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2nd International Conference on smart Technologies for smart Nation, smartTechCon 2023

作者： santhuja, P. Ranganathan, Chitra sabapathy sasi Kumar, C. srinivasan, s. Latha, N. Department of Computer Science Engineering B V Raju Institute of Technology Telangana Hyderabad India Mphasis Corporation ChandlerAZ United States Department of Computer Science and Engineering Shree Sathyam College of Engineering and Technology Tamil Nadu Salem India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Wireless Mesh Network (WMN) is envisioned as an efficiently feasible paradigm and a capable method for sustaining Quality of service (Qos). During data transmission, multi-hop WMNs incident repetitive link failures due to many reasons. Hence, these failures originate harsh function degradation in WMNs. This approach presents Route stability with Node Reliability-based auto-reconfiguration (RsNR) in WMN. Providing reliability is measured to be a demanding job because of the imperfect energy and memory. This approach regulates the radios in the WMN conditional on signal strength to ensure reliability by applying self-reconfiguration. The stability of the path builds the route an applicable one to forward information. The node reliability and the link stability measure the link quality. This link quality node makes self-reconfiguration. The node with a lesser sINR value with the best Link quality node is chosen as the next node. The RsNR approach results revealed increased efficiency of the received rate and residual energy of the packet received rate. © 2023 IEEE.

关键词： Link failure detection Link stability Node reliability self-reconfiguration Wireless mesh network

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Designing an AI driven intelligent Tutorial system

Designing an AI driven intelligent Tutorial System

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Recent Trends in computer science and Technology (ICRTCsT), International Conference on

作者： Md shamsher Alam surjeet Kumar Zeeshan Khursheed Hemant Kumar Mahato shadan bashar Anjali suman Computer Science and Engineering Associate Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science and Engineering Assistant Professor in Government Engineering College Sheikhpura Bihar India DGM Jio Platform Limited Mumbai India Master of Computer Application Assistant Professor in NSHM Durgapur India Master of Computer Application Assistant Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science & Engineering Student R.V.S College of Engineering & Technology Jamshedpur India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. This research paper explores the design and implementation of an AI-driven Intelligent Tutorial system (ITs) aimed at enhancing the personalized learning experience. The system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. The paper delves into the key components of the proposed ITs, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. The paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. Through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITs on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. The findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： Ethics Data privacy Machine learning algorithms Image recognition Decision making Tutorials Market research

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Automatic Detection of Multilingual Misogynistic Content in social Media Data based on Machine Learning Approach

Automatic Detection of Multilingual Misogynistic Content in ...

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Integrated Circuits and Communication systems (ICICACs), IEEE International Conference on

作者： Pravin D. Kaware Anjali b. Raut Department of Computer Science & Engineering H.V.P.M’s College of Engineering & Technology Amravati India

Global communication has changed due to social media's rapid ascent. because social media is a global stage where people from all walks of life may communicate in their own language, it is important to tackle misogynistic content detection on these platforms in a multilingual manner. Hence, this paper presents the development of an automatic detection of misogynistic content in social media data that uses machine learning (ML) and natural language processing (NLP) techniques. The model is based on a multilingual annotated corpus of aggressive and sexist content in Hindi and Indian English based on TRAC-2 benchmark dataset. To train machine learning classifiers, the four ML methods are utilized in this research based on hybrid linguistic features. Finally, test results were assessed to construct the best classifier model in both languages. The proposed methodology outperforms than the existing state of the art model, demonstrating it successfully handles social media content.

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Using Machine Learning to detect abnormalities on Modbus/TCP Networks 22

Using Machine Learning to detect abnormalities on Modbus/TCP...

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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Yatish sekaran Tanmoy Debnath Taeesh Azal Assadi sai Dileep suvvari shubh Oswal Computer Science and Engineering BMS College Of Engineering India Computer Science and Engineering B.M.S. College of Engineering India Electronics and Communication BMS College of Engineering India Computer Science and Engineering BMS College of Engineering India

ISBN: (纸本)9781450399937

Modbus/TCP is a communication protocol extensively used in machine-to-machine communications. Therefore, anomaly detection on this protocol is of paramount importance in the industry as it helps protect cyber assets and monitor IoT devices created by manufacturing plants across the globe. This detection is achieved by looking at standard patterns and then comparing them with patterns of data found unknown to the system itself. Today, catapulted by rapid developments in machine learning, there has been a marked reduction in the time it takes to identify anomalies in Modbus/TCP networks. Anomaly detection methodologies, leveraging neural networks trained through numerous machine-learning algorithms, further provide a system that could be effectively applied to many IoT device marketplaces, such as consumer-level applications. This paper proposes a framework for evaluating feature sets and learning-based models to detect Modbus/TCP anomalies efficiently and accurately.

关键词： Anomaly Detection

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Comparative Analysis of various Recommender systems for Learners Using Artificial Intelligence

Comparative Analysis of Various Recommender Systems for Lear...

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Data, Computation and Communication (ICDCC), International Conference on

作者： Rashmi P. bijwe Anjali b. Raut Department of Computer Science & Engineering H.V.P.M's College of Engineering & Technology Amravati India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Recommender systems are becoming an essential component of personalized learning environments, providing individual learners with individualized instructional materials. These systems optimize material delivery, increase engagement, and improve academic achievements by leveraging Artificial Intelligence (AI) for learner-centric suggestions. This paper provides a comparative examination of several AI-driven recommender systems utilized in academia. various models were evaluated concerning their accuracy, scalability, and flexibility within the context of collaborative filtering, content-based filtering, and hybrid techniques. Our investigation also includes cutting-edge methods for recommendation systems, including graph-based models and deep learning. The benefits and limitations of each approach are demonstrated, providing information on how AI may enhance personalized learning. Researchers and practitioners can get valuable insights from the analysis to improve the efficiency and efficacy of learning recommender systems.

关键词： Deep learning Reviews scalability Computational modeling Learning (artificial intelligence) streaming media Real-time systems Data models Robustness Recommender systems

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Remaining Useful Life Estimation of Lenses for an Ion beam Etching Tool in semiconductor Manufacturing Using Deep Convolutional Neural Networks 13

Remaining Useful Life Estimation of Lenses for an Ion Beam E...

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13th International Conference on Electronics, Communications and Networks, CECNet 2023

作者： Wan, Jian McLoone, seán Department of Mechanical Biomedical and Design Engineering School of Engineering and Technology College of Engineering and Physical Sciences Aston University BirminghamB4 7ET United Kingdom School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast BelfastBT9 5BN United Kingdom

ISBN: (纸本)9781643684802

Maintenance plays a significant role in semiconductor manufacturing as plant yield, factory downtime and operation cost are all closely related to maintenance efficiency. Accordingly, maintenance strategies in semiconductor manufacturing industries are increasingly shifting from traditional preventive maintenance (PM) to more efficient predictive maintenance (PdM). PdM uses manufacturing process data to develop predictive models for remaining useful life (RUL) estimation of key equipment components. Traditional approaches to building predictive models for RUL estimation involve manual selection of features from manufacturing process data. This paper proposes to use deep convolutional neural networks (CNN) for the task of estimating RUL of lenses for an ion beam etch tool in semiconductor manufacturing. The proposed approach has the advantage of automatic feature extraction through the use of convolution and pool filters along the temporal dimension of the optical emission spectroscopy (OEs) data from the endpoint detection system. simulation studies demonstrate the feasibility and the effectiveness of the proposed approach. © 2024 The authors and IOs Press.

关键词： Optical emission spectroscopy

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streamlined breast Cancer Detection Using Ultra Wideband Radar without Invasive Measures 2

Streamlined Breast Cancer Detection Using Ultra Wideband Rad...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Arunachalam, P. Latha, P. Nessariose Jose, Naduvathezhath Godwin James, T. Prabahar vivekanandan, G. Arya, Aayushi Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sugunthala R&D Institute of Science and Technology No-42 Avadi-Vel Tech Road Vel Nagar TN Chennai India Department of Computer Sciences and Engineering V.S.B. Engineering Collage Karur India Denken Solutions India Department of Computer Science and Engineering India Department of CSE Sri Sairam Institute of Technology Chennai India School of Technology Woxsen University India

ISBN: (纸本)9798350383867

The paper proposes the new radar technology for the first stages of detection of the disease without the subjecting the patient to surgery. In this paradigm, such high-end radar technologies such as bistatic and monostatic patterns assist in identifying small tumours that are far from the surface of the breast tissue. One of them is applying the Ultra-Wideband pulses and the specific type is the seventh derivative Gaussian pulse. These pulses are shaped in a very strict manner, and which makes for sharp transition band pass Finite Impulse Response filters for the best data transfer and to eliminate interferences. The capability for sustaining practices recommended by the Federal Communication Commission concerning spectral standards allows signals to proceed safely through breast tissue. by modifying the amplitude and phase of the transmitted pulse in the study, it increases the power of the main lobe radiated as well as on the same note reduces the levels of sidelobes and losses in the channel as these are accomplished using radar antennas. It found that because of the characteristics of the backscattered signals, there are specific temporal and spectral patterns that can reliably indicate the existence of tumor. based on stimulations, there are huge disparities in electric field strength introduced in normal and malignant breast tissue, which supports the efficiency of the new radar system. Moreover, among the important information accumulating duties we can distinguish specific Absorption Rate (sAR) which specifies the tumour place as it being illustrated by the raised energy absorption in breast malignant tissue relatively to healthy tissue. similar experiments are also carried out with an actual heterogeneous breast phantom with tumors, and it can also be seen that the results are almost identical with the above simulation results and hence it can be understood that the enhanced radar technology is good enough for the earliest detection of breast cancer. At thi

关键词： Diseases

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Comparative study of Customer Churn Prediction based on Data Ensemble Approach

Comparative Study of Customer Churn Prediction Based on Data...

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2023 Intelligent Computing and Control for engineering and business systems, ICCEbs 2023

作者： Kumar, s.Dinesh soundarapandiyan, K. Meera, s. Sri Sairam Engineering College Department of Management Studies Tamil Nadu Chennai India B.S.Abdur Rahman Crescent Institute of Science & Technology Dept. of Commerce Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350394580

Customer Churn is the prima facie problem of any company irrespective of their size and also it has a direct influence on the revenue of the companies. so this makes every company to understand the rationale behind the customer churn and predict the potential customer churn and the reasons for the same. by identifying the factors of churn, the companies will be in adept position to retain the customers. Thence the main intention of this work is to build-up a churn prediction model using machine learning that assists the companies to predict the customers who are most likely to churn. The models were measured by adopting Area Under Curve (AUC) measure, the confusion matrix (Accuracy, Recall, Precision) and F1 score. The ensemble modeling was also adopted in order to compare and consolidate the each models for a common outcome. The models were run through Python (Jupyter Notebook) environment on the big dataset obtained from Kaggle. This research tried out with following model algorithms: Naive bayes Model, Discriminant Analysis, Decision Tree, Random Forest, and Logistic Regression and further to attest the outcome of the data ensemble modeling of Ada boost, Gradient boosting and bagging has also been used. Out of all the models Random Forest model was considered to be the best fit model due to high accuracy, specificity, AUC and FI score. so this model can be used as a classification churn prediction model. © 2023 IEEE.

关键词： Customer Churn Decision Tree Discriminant Analysis Ensemble modeling Logistic Regression ML algorithms Naive bayes Random Forest

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A proactive load balanced handoff scheme for vehicular ad-hoc networks

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International Journal of vehicle Information and Communication systems 2023年第4期8卷 345-362页

作者： Madli, Rajeshwari varaprasad, G. Department of Computer Science and Engineering B.M.S. College of Engineering Bengaluru Karnataka Bangalore India

The embracement of the wireless technology by the automobile industry has led to novel research interests in the field of vehicular Ad-hoc Networks (vANETs). In addition to ad-hoc mode, a vANET supports infrastructure mode of communication, which provides access to internet services such as Hyper Text Transfer Protocol (HTTP), voice over Internet Protocol (voIP) and File Transfer Protocol (FTP), through access points known as Road side Units (RsU). vehicles can benefit from these services when associated with an RsU. However, because of uneven distribution and high mobility of vehicles, some RsUs are overloaded, resulting in network congestion and affecting the packet delivery ratio and throughput. This paper proposes a proactive load-balanced handoff scheme, in which vehicles evade overloaded RsUs and choose an RsU based on signal strength, network load and service reliability. simulation results indicate that vehicles using the load-balanced handoff method, experience a higher packet delivery ratio and throughput. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： vehicular ad hoc networks

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study on Multi-Tone Piano Transcription Algorithm based on bPNN 2

Study on Multi-Tone Piano Transcription Algorithm Based on B...

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2nd International Conference on smart Technologies for smart Nation, smartTechCon 2023

作者： sahaya sheela, M. Abbas, Ahmed H.R. singh, Anil Pratap vijayakumari, G. Parvin begum, I. Anandhi, R.J. Tamil Nadu Chennai India College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science & Engineering IES University MP Bhopal462044 India New Prince Shri Bhavani College Of Engineering And Technology Tamilnadu India Department of Computer applications B.S.Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Vandalur600048 India Department of Information Science & Engineering New Horizon College of Engineering Bellandur Post Bengaluru560103 India

ISBN: (纸本)9798350305418

bPNN ensemble can significantly improve the generalization ability of knowledge system by training multiple bPNNs and synthesizing their conclusions. It not only helps scientists to study machinery knowledge and neural computing in depth, but also helps ordinary engineers and technicians to use bPNN technology to solve real-world problems. Ensemble knowledge has become one of the hot spots in the territory of machinery knowledge in recent years, and selective integration method has become an important direction of ensemble knowledge because of its advantages in adaptability, generalization and combination. In this paper, the transcription of multi-tone piano is studied based on bPNN. The corresponding study methods are used in the research. Through the establishment of data graph and algorithm formula, the corresponding study is carried out. From the research, it can be found that the piano transcription efficiency based on bPNN is very high, up to about 90.43%. In the future, people may pay more Focus to the piano study of bPNN. © 2023 IEEE.

关键词： bPNN Multi-tone piano transcription algorithm

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