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Sentiment Analysis and deep learning (ICSAdL), International Conference on

作者： Govinda rajulu. G L. Sharmila d. Venkatesan E. Soumya Sargu Chandra Prakash Ms. P. Priyanka Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin's Engineeringollege Dhulapally Secunderabad Telangana India Deptarment of CSE St. Martin's Engineeringollege Dhulapally Secunderabad Telangana India Department of CSE(AI&ML) St. Martin's Engineeringollege Dhulapally Secunderabad Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This paper proposes a GSM-based children's rescue system designed for earthquake emergencies, emphasizing cost-effectiveness and simplicity while ensuring rapid child rescue. The system operates in three key stages: data acquisition, classification, and alert notification. Sensors collect temperature, pH, and heart rate data, which are then digitized and classified to assess the child's condition. If abnormal values are detected, an alert notification is sent via GSM to parents and rescue teams, providing real-time location data through GPS. The hardware implementation, based on raspberry Pi, converts analog sensor signals into digital data for processing. The system's efficacy is validated through various scenarios, demonstrating its ability to detect critical conditions and promptly trigger alerts, enhancing child safety during disasters.

关键词： Temperature sensors GSM Chemical sensors Earthquakes Sensor systems real-time systems Biomedical monitoring Monitoring Intelligent sensors Global Positioning System

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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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Corrosion resistance and conductivity behavior of Cr-Al-N MAX phase coatings prepared by magnetron sputtering technology

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Thin Solid Films 2024年 793卷

作者： Su, Y.L. Kao, W.H. Horng, J.H. Hsueh, H.C. Cian, Y.C. Hsieh, T.C. Department of Mechanical Engineering National Cheng Kung University Tainan701 Taiwan Department of Automation Engineering & Institute of Mechatronoptic Systems Chienkuo Technology University Changhua500 Taiwan Department of Power Mechanical Engineering National Formosa University Yunlin633 Taiwan Smart Machine and Intelligent Manufacturing Research Center National Formosa University Yunlin633 Taiwan Micro/Meso Mechanical Manufacturing R&D Department Surface Engineering & Heat Treatment Section Metal Industries Research and Development Centre Kaohsiung811 Taiwan

Chromium aluminum nitride (CrAlN) coatings containing Cr2AlN phase were prepared by unbalanced magnetron sputtering followed by annealing at 1000 °C. The crystal structures, X-ray diffraction patterns, and selected area electron diffraction patterns of the Cr2AlN phase were modeled using computer software and compared with experimental observations. A good agreement was found between the two sets of results, thereby confirming the presence of Cr2AlN phase in the coatings. While previous studies have demonstrated the formation of Cr2AlN phase theoretically, this study presents experimental evidence of its physical existence. The potentiodynamic polarization results and interfacial contact resistance measurements show that the CrAlN coating with Cr2AlN phase has excellent corrosion resistance (a corrosion current density of 5.35 × 10−7 A/cm2 and electrical conductivity of 5.5 mΩ·cm2) and is thus a promising candidate for the protection of fuel cell bipolar plates. © 2024 Elsevier B.V.

关键词： Chromium compounds

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Advanced Process Control in Manufacturing Using IoT devices and Artificial Neural Networks

Advanced Process Control in Manufacturing Using IoT Devices ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： V. Sumathi ramesh S Chethan Chandra S Basavaraddi Visumathi J Ishwarya M.V S. Srinivasan Department of Mathematics Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Belagavi Karnataka India Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems.

关键词： Performance evaluation Adaptation models Process control Artificial neural networks Predictive models real-time systems Manufacturing Sensors Internet of Things Monitoring

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Automated Personalized Health Analytics using IoT and machine learning Algorithms

Automated Personalized Health Analytics using IoT and Machin...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： T M Sivanesan Smitha J A U. Arul K. Balasubramanian Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru Karnataka India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India

This research work develops and implements an integrated system using IoT technologies and machine learning algorithm to measure Body Mass Index (BMI) and perform health analytics in hospitals. Obesity, overweight, and chronic diseases are major public health issues. Healthcare workers need accurate and quick BMI readings to monitor weight status and health issues. To overcome these limitations, the proposed system uses IoT sensors to automate BMI evaluations and collect real-time health data. Wireless IoT devices interface with hospital information systems. The Support Vector machine (SVM) algorithm analyzes BMI, physical activity, and vital sign data. It can identify and predict BMI values from data. SVM can predict BMI for customized weight loss recommendations. The proposed setup helps hospitals to automate BMI measuring, thus reducing medical staff burden and manual intervention. real-time monitoring and analysis enable early identification and individualized treatment of obesity-related health concerns. IoT devices with the SVM algorithm enable data-driven decision-making, improving patient outcomes and healthcare efficiency.

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Evaluation of CatBoost for diabetes Prevention in Comparison to XGBoost: To avert mortality by developing an AI model capable of predicting the onset of diabetes

Evaluation of CatBoost for Diabetes Prevention in Comparison...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： G. Gifta Jerith S. Jagadeesh P. Ponmathi Jeba Kiruba C Mahesh S. Anantha Babu S. Samsudeen Shaffi Dept. of CSE (Artificial Intelligence and Machine Learning) School of Engineering Malla Reddy University Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha Engineering College Chennai India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

diabetes is a metabolic condition defined by consistently elevated blood glucose levels. Hyperglycemia is characterized by heightened hunger, thirst, and urination. Numerous consequences may arise from untreated diabetes. diabetic ketoacidosis, hyperosmolarity, hyperglycemia, and even mortality may occur as acute consequences. Heart disease, stroke, chronic renal failure, foot ulcers, and eye impairment are some of the worst long-term consequences. A total of 463 million individuals were found to have diabetes in 2019, according to the worldwide diabetes organisation. Assuming predictions come true, that number will soar to 700 million by 2045, up 578 million by 2030. It has modern medical applications, allowing for the rapid and precise diagnosis of illness. Thus, we may use machine learning to avert mortality by developing an AI model capable of predicting the onset of diabetes; to determine which algorithm is most suited for this task, we will compare the CatBoost and XGBoost methods. In the end, the research compared two diabetes prediction models, XGBoost and CatBoost, using a variety of health indicators from the dataset. Using a real-world dataset obtained from Kaggle, our suggested system is trained and built using Python. Performance assessment criteria such as precision, recall, F1score and accuracy measurements are used to thoroughly analyse our methods. In comparison to XGBoost's 88.33% accuracy rate, 85.25 percent precision, 85.63 percent recall, and an F1 Score of 86.50. CatBoost's 91.08% percent accuracy, 85.25 percent precision, 85.63 percent recall, and 87.38 F1 Score. CatBoost marks it as the most successful ensemble approach.

关键词： deep learning Heart Accuracy machine learning algorithms Prevention and mitigation Mortality Medical services Predictive models Prediction algorithms diabetes

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ENLS: Experimental Evaluation of Credit Card Fraud Identification System Using Elevated Neural learning Scheme With Cybersecurity Features

ENLS: Experimental Evaluation of Credit Card Fraud Identific...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： r. Bhairavi Lakshmi M r. Sankar V. rama Krishna Sharma Anni Princy B Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics S.A Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Panimalar Engineering College Chennai Tamilnadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

rapid developments in e-commerce have led to a meteoric rise in credit card usage. More and more people are falling victim to credit card fraud as a result of this payment method's widespread use. Consequently, we want a robust and reliable fraud detection system to put a stop to these scams. We have covered the basics of credit card theft in this article. Finding a remedy to these sorts of scams is, thus, quite crucial. We developed a model to identify fraudulent credit card transactions in this article. The majority of the critical capabilities needed to identify fraudulent and unlawful transactions can be met by this system. The pattern and behaviour of illicit transactions are getting more difficult to trace due to the ongoing change in technology. Using technology that incorporates an enhanced deep learning scheme, known as an Elevated Neural learning Scheme (ENLS), can help find a solution. In order to determine how effective the suggested scheme is, this method is cross-validated using the traditional learning technique known as Logistic regression (Lr). In order to do this, we apply various classification models to the data, including ENLS and Lr, and then we quantify the model's performance using quantitative metrics like accuracy. Our study's conclusion describes the optimal classifier by training and testing with the suggested methods, which yields a superior answer.

关键词： Training Technological innovation Accuracy reviews Credit cards Feature extraction data models Fraud reliability Testing

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MELLOddY: Cross-pharma Federated learning at Unprecedented Scale Unlocks Benefits in QSAr without Compromising Proprietary Information

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JOUrNAL OF CHEMICAL INFOrMATION ANd MOdELING 2023年第7期64卷 2331-2344页

作者： Heyndrickx, Wouter Mervin, Lewis Morawietz, Tobias Sturm, Noe Friedrich, Lukas Zalewski, Adam Pentina, Anastasia Humbeck, Lina Oldenhof, Martijn Niwayama, ritsuya Schmidtke, Peter Fechner, Nikolas Simm, Jaak Arany, Adam drizard, Nicolas Jabal, rama Afanasyeva, Arina Loeb, regis Verma, Shlok Harnqvist, Simon Holmes, Matthew Pejo, Balazs Telenczuk, Maria Holway, Nicholas dieckmann, Arne rieke, Nicola Zumsande, Friederike Clevert, djork-Arne Krug, Michael Luscombe, Christopher Green, darren Ertl, Peter Antal, Peter Marcus, david do Huu, Nicolas Fuji, Hideyoshi Pickett, Stephen Acs, Gergely Boniface, Eric Beck, Bernd Sun, Yax Gohier, Arnaud rippmann, Friedrich Engkvist, Ola Goeller, Andreas H. Moreau, Yves Galtier, Mathieu N. Schuffenhauer, Ansgar Ceulemans, Hugo Janssen Pharmaceut NV B-2340 Beerse Belgium AstraZeneca R&D Cambridge CB2 0SL England Bayer Pharm AG Global Drug Discovery Chem Res Computat Chem D-42096 Wuppertal Germany Novartis Inst BioMed Res CH-4002 Basel Switzerland Merck KGaA Global Res & Dev D-64293 Darmstadt Germany Amgen Res Munich GmbH D-81477 Munich Germany Bayer AG Machine Learning Res Res & Dev Pharmaceut D-13353 Berlin Germany Boehringer Ingelheim Pharm GmbH & Co KG BI Med Chem Dept D-88397 Biberach Germany Katholieke Univ Leuven ESAT STADIUS B-3001 Heverlee Belgium Inst Rech Servier F-78290 Croissy Sur Seine Ile De France France Discngine F-75012 Paris France Iktos F-75017 Paris France Astellas Pharma Inc Modal Informat Grp Digital Res Solut Adv Informat & Analyt Tsukuba Ibaraki 3058585 Japan GlaxoSmithKline Computat Sci Brentford SG1 2NY Herts England Budapest Univ Technol & Econ Dept Networked Syst & Serv H-1111 Budapest Hungary Owkin F-75010 Paris France Bayer AG API Prod Prod Supply Pharmaceut D-59192 Bergkamen Germany NVIDIA GmbH D-81369 Munich Germany Budapest Univ Technol & Econ Dept Measurement & Informat Syst H-1111 Budapest Hungary Substra Fdn Labelia Labs F-44000 Nantes France Amgen Res Thousand Oaks CA 91320 USA AstraZeneca Mol AI Discovery Sci S-43150 Molndal Sweden Owkin F-44000 Nantes France

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLOddY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security. The experiments involved an unprecedented cross-pharma data set of 2.6+ billion confidential experimental activity data points, documenting 21+ million physical small molecules and 40+ thousand assays in on-target and secondary pharmacodynamics and pharmacokinetics. Appropriate complementary metrics were developed to evaluate the predictive performance in the federated setting. In addition to predictive performance increases in labeled space, the results point toward an extended applicability domain in federated learning. Increases in collective training data volume, including by means of auxiliary data resulting from single concentration high-throughput and imaging assays, continued to boost predictive performance, albeit with a saturating return. Markedly higher improvements were observed for the pharmacokinetics and safety panel assay-based task subsets.

关键词： Assays Bioinformatics and computational biology drug discovery machine learning Structure activity relationship

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MANAGING NEXT-GENErATION NETWOrKS FOr INTELLIGENT SErVICES ANd APPLICATIONS

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IEEE NETWOrK 2022年第3期36卷 4-6页

作者： Aloqaily, Moayad Sedjelmac, Hichem Tseng, Lewis da Costa Cordeiro, Weverton Luis Zhang, Qian Amer Univ Middle East AUM Kuwait Kuwait Al Ain Univ Fac Engn Abu Dhabi U Arab Emirates Analytics Inc Ottawa ON Canada Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates Ericsson R&D France Paris France Orange Labs Cyber Secur & AI Paris France Orange Labs Paris France Univ Burgundy DRIVE Lab Dijon France Boston Coll Dept Comp Sci Chestnut Hill MA 02167 USA Toyota Info Technol Ctr Mountain View CA USA Fed Univ Rio Grande Sul FRGS Inst Informat INF Porto Alegre RS Brazil Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Univ Sci & Technol Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Huawei HKUST Innovat Lab Hong Kong Peoples R China HKUST Digital Life Res Ctr Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Res Asia Wireless & Networking Grp Beijing Peoples R China

Next-generation networks (NGNs) integrate the functionalities of a plethora of established and emerging technologies such as 5G, artificial intelligence (AI), edge intelligence, network softwarization, and data plane programmability. NGNs promise to achieve ultra-fast data rates and minimal latency in wireless communication and networking. They enable smart and autonomous services and applications through AI and machine learning (ML). Coupled with advances in end-user devices, NGNs enable a plethora of smart services and user-defined applications. Given the rise in system complexity and the exponential increase in the amount of data exchanged through networks, new state-of-the-art NGN management solutions are needed. By adapting cooperative and distributed management solutions, more reliable and efficient services and applications are conveyed to end users. Moreover, with distributed learning solutions, NGNs could be optimized to support the dynamic nature of network configuration and enable end-to-end system automation. For instance, the integration of federated learning (FL), deep reinforcement learning (drL), and blockchain to NGNs can support scalable, secure, and diversified services and applications. Furthermore, through data plane programmability, network intelligence could be implemented directly on programmable devices in the network core. An intelligent forwarding plane would enable faster reaction to network events without depending on time-consuming exchange between the data and control planes.

关键词： Special issues and sections Next generation networking reinforcement learning distributed management Computer aided instruction Complexity theory Blockchains Collaborative work 5G mobile communication deep learning

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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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