Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The SMS spam detection Project provides a comprehensive framework for reliably identifying and classifying spam messages sent over SMS communication channels by fusing ML algorithms with NLP techniques. Using EDA, a dataset of labelled SMS messages identified as spam or ham is thoroughly studied to reveal patterns in the word count, sentence count, and message length distributions. Then, unstructured text is transformed into structured data that may be utilized with ML models through tokenization, punctuation and stop word removal, stemming, and other text preprocessing techniques. Feature engineering, which numerically represents text data using vectorization techniques like BoW and TF-IDF, plays a significant role. Various ML algorithms, including Gaussian Naive Bayes, Multinomial Naive Bayes, SVM, and ensemble approaches, are trained and evaluated. Performance metrics, including accuracy and precision, serve as standards for model selection.

关键词： Support vector machines Measurement Industries Medical services Message services Tokenization Numerical models Bayes methods Reliability Standards

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ElephantGuardNet: A Comprehensive Solution for Elephant Conservation and Human-Elephant Conflict Reduction

ElephantGuardNet: A Comprehensive Solution for Elephant Cons...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： S. Naveen R. Dhamotharan S. Sahebzathi R. Santhana Krishnan V. Vinoth Kumar P. Sundaravadivel Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The ElephantGuardNet system is an advanced technological framework designed to monitor and protect elephants while minimizing human-elephant conflict. The system leverages a combination of real-time data collection, AI-driven analysis, and IoT sensors to track elephant movements across vast territories. By integrating wildlife camera traps, drones, satellite imagery, and IoT sensors such as infrared motion detectors and seismic sensors, the system provides continuous monitoring of elephant activity, enabling proactive management of potential risks. A custom dataset, ElephantMovementTracker, compiled from diverse data sources, forms the foundation for the model's training. Preprocessing techniques such as data normalization, augmentation, and noise filtering enhance the dataset's quality and ensure its suitability for deep learning applications. EfficientNet B7 and YOLO are employed for image and real-time detection tasks, respectively, while 3D CNNs are used for video analytics, enabling accurate tracking of dynamic movement patterns. GRU-based models predict future elephant movements, helping assess the likelihood of conflict. XGBoost is utilized for classification tasks, evaluating the risk of human-elephant conflict. The system is deployed on AWS and incorporates NVIDIA Jetson Nano for real-time detection and automated alerting via SMS and email. IoT sensors and automated deterrent systems, including sound emitters and drones, further mitigate conflict risk. The system's effectiveness in reducing human-elephant conflicts and providing a scalable solution for elephant conservation demonstrates the potential of AI and IoT integration in wildlife protection efforts, offering a comprehensive approach to both conservation and human-elephant coexistence.

关键词： Deep learning YOLO Accuracy Tracking Visual analytics Wildlife Predictive models Real-time systems Sensor systems Drones

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Optimizing the Shopping Experience Through Smart Trolleys with Integrated Deep learning Techniques

Optimizing the Shopping Experience Through Smart Trolleys wi...

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Sustainable Expert Systems (ICSES), International Conference on

作者： T. S. Akhila X. Arogya Presskila P. Kalyanakumar R. Santhana Krishnan M. SahanaPreethi P. Sundaravadivel Department of Electronics and Communication Engineering Dhanalakshmi College of Engineering Tambaram Chennai India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research study presents an innovative smart shopping trolley system designed to enhance the retail shopping experience by integrating advanced deep learning techniques and sensor technology. The system begins with a secure membership card login process, enabling personalized customer interaction. Upon entry into the trolley system, products can be easily added and identified through a camera utilizing EfficientN et, a deep learning model trained on the store's inventory. The system incorporates an infrared motion sensor and a weight sensor to ensure accuracy in product registration, enhancing reliability and preventing errors. As the shopping process unfolds, a Transformer-based recommendation system analyzes real-time choices and shopping history to provide tailored product suggestions displayed on the trolley's LCD screen. To enhance security, an Autoencoder neural network monitors for anomalies in product identification and removal, alerting store personnel in case of discrepancies. Upon completing the shopping process, a QR code is generated for payment, streamlining the checkout experience. This smart shopping trolley system optimizes the shopping process, leveraging real-time data to improve efficiency, security, and overall customer satisfaction in the retail environment. The system aims to revolutionize traditional shopping methods by applying deep learning and IoT technologies and create a seamless, engaging consumer experience.

关键词： Deep learning Technological innovation Accuracy Customer satisfaction User interfaces Streaming media Transformers Real-time systems Sensor systems Security

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Hybrid Deep learning for International Trading: Integrating LSTM and Transformer Models

Hybrid Deep Learning for International Trading: Integrating ...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： T. S. Akhila J. Relin Francis Raj D. Kani Jesintha R. Santhana Krishnan V. Vinoth Kumar P. Sundaravadivel Department of Electronics and Communication Engineering Dhanalakshmi College of Engineering Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This research proposes a hybrid deep learning model combining Long Short-Term Memory (LSTM) networks and Transformers to enhance decision-making in international trading. The model leverages advanced techniques such as RoBERTa for text tokenization, FinBERT for sentiment analysis, and XLM-Roberta for multilingual language processing. LSTM is employed to analyze sequential numerical data like historical prices and trading volumes, providing accurate price and volatility forecasts. The Transformer component processes textual data to extract sentiment classifications and event-driven market impacts, facilitating a comprehensive understanding of market conditions. A fully connected fusion layer integrates insights from both components, enabling the model to classify market conditions holistically. The optimization process utilizes the Adam optimizer with learning rate scheduling to ensure efficient and effective training. Loss functions like Mean Squared Error (MSE) and Cross-Entropy Loss are employed to optimize numerical predictions and categorical outcomes, respectively. By combining temporal analysis with sentiment-driven insights, this hybrid model offers improved price forecasting, advanced multilingual sentiment analysis, and event-driven market condition predictions. The proposed approach aims to provide traders with actionable insights, enabling more informed decision-making in the highly dynamic and multilingual environment of international trading.

关键词： Deep learning Adaptation models Sentiment analysis Analytical models Predictive models Transformers Data models Numerical models Multilingual Long short term memory

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Performance Analysis of Acoustic Scene Classification Using ANN and CNN Techniques

Performance Analysis of Acoustic Scene Classification Using ...

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Integrated intelligence and Communication Systems (ICIICS), International Conference on

作者： Rakshitha K Karthik Aishwarya D Shetty P Sowmya Rashmitha Shettigar Department of Artificial Intelligence & Machine Learning Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte India School of Computer Science and Engineering Vellore Institute of Technology (VIT) Vellore India Department of Computer Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte India Department of B sc (IT)/CS Thakur Ramnarayan college of arts and commerce Dhaisar East Mumbai

artificial neural networks have achieved notable improvements in various learning tasks and have also been used to classify environmental sounds. The technique for allowing devices to recognize and interpret the surroundings from a recording is called Acoustic Scene Classification (ASC). Computational Auditory/Scene analysis is analyzing scenes using computational techniques. This research paper’s primary goal is to categorize audio recordings based on the scenarios or environments in which they were captured. In most applications, deep learning is one of the most recent trends. In this work, we have concentrated on two deep learning techniques employed to classify audio scenes: Convolution Neural Networks (CNN) and artificial Neural Networks (ANN) to develop the model. ReLU and LeakyReLU activation functions are used to train and validate the model. For evaluating the model performance, K-fold cross-validation was used with K=10 and obtained an accuracy of 75.8% using an artificial Neural Network and a proposed model with 90.6% accuracy using a Convolutional Neural Network. Comparing the results with other baseline models that achieved a performance of 73.3% for MCLNN with Mel-spectrogram, 78.2% for Pyramid-Combined CNN, 78.3% for Envnet-v2, and 81.9% for D-CNN.

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machine learning-Driven Blockchain for Enhanced Transparency in Government Public Records

Machine Learning-Driven Blockchain for Enhanced Transparency...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Sorabh Lakhanpal CH Vijendar Reddy Uma Reddy Akshay Varkale Ashwani Kumar Ali Albawi Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal M.P. India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

With more things done online, citizens have never wanted government data to be more available and dependable. This paper proposes a solution using blockchain and powerful algorithms. The recommended approach uses blockchain's immutability and safety, machine learning to discover outliers, and NLP to organize data. This coordinated strategy automates and opens government records to increase data quality, accessibility, and speed. Several tests and performance assessments compared the recommended solution to the traditional record-keeping method. Finding anomalies is faster using machine learning anomaly detection and natural language processing record sorting. This ensures data accuracy and clarity in real time. Spreading blockchain keeps documents secure and unchangeable. This makes changing difficult for those who shouldn't. The study found that the proposed strategy might improve public record management. It improves transparency, a crucial trait for responsible leadership, in a powerful, safe, and automated way using blockchain technology and cutting-edge algorithms.

关键词： machine learning algorithms Government machine learning Real-time systems Natural language processing Blockchains Safety

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Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking

arXiv

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arXiv 2024年

作者： Dong, Ming Chen, Yujing Zhang, Miao Sun, Hao He, Tingting Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan China National Language Resources Monitoring Research Center for Network Media Central China Normal University Wuhan China School of Computer Central China Normal University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China

Chinese Spell Checking (CSC) is a widely used technology, which plays a vital role in speech to text (STT) and optical character recognition (OCR). Most of the existing CSC approaches relying on BERT architecture achieve excellent performance. However, limited by the scale of the foundation model, BERT-based method does not work well in few-shot scenarios, showing certain limitations in practical applications. In this paper, we explore using an in-context learning method named RS-LLM (Rich Semantic based LLMs) to introduce large language models (LLMs) as the foundation model. Besides, we study the impact of introducing various Chinese rich semantic information in our framework. We found that by introducing a small number of specific Chinese rich semantic structures, LLMs achieve better performance than most of the BERT-based model on few-shot CSC task. Furthermore, we conduct experiments on multiple datasets, and the experimental results verified the superiority of our proposed framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Cloud-based Data Analytics for Automated Coastal Cleanup Robots with Convolutional Neural Network

Cloud-based Data Analytics for Automated Coastal Cleanup Rob...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： A. Ramesh Babu Chethan Chandra S Basavaraddi Ramesh S S K Mouleeswaran Santosh Kumar Sahoo P. Solainayagi School of Computer Applications Vidya Vikas Institute of Engineering and Technology Mysore Karnataka India Department of Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology Bangalore VTU Belagavi Karnataka India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Engineering and Technology VIT University Bhopal Madhya Pradesh India Department of Computer Science and Engineering Dayananda Sagar University Bangalore Karnataka India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The growing danger of coastal pollution requires novel, automated remediation methods. This paper proposes combining autonomous robots with sophisticated sensor technologies and a Convolutional Neural Network (CNN)-based data analytics system to clean up coastal areas. The robots autonomously cruise coastal settings, discover and categories marine waste using onboard sensors, and send data to a cloud-based infrastructure for analysis. The proposed method relies on CNN, a deep learning model known for image recognition. For reliable identification and classification, CNN is trained on a broad collection of marine waste images. The cloud-based data analytics platform receives, processes, and stores cleaning robot data. It handles massive datasets effectively using cloud computing scalability and processing power. The technology uses real-time cloud image processing to identify garbage and enable robots to adjust their cleaning tactics. The data analytics platform generates detailed reports and visualizations to analyze cleaning efficiency and environmental effect. It advances automated coastal cleaning technology by integrating advanced robotics, sensor technologies, and deep learning. The proposed approach improves coastal cleaning speed, accuracy, and flexibility to protect marine habitats and biodiversity.

关键词： Deep learning Cloud computing Data analysis Pollution Accuracy Sea measurements Robot sensing systems Cleaning Real-time systems Convolutional neural networks

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet

Detection of Mango Leaves Disease using CSUBW based Residual...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Sunil Kumar V Jyothi A P Nagamani H S Sumanth S Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Faculty of Engineering and Technology Ramaiah University of Applied Sciences Bengaluru India Department of Computer Science Govt. Smt VHD Central Institute of Home Science (MCU) Bengaluru India Department of Computer Science and Computer Applications Government College for Women (Affiliated to Bengaluru North University) Kolar India

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy.

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artificial Neural Network with extreme learning machine-based Wastewater Treatment systems 2

Artificial Neural Network with extreme learning machine-base...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Manimekalai, S. Prasath, B. Daniel Shadrach, Finney Lakshmanan, V. Daniya, T. Guha, Tapas Theivani Ammal College for Women P.G & Research Department of Computer Science Villupuram India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Eshwar College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Gmr Institute of Technology Department of Information Technology Andhra Pradesh Rajam India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The rapid increase in the number of people living in different regions of the globe has led to a significant problem: there is not enough freshwater accessible. The bulk of the world's water resources come from the ocean, and this includes both brackish water and saltwater from the ocean. Desalination and other practises for aquatic behaviour that make it feasible to meet the needs for water in both residential and business settings are two examples of processes that make this possible. In order to treat water and wastewater, membrane processes like microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), membrane bioreactor (MBR), and membrane distillation (MD) are utilised. MF, UF, and NF stand for microfiltration, ultrafiltration, and nanofiltration, respectively. In recent years, models of artificial intelligence, such as artificial neural networks (ANN), have emerged as a generic alternative for the purpose of modelling these methods. This may be explained by the many advantages that these models have over the conventional model in contrast to which they were based. In this paper, an overview of artificial neural network (ANN) models that have been created over the last two and a half decades for membrane methods that have been employed in the action of wastewater and purification is provided. In order to make accurate forecasts of these features' evolution over time, we used ANN and ELM, two strong artificial neural networks. It is possible to conduct corrective measures in order to guarantee that the operations will be carried out in accordance with the requirements. This technique may be beneficial to water treatment facilities because to its complex process, which can modify its characteristics in a manner that is both dynamic and inconsistent. The ultimate objective is to increase the degree of accuracy that can be achieved by wastewater treatment models when making forecasts. This research estimates the concentration of COD i

关键词： Neural networks

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