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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Tyagi, Neha Mishra, Rashmi Patel, Sitanshu Kumar Karki, Somraj Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798331523893

In order to facilitate better data analysis, clustering is a prominent data mining approach which separates data into separate categories based on their similarities and distinctions. It's a method of unsupervised learning which may generate a model with a training dataset. Throughout this study, the Euclidian distance and EPS (Epsilon) value has been determined using the DBSCAN technique based on the similarities as well as disparity for the input data. Moreover, Euclidian distance is dynamically determined using the back propagation method;a simulation study is carried out that demonstrates both speed and precision improvements. DBSCAN, or spatial clustering based on density of application with noise, is a common clustering technique for geographic datasets that can locate clusters of any shape and automatically detect noise points. To deal with the problem, an improved DBSCAN technique based on the selection of features (FS-DBSCAN) has been suggested. However, the algorithm proved unstable while handling huge dimensional data. Many simulation via real-world datasets testify the algorithm's efficiency. Comparison of the proposed algorithm to the DBSCAN algorithm demonstrates its benefits. Structures within the collections may be identified by dividing of collections of products into classes and clusters, with a focus on increasing the resemblances of objects within each cluster and lowering the similarities of things belonging to different clusters. © 2025 IEEE.

关键词： Backpropagation

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Designing Document stores using Feature Model and Application Workload 3

Designing Document stores using Feature Model and Applicatio...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Saha, Sweta Sachdeva, Shelly Nit Delhi Department of Computer Science and Engineering India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

NoSQL databases have become the go-to choice for managing diverse and dynamic datasets in today's data-driven landscape. Despite NoSQL offering flexibility, designing efficient schemas for these databases remains a challenge, necessitating careful consideration of factors like scalability, consistency, and performance. Aligning to works done in the field of optimal schema design in NoSQL database, our approach focuses on designing a framework that converts ER schema into NoSQL Schema by using Feature Model and user queries. ER schema as ER diagrams were chosen as they are suitable for taking a more detailed look at information when the database contains more data. The feature model provides a generalized approach to capture the data-related needs (i.e., the data types, their attributes, and the various relationships among them), and the workload queries capture the functional needs (i.e., what users want to do with the data). In the modeling phase, the workload queries are converted to query graphs, and a label is generated from the data volume in the query graphs. This label is further assigned to the schema graph created from the ER diagram;based on this assigned label, multiple feature models are composed, which are finally converted to a logical schema. Queries were performed and compared with the existing method. The results showcase that aligning schema design with data-related and functional needs using feature model leads to better organization and improved Query performance. © 2024 IEEE.

关键词： Structured Query Language

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A fast and highly scalable frequent pattern mining algorithm

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Future Generation computer Systems 2024年 160卷 854-868页

作者： Cheng, Wan-Shu Lin, Yi-Ting Huang, Peng-Yu Chen, Ju-Chin Lin, Kawuu W. Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

As the use of big data and its potential benefits become more widespread, public and private organizations around the world have realized the imperative of incorporating comprehensive and robust technologies into their business processes. In particular, companies are implementing more and more intelligent systems into their business processes, resulting in an exponential increase in the amount of data being collected and used in everyday life on a quarterly basis. Therefore, an algorithm that can efficiently explore the frequent patterns from big data will be able to productively analyze and utilize the generated data to better optimize resources and even develop new business models. Thus, in this urban data era, it is not only essential to make more efficient and accurate decisions to improve business profitability and customer satisfaction, but also a highly challenging core issue. In this study, a new approach, scalable FP mining, is proposed for the first time to achieve a certain level of performance and more efficient memory utilization despite large amounts of data. Experiment results for different data characteristics show that the best performance of the proposed method requires only 48.9% of the DP algorithm's execution time. In further experiments with increased exploration difficulty, the optimal execution time is only 37%. On specific associated databases, the proposed method maintains stable and excellent performance with 12.7% for different data characteristics. The experimental results with increasing exploration difficulty at each stage demonstrate the stability, robustness, and reliability of the proposed SFP, especially under high data complexity and exploration difficulty. © 2024

关键词： Data mining

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Conversational AI Chatbot Using Log Pattern Detection

Conversational AI Chatbot Using Log Pattern Detection

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Tamil Iniyan, K.S. Rakesh, M. Khan, Umar Ahmed Shashikala, H.K. Khan, Adeebulla Das, Rahul University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350388800

This application is a self-learning and self-healing capability solution for users. The backend system is integrated with multiple data sources relevant to the application, user transaction database, system infrastructure and application services etc., to capture the user interaction with the application and the issues he/she faced before hand and send personalized message to the user. This will provide either a self-healing capability or the agent is ready with the required data to help the customer answer his queries. The solution can be integrated for any domain mobile or web applications application. The application uses log pattern detection to detect a potential outage and solve the upcoming error using different approaches. The application also sends personalized message to the user according to his interests. This application uses different AWS services for creating conversational interfaces between the user and the agent, to provision and manage servers, to store and retrieve data anytime from the web, to train the AI to analyze the log patterns and detect any irregularities etc. © 2024 IEEE.

关键词： Chatbots

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Delineating Emotions in Speech: Comparative Insights from Machine Learning and Deep Learning 15

Delineating Emotions in Speech: Comparative Insights from Ma...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shareefunnisa, Syed Kausik, Chintalapati Vishnu Kaumudi, Chintalapati Lalitha Sekhar, Eluri Raja Department of Computer Science and Engineering AP Guntur India

ISBN: (纸本)9798350370249

This study utilizes multiple machine learning algorithms to investigate Speech Emotion Recognition (SER). We examine how well complex patterns in speech data may be captured by Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In contrast, we assess classical machine learning models trained using Mel-frequency cepstral coefficients (MFCCs) derived from speech, such as Support Vector Machines (SVM), Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). To potentially achieve better performance, we examine ensemble approaches that combine standard machine learning with deep learning. A thorough study of these methods' performance in SER tasks may be attained by testing them on the RAVDESS and TESS datasets. The results are intended to aid in the creation of reliable speech-to-text emotion identification algorithms for enhanced HCI (Human computer Interaction). © 2024 IEEE.

关键词： CNN Deep Learning Feature Extraction RNN

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Thermoelectric infrared detectors:Design,fabrication,and performance assessment

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Journal of Semiconductors 2024年第12期45卷 95-110页

作者： Daryoosh Vashaee Department of Electrical and Computer Engineering North Carolina State UniversityRaleighNC 27606USA Department of Materials Science and Engineering North Carolina State UniversityRaleighNC 27606USA

This study presents a comprehensive optimization and comparative analysis of thermoelectric(TE)infrared(IR)detec-tors using Bi_(2)Te_(3) and Si *** theoretical modeling and numerical simulations,we explored the impact of TE mate-rial properties,device structure,and operating conditions on responsivity,detectivity,noise equivalent temperature difference(NETD),and noise equivalent power(NEP).Our study offers an optimally designed IR detector with responsivity and detectivity approaching 2×10^(5) V/W and 6×10^(9) cm∙Hz^(1/2)/W,*** enhancement is attributed to unique design features,includ-ing raised thermal collectors and long suspended thin thermoelectric wire sensing elements embedded in low thermal conductivity organic materials like ***,we demonstrate the compatibility of Bi_(2)Te_(3)-based detector fabrication pro-cesses with existing MEMS foundry processes,facilitating scalability and ***,for TE IR detectors,zT/κemerges as a critical parameter contrary to conventional TE material selection based solely on zT(where zT is the thermoelec-tric figure of merit andκis the thermal conductivity).

关键词： thermoelectric infrared focal plane arrays micromachining Bi_(2)Te_(3)

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Attention-based Improved YOLOv5 for Effective Waste Detection and Classification System

Attention-based Improved YOLOv5 for Effective Waste Detectio...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Anupriya Kumar, Ratish Chandigarh University Computer Science and Engineering Mohali India

ISBN: (纸本)9798350378092

Deep learning architectures are now prevalent among several application domains due to their better performance. Waste detection and classification are two applications that can be enhanced using deep learning. This work focuses on vision-based analysis using only camera sensors;other sensors are not included due to cost optimization. Different waste detection approaches were earlier designed based on computer vision and image processing, but there is a scope for improvement in detection accuracy. With this aim, this work proposes an improved YOLOv5 architecture. This architecture makes use of both spatial and channel attention to enhance the performance of the suggested model and extract more pertinent information during feature learning. The waste recycling plant dataset (WaRP-D) is used to analyze the performance of the improved architecture. This dataset contains image data of different waste objects such as bottles, containers, cans, cardboard, etc.;their annotations are also publicly available. The performance of our proposed model is measured considering various parameters in terms of precision, recall, and mean average precision with different thresholds (mAP0.5 and mAP0.5-0.95). A comparison of the proposed architecture is carried out using simple YOLOv5 architecture, which shows the effectiveness of the proposed approach. This work also compares the classification performance of this improved YOLOv5 architecture with gated recurrent units (GRU) and an optimized GRU using grey wolf optimization (GWO-GRU) on the Waste Segregation Image dataset. © 2024 IEEE.

关键词： Image annotation

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A Comprehensive Analysis of Ultralightweight Secure Communication Protocol

A Comprehensive Analysis of Ultralightweight Secure Communic...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Singh, Prashansa Kaur, Manjot Bajaj, Rohit Computer Science and Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350388800

The proliferation of wireless sensor networks and Internet of Things (IoT) devices in the quickly changing digital landscape has highlighted the urgent need for strong security solutions that can handle these systems' limited resources. A key solution to this problem is the emergence of ultra- lightweight security protocols, which provide strong security features while respecting the strict computational, energy, and memory constraints imposed on these kinds of devices. This indepth analysis explores the field of ultra-lightweight security protocols, offering a thorough examination of their evolution, salient features, and the particular security issues they resolve. We carefully examine and contrast different protocols, pointing out their advantages and disadvantages as well as compromises between resource limitations and security resilience. In addition, this review highlights recent developments and advancements in the field, pointing out new trends and possible avenues for future research. This study aims to be a useful resource for researchers, practitioners, and developers, guiding the design and implementation of safe, effective, and scalable systems in the Internet of Things era by providing a comprehensive overview of ultra-lightweight security protocols. © 2024 IEEE.

关键词： Internet of things

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ESAF: An Efficient and Secure Authentication Framework for V2G Network 16

ESAF: An Efficient and Secure Authentication Framework for V...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mehta, Prarthana J. Parne, Balu L. Patel, Sankita J. Department of Computer Science and Engineering Gujarat Surat India

ISBN: (纸本)9798350383119

Electrical Vehicles (EVs) are integrated into the Smart Grid (SG) network to enhance energy flow and fulfill future energy demand using the Vehicle to Grid (V2G) technology. The V2G supports bidirectional information sharing among the power grid and EVs. The EVs can send a request to charge their batteries from the charging station and transfer excess energy to the power grid from their batteries. However, the communication is carried out over a public channel and that leads to several security and privacy concerns in the V2G network. Moreover, the IoT devices embedded in the EVs and charging stations are resource constrained in nature. So, there is a need to overcome the mentioned security and privacy issues with lightweight secure algorithms. In addition, EVs and charging stations are not protected by a human thus, it is necessary to protect them from physical attacks as well. To address these limitations, we propose Physically Unclonable Function (PUF) based an Efficient and Secure Authentication Framework (ESAF) to carry out secure communication in the V2G network. The ESAF is suitable for resource constrained networks as it uses lightweight basic concatenation, bitwise XoR, and one way hash operations. The correctness of the ESAF is validated through the security analysis using the BAN logic and AVISPA tool. In addition, the performance of the proposed ESAF illustrates that the computation and communication overheads are competitive with improved security. © 2024 IEEE.

关键词： Authentication Fuzzy Extractor PUF Session Key Smart Grid User Anonymity V2G

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Cognitive Resonance in Emotional Decoding: A CNN-ResNetB0 Synergy 2

Cognitive Resonance in Emotional Decoding: A CNN-ResNetB0 Sy...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Vidya, P. Chattopadhyay, Sharon Bhavatarini, N. Reva University Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350371055

Emotion recognition from facial expressions has gained significant attention for its abeyant applications in human-computer synergy and affective computing. In this research paper, we extant a human face emotion detection model planted on Convolutional Neural Networks (CNNs). Our model weights the power of CNNs to automatically learn and excerpt discriminative lineaments from facial portraits. We introduce a comprehensive dataset containing a diverse range of emotions, enabling robust model training and evaluation. Additionally, we explore the interpretability of the model by visualizing activation maps to identify the regions of the face contributing most to emotion classification. The proposed model's efficiency and realtime applicability make it a promising candidate for emotion-aware systems in real-world scenarios. This work offers a robust and interpretable solution for human face emotion detection, utilizing Convolutional Neural Networks, with high accuracy and potential applications in domains where understanding human emotions is crucial. © 2024 IEEE.

关键词： CNN emotion detection emotion recognition facial expressions

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