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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Das, Priyanka Mukherjee, Chandrani Gupta, Samiksha Roy, Sayan Shah, Khushi Singh, Shreya Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350384369

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique. © 2024 IEEE.

关键词： Food chemistry

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Acoustic data acquisition and integration for semantic organization of sentimental data and analysis in a PWSN

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Multimedia Tools and Applications 2024年 1-23页

作者： Das, Sushovan Mondal, Uttam Kr. Dept. of CSE College of Engineering and Mgmt WB Kolaghat721171 India Dept. of Computer Science Vidyasagar University WB Midnapur721102 India

In an acoustic pervasive wireless sensor network (PWSN), the BASE station plays a vital role in gathering and integrating acoustic sensor data from various nodes, including end and router devices tracking time-driven events. The semantic BASE station is crucial in the IoT landscape as it consolidates data from these networks, enabling thorough sentiment analysis of acoustic signals and yielding insights across domains. A semantic processor at the BASE station is essential for an energy-efficient and intelligent PWSN, managing data collection, integration, signal feature extraction, and publication for model training and sentiment analysis. This paper introduces a novel approach to designing a semantic BASE station, focusing on ontology generation, evaluation, and updates to bolster pervasive wireless sensors in capturing and depicting events and time through an ontological framework. The study addresses challenges in efficiently gathering, integrating, and processing acoustic data from pervasive nodes, proposing a semantic processor at the BASE station to enhance feature extraction and metadata publication. The semantic organization of feature-extracted labeled metadata enables the analysis of comprehensive machine learning (ML) applications such as sentiment analysis, type detection, and environment detection by generating confusion matrix. Evaluation includes performance metrics (NEEN, LSNS, BDAS) as well as accuracy, precision, sensitivity, and specificity for sentimental data analysis to validate the proposed technique’s efficacy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sensor nodes

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Comprehensive Testing Strategies for Water Purity by Assessing and Eliminating Viruses and Bacteria using Machine Learning algorithms

Comprehensive Testing Strategies for Water Purity by Assessi...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Thaluka, Mohan Raj Maheswari, K. Srujan Raju, K. Nuthanakanti, Bhaskar Pavan Kumar, G. Santhuja, P. Cmr Technical Campus Dept. Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350366846

Water, essential for life, is often contaminated with bacteria and viruses, depriving many accesses towards purified drinking and irrigation water. The presence of pathogens in unpurified water puts human health at risk and contaminates crops, leading to waterborne illnesses. Detecting indicator bacteria and viruses in water is crucial for assessing microbiological quality. Despite efforts by regulatory authorities, disease outbreaks linked to drinking water persist globally. The World Health Organization released some threshold values based on some water parameters. The metrics are named as The Water Purity by Assessing and Eliminating Viruses and Bacteria (WPAEVB) and Irrigation WQI (IWQI) which can measure the water quality. This paper proposed a network architecture to analyze all the parameters by using machine learning tools (ML) tools which will determine the drinking water and irrigation water based on virus and bacteria microbiological values. The model is developed based on LoRa and land topology. Here we used three models SVM, logistic regression (LR), and random forest (RF) to know whether irrigation water is being used for drinking water by detecting the percentage and levels of bacteria and virus. The dataset was developed based on the ML models due to the lower availability of datasets related to irrigation and drinking water and bacteria and virus percentage is also calculated by using three models. Following the application of every model, SVM produced the greatest results for irrigation water, while LR produced the best performance for drinking water. The three ML models were applied in order to complete the iterative feature elimination. © 2024 IEEE.

关键词： Bacteria

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Biomimetic and Biological Sensing Technologies for the Assessment of Water Contaminants 1

Biomimetic and Biological Sensing Technologies for the Asses...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Thaluka, Mohan Raj Maheswari, K. Nuthanakanti, Bhaskar Srujan Raju, K. Jonnadula, Narasimharao Okali, Rakshita Dept. Computer science and engineering CMR Technical Campus Hyderabad India

ISBN: (纸本)9798350367720

Biological sensors, including biosensors and biomimetic sensors, have gained significant attention in recent years due to their exceptional capabilities in detecting and monitoring pollutants in water environments. These sensors employ biological components such as bacteria, Fungi, enzymes, antibodies, microbial cells, or biomimetic materials to recognize and quantify specific contaminants to pathogens, with high sensitivity and selectivity. This review provides an in-dept. analysis of the current state-of-the-art in biological sensor technology for water quality assessment. It discusses the underlying principles of biological sensing mechanisms and explores recent advances in sensor fabrication, including materials, immobilization techniques, and signal transduction methods. Moreover, the review examines the diverse applications of biological sensors in water monitoring, ranging from detecting chemical pollutants to assessing microbial contamination. Additionally, challenges such as stability, reproducibility, and field deployment are addressed, along with future directions for research and development to further enhance the performance and applicability of biological sensors in safeguarding water resources and public health. Here, we employed three models - SVM, logistic regression (LR), and random forest (RF) - to determine the biological and chemical components of water, such as bacteria, fungi, enzymes, antibodies, microbial cells, or biomimetic materials, in order to determine whether it is being utilized for drinking water. Because there are fewer datasets available for biological and chemical components, the dataset was created using machine learning (ML) models. Three machine learning models were used to calculate water pollutants percentages, with SVM proving most effective for irrigation and domestic water, and LR for drinking water, resulting in accurate water supply. © 2024 IEEE.

关键词： Antibodies

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Building Network Digital Twins Part I: State Synchronization 3

Building Network Digital Twins Part I: State Synchronization

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3rd International Conference on 6G Networking, 6GNet 2024

作者： Sengendo, John Granelli, Fabrizio University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350378597

The Network Digital Twin is emerging as a promising technology for future networks, including 6G, as it allows to gain deep knowledge of its Physical Twin and to perform 'what-if' analyses in a controlled environment. However, while the concept of Digital Twin reached maturity in other fields (such as manufacturing), still the application of this concept to networks presents some relevant challenges. This paper studies the problem of synchronization between the Digital and the Physical Twin, a key functionality to enable the Digital Twin to accurately replicate the behaviour of the Physical Twin. Indeed, accurate synchronization is challenging due to the amount of data to exchange and the delays due to acquisition, modeling and transmission of the related information. The paper analyzes the delay chain in the synchronization process and introduces the concept of 'negative delay' through accurate prediction in order to minimize the synchronization delay and enable real-time or 'near' real-time synchronization between the Physical and the Digital Twin. © 2024 IEEE.

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Augmenting Sentiments into Chat-GPT Using FacialEmotion Recognition 10

Augmenting Sentiments into Chat-GPT Using FacialEmotion Reco...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Iyer, Aditya A. Vojjala, Saipranav Andrew, J. Manipal Institute of Technology Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350384369

This research initiative addresses the task of enhancing Chat Generative Pre-trained Transformer's (ChatGPT's) conversational capabilities by integrating the comprehension and response to user emotions conveyed through facial expressions. The central challenge lies in refining the AI system's proficiency to tailor responses according to users' detected emotional states. In this pursuit, our study adopts a comprehensive approach, aiming to seamlessly incorporate emotional intelligence into AI-driven interactions. To achieve this objective, the methodology involves integrating real-time sentiment analysis based on facial expressions into the ChatGPT framework. This is carried out through the utilization of a deep convolutional neural network (DCNN) architecture, designed to recognize and interpret various emotions exhibited in facial expressions. The primary goal is to enable ChatGPT to dynamically adjust its responses, fostering a more empathetic and contextually relevant interaction with users. In terms of evaluation metrics for facial expression recog- nition, our assessment employs a confusion matrix to quantify the model's performance across different emotional categories. Additionally, a heuristic approach is implemented, wherein the sum total probability of each detected emotion is calculated over the duration the user enters the prompt. These evaluation methodologies aim to provide a comprehensive understanding of the model's accuracy and effectiveness in discerning and responding to user emotions. Overall, this research contributes to the ongoing endeavor of imbuing AI systems with emotional intelligence, paving the way for more nuanced and human-like interactions. © 2024 IEEE.

关键词： Chatbots

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An algorithm frequently mines a set of items for big data to enhance efficiency

An algorithm frequently mines a set of items for big data to...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Sonkar, Sanjay Kumar Singh, Balkar SunRise University Dept. of Computer Science and Engineering Rajasthan Alwar India

ISBN: (纸本)9798350382723

In the landscape of rapidly expanding data streams, deriving meaningful insights, particularly frequent itemsets, within massive datasets at a swift pace poses a significant challenge. Addressing this challenge, various methodologies for Frequent Itemset Mining have been proposed, yet they struggle with low support counts and efficiency concerns in handling large datasets. In response, our research introduces Jagged Itemset Counting (JIC) methodologies, aiming to effectively mine Frequent Itemsets from extensive data. The core objective revolves around devising a robust algorithm capable of identifying all Frequent Itemsets, irrespective of database size or the nature of the itemset. Central to this approach is the introduction of a straightforward label representation, GPLN (Geometric Progression Label Number), assigned to each frequent item. Utilizing CGPLN (Cumulative Geometric Progression Label Number), derived from the arithmetic sum of GPLNs within transaction subsets, forms the CGPLN-Label representation for each transaction subset (itemset). Comparative analysis reveals superior performance of JIC over Apriori and Eclat algorithms for small and medium-sized datasets, exhibiting efficiency even at minimal support thresholds. In the realm of Big Data, where FP-Growth and Eclat falter, the proposed technique shines with faster execution times, optimized main memory utilization, and efficient disc memory usage. © 2024 IEEE.

关键词： Data mining

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Deciphering Deception: Unmasking Fake News in Multilingual Contexts 5

Deciphering Deception: Unmasking Fake News in Multilingual C...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Agarwal, Ashutosh Singh, Yash Pratap Rai, Vipin Galgotias University Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

A major global worry in the ever-expanding digital ecosystem is the spread of incorrect information. The ability to recognize false news in non-English languages is still lacking, despite great advancements in the identification of false news in English. This research looks at the unique opportunities and problems that these languages bring. It offers a thorough assessment and comparative analysis of existing approaches, highlighting their advantages and disadvantages. By doing comparative study of various differ techniques we have found that Multilingual-Fake Model currently works the best. The results of the study highlight how critical it is to use inclusive and linguistically varied tactics in order to successfully counter disinformation. It emphasizes how crucial it is to create trustworthy tools and processes that can adjust to the subtleties of many languages and their socio-cultural settings. It highlights how crucial it is to comprehend these components completely in order to create detection methods that are more potent. The study admits that the dissemination of incorrect information is an issue that is deeply rooted in the social environment of linguistic groups and transcends language borders. A discussion of possible future study areas closes the report. In a multilingual culture, this study marks a substantial breakthrough toward the development of more thorough and efficient techniques for spotting bogus news. © 2024 IEEE.

关键词： Bidirectional Transformer fake news multi-lingual

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Sustainable Decision in using Tuned Liquid Column Dampers for the Structural Energy Dissipation of Earthquake

Sustainable Decision in using Tuned Liquid Column Dampers fo...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ibrahim, Basil Leblouba, Moussa Barakat, Samer Mohamed, Hamdy M. Elzefzafy, Hend Univerity of Sherbrooke Dept. of Civil and Building Engineering Quebec Canada University of Sharjah Dept. of Civil and Environmental Engineering Sharjah United Arab Emirates College of Engineering Applied Science University Bahrain

ISBN: (纸本)9798350369106

This paper introduces a novel simplified modeling approach for tuned liquid column dampers (TLCDs) designed to enhance the response control of single degree of freedom (SDOF) systems. The study focuses on the operational principles of TLCDs, which utilize the inertia of liquid within a column to dissipate energy and mitigate vibrations induced by dynamic loads, such as those from wind and seismic activity. Through the linearization of the governing equations of motion using MATLAB Simulink, the effectiveness of TLCDs is analyzed under the influence of El Centro ground motion. A comparative case study evaluates the structural response of an SDOF system both with and without the integration of a TLCD, demonstrating the significant benefits of employing these dampers in reducing acceleration and displacement. Results indicate that TLCDs dissipate motion significantly faster, achieving an 80% reduction in structural displacement within the first 10 seconds of El Centro ground motion. The findings suggest that properly tuned TLCDs can substantially enhance the stability and safety of structures subjected to dynamic forces. © 2024 IEEE.

关键词： Dynamic loads

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An Effective Pre-Owned Car Price Prediction Based on Multi Linear Regression Technique 1

An Effective Pre-Owned Car Price Prediction Based on Multi L...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Kumar, Aman Singh, Dheeraj Chauhan, Keshav Guru Prasad, M.S. Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350341737

The market for the purchase and sale of second- hand automobiles has seen a trend towards expansion in recent years. Used vehicle buyers and sellers are able to conduct business at rates that are acceptable and fair thanks to scientific examination of automobile prices. This contributes to a reduction in the risks associated with conducting transactions in the used car market and encourages the market to continue to grow in a sustainable manner. Even though a lot of effort has been done on predicting the price of used cars, there are still a lot of obstacles standing in the way of an accurate and reliable estimate. Predictions of the prices of used automobiles that are accurate can assist individuals in making wise choices and avoiding the inflated costs of used vehicles on the market to the greatest extent possible. In this work multi linear regression technique is used, which is a type of supervised machine learning algorithm to build a model for predicting the price of pre-owned cars effectively. The dataset used for the prediction was collected from Kaggle webpage. After training and testing the model, the proposed model deployed using flask framework. The accuracy of the proposed model is 89.23% based on testing data. © 2023 IEEE.

关键词： Automobiles

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