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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Singh, Arvind Tripathi, Abhinandan Buddha Institute of Technology Dept. of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

The number of reported instances of cyber abuse has also increased in tandem with the growth in the quantity of people that are regularly using the internet. Users' online freedom and privacy are jeopardized by such occurrences. Traditionally, the removal of such content from the internet has been accomplished through manual moderation and reporting systems. This strategy has, however, certain drawbacks, such as a reliance on people, longer delays, and less privacy for data. Traditional recurrent sequence models and supervised machine learning have been used in previous attempts to automate this procedure, but they have a tendency to perform badly on non-English material. A versatile solution that can handle multilingual text is urgently needed, given the increasing diversity of consumers using the internet. Moreover, translations frequently lose important context and emotion from content written in vernacular languages. In this work, we offer a generative deep learning based method for the detection of cyber abuse throughout Hindi, English with mixed codes, and Hindi text. The method uses bidirectional transformer-based BERT *** the TRAC-1 standard aggressiveness identification task, the suggested architecture can get extremely positive outcomes on the task leaderboard for English and accomplish cutting-edge performance on the Hindi dataset with mixed codes. The outcomes show that the new strategy is superior to the current ones because they were obtained without the need of numerous models or ensemble-based methods. Effective models that utilize deep learning techniques to analyze multilingual text can process a wider variety of inputs, making them indispensable in combating these types of social ills. © 2024 IEEE.

关键词： Generative adversarial networks

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Negation Handling and Domain Generalization in Sentiment Analysis using Machine Learning Models

Negation Handling and Domain Generalization in Sentiment Ana...

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2024 International Conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Biradar, Shrnivas Raju, G.T. Divakar, K.M. SJC Institute of Technology Dept. of Computer Science and Engineering Chikkaballapur India SJC Institute of Technology Dept. of Information Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Conventional lexicon-based approaches to sentiment analysis typically lack the necessary methods to properly identify the negation window, making it impossible to model negation. An enormous increase in sentiment-rich electronic and social media has been observed daily. Negation modifiers cause problems for Sentiment Classification techniques and have the power to entirely change the discourse's meaning. Therefore, it becomes essential to manage them well. Opinion mining or sentiment analysis is the study of people's attitudes, feelings, and views as they are expressed in written language. It is one of the busiest text mining and natural language processing research projects. Even though sentiment analysis research has gained popularity in the field of natural language processing, for this problem, the state-of-the-art machine learning approach is based on Bag of Words. But the BOW model pays little attention to polarity shift, which could have a distinct overall effect. One of the main issues with doing sentimental analysis on any given text or sentence is handling polarity shift, which is what this study attempts to address. Sentiment analysis use Natural Language Processing principles to identify negation in the text. Our goal is to identify the negation effect on customer reviews that, although appearing good, are actually negative. The suggested modified negation methodology helps to increase classification accuracy by providing a method for computing negation identification. In terms of review classification by accuracy, precision, and recall, this approach yielded a noteworthy outcome. When test and training data are from distinct domains, machine learning faces the challenge of domain generalization. Despite the large body of research on cross-domain text classification, the majority of current methods concentrate on one-to-one or many-to-one domain adaptation. Our domain generalization method regularly outperforms state-of-the-art domain adaption methods, a

关键词： Natural language processing systems

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Financial Health Assessment for Households in Kenya

Financial Health Assessment for Households in Kenya

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2024 International Conference on Information and Communication technology for Development for Africa, ICT4DA 2024

作者： Kinyanjui, Samson Lopuran, Felix Kimanga, Peter Mugoh, Edna Kiprotich, Dennis Gikunda, Patrick Dedan Kimathi University of Technology Dept. Computer Science Nyeri Kenya

ISBN: (纸本)9798331527792

The analysis of fiscal position represents significant in the encouragement of economic stability and development specifically to the country like Kenya. This work employs the clustering analysis technique in order to assess the Kenyan households' financial literacy based on a financial dataset. These include transaction details, current incomes, expenditures, savings and investment practices among the clients. This paper makes use of K-means clustering technique to investigate the various clusters of households that possibly exist based on their transactional data and assess the financial characteristics and health profiles of the respective clusters. The data used was gathered from 298 distinct households across 5 counties in Kenya. Using correlation analysis and variance threshold, a feature selection method, which aimed at identifying features depicting the greatest extent of clustering tendency was also created. The clustering resulted into eight groups mainly identifiable by transaction direction, transaction value,transaction family, transaction purpose, and mode of transaction. The findings are useful in forecasting and mitigating potential risks within financial planning and management, and may improve financial wellbeing and sustainability at the household level. The project demonstrates how it is possible to implement machine learning methodologies that provide more valuable information at more efficient assessments of the financial well-being of households in Kenya. © 2024 IEEE.

关键词： Decentralized finance

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Evaluating the Effectiveness of Anonymization systems Against Website Fingerprinting Attacks Using Machine Learning Algorithms: Implementation and Countermeasures 1

Evaluating the Effectiveness of Anonymization Systems Agains...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al Azzani, Abdoulwase Shaawa, Abdulqader Dept. Computer Science and Information Technology Sana’a University Sana’a Yemen

ISBN: (纸本)9798331533557

Website Fingerprinting (WF) is a statistical traffic analysis attack, that allows a local, passive eavesdropper to determine a client’s web activity by leveraging features from her packet sequence. These attacks break the privacy expected by users of Privacy Enhancing Technologies (PETs). The basic idea of this kind of attack is to derive a website’s fingerprints from traffic and observed patterns, which can be followed when a website is visited. A library of website fingerprints is created for many interesting websites. This library of previously recorded fingerprints is then compared with the traffic patterns, which could be observed if some anonymization service is used. This lets the attacker learn which websites are downloaded, even if all traffic is encrypted. In this paper, the attack was implemented against the offered protection by a specific anonymization system to reproduce the results reported in the literature. We achieved over 94% accuracy when applied on a new set of monitored sites. Finally, a new suggested cost-effective countermeasure was proposed, reducing the success of those attacks to less than 50%. © 2024 IEEE.

关键词： Anonymity

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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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Identification of Sugarcane Leaf Diseases and Deficiency Disorders Using Transformers 1

Identification of Sugarcane Leaf Diseases and Deficiency Dis...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Sarkar, Subhasish Kumar, Gautam National Institute of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350365016

Early identification of plant diseases and nutrient deficiencies is crucial for ensuring healthy food production, especially for crops like sugarcane, which contribute significantly to our food supply. Farmers often struggle to differentiate between diseases and deficiencies due to similar symptoms. To overcome such challenges, this study leverages the application of transformers to classify whether the given input is suffering from disease or deficiency. For this purpose, two transformers, i.e., Vision Transformer (ViT) and Swin Transformer is trained on the dataset having leaves of sugarcane. The performance of the model is calculated in terms of precision, recall, accuracy and F-score. From the results, it is found that the Vision transformer with an accuracy of 99.6% can classify diseases and deficiencies more accurately compared to the 99.2% accuracy for Swin transformer. © 2024 IEEE.

关键词： Plant diseases

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1 AI-Powered Facial Expression Analysis In Real Time: Unveiling Emotions 1

1 AI-Powered Facial Expression Analysis In Real Time: Unveil...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Aishwarya, D. Sandeep, Ponduru Akhilesh Varma, Penmatsa V.P.S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371802

This study introduces a novel facial deconstruction method for face emotion identification. After identifying facial landmarks with the IntraFace algorithm, seven regions of interest (ROI) are extracted, representing the principal featuresof the face: the mouth, nose, an area between the eyebrows, right and left eyes, and eyebrows. Next, a variety of local descriptors, such as Local Binary Patterns, Completed Local Binary Patterns. Local Ternary Patterns, and Dynamic Local Ternary Patterns, are employed to capture distinctive features. Inputting the feature vector which represents a face image into a multi-class support vector machine completes the recognition process. The suggested method outperforms previous facial deconstruction techniques, according on experimental findings on two publicly available datasets. © 2024 IEEE.

关键词： Local binary pattern

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Multi-Musical Instrument Recognition Neural Network 6

Multi-Musical Instrument Recognition Neural Network

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Timouri, Bardia Aulakh, Ravdeep Cruz, Gathrean Dela British Columbia Institute of Technology Dept. of Computer Science Vancouver Canada

ISBN: (纸本)9798350374162

Artificial intelligence continues to evolve, particularly in the realms of natural language processing (LLMs), image generation, and task automation. Despite these advancements, multi-musical instrument recognition remains a challenging area with limited effective solutions. Addressing this, our research introduces an innovative methodology using a convolutional neural network (CNN) embedded within an artificial neural network framework. This method utilizes the OpenMIC-2018 dataset, meticulously refined for our purposes. We process entire songs by converting them into mel-spectrograms, which are instrumental in distinguishing subtle variations in pitch, dynamics, and timbre. Our approach sets a new benchmark in the field, adept.y identifying and categorizing up to 10 distinct musical instruments within complex audio recordings. It boasts an impressive F1 score of 56%. This significant achievement not only advances audio signal processing but also highlights the versatility and effectiveness of CNNs in handling sophisticated tasks like multi-instrument recognition. Our work serves as a stepping stone for future explorations in audio recognition, potentially paving the way for more nuanced and accurate audio analysis in various applications, from music production to sound engineering. © 2024 IEEE.

关键词： Spectrographs

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The Cubic Bipolar Neutrosophic Sets theory and Uncertainty Management in Environmental Data Analysis

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Neutrosophic Sets and systems 2024年 72卷 60-133页

作者： Salama, A.A. Khalid, Huda E. Mabrouk, Ahmed G. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dean of higher Institute of Computer Science and Information Systems Ministry of Higher Education October City Egypt Telafer University Telafer Iraq Higher Institute of Computer Science and Information Systems Ministry of Higher Education October City Egypt

Environmental data analysis often faces uncertainties in measurements. Cubic Bipolar Neutrosophic Sets (CBN Sets) provide a powerful framework to address this challenge. This paper explores the mathematical foundations of CBN Sets and highlights their practical applications through illustrative examples. We demonstrate how CBN Sets effectively capture varying degrees of certainty, possibility, and impermanence in environmental parameters. The concept of inclusion relations facilitates comparisons and information fusion. Fundamental set operations (intersection, union, complement) are explored for manipulating and analyzing uncertain environmental data. We present the application of CBN Sets in water quality assessment, highlighting their ability to analyze parameters while accounting for measurement uncertainties. The potential for air quality monitoring using CBN Sets is also discussed. Finally, distance measures and similarity coefficients are introduced to quantify relationships between air quality characteristics from different stations. By leveraging CBN Sets and their associated operations, researchers can gain a more nuanced understanding of environmental data, enabling informed decision-making for a healthier planet. © 2024, University of New Mexico. All rights reserved.

关键词： Environmental monitoring

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Pre-Harvest to Post-Harvest: A Review of AI and IoT Applications in Smart Agriculture and the Prospects of 6G-Enabled IoT Framework 27

Pre-Harvest to Post-Harvest: A Review of AI and IoT Applicat...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Bhola, Amit Sharma, Himanshu Sagar, Anil Kumar Kumar, Prabhat Sharda Univesity Dept. of Computer Science and Engg Greater Noida India National Institute of Technology Dept. of Computer Science and Engg Bihar Patna India

ISBN: (纸本)9798350392319

Farmers are increasingly adopting Smart farming worldwide, leveraging various advanced technologies. Artificial Intelligence (AI) is instrumental in driving the evolution of smart agriculture applications. Internet of Things (IoT), edge computing, cloud computing, and big data are among the forefront technologies employed in this field. Agricultural activities typically encompass three phases: pre-harvest, during harvest, and postharvest. The pre-harvest phase involves choosing seeds, preparing the farm, and selecting the crops. Harvesting tasks include crop classification, disease analysis, and pathogen detection. Post-harvesting activities involve storage, cooling, and reaping, among others. This study conducted an in-dept. review of activities in each phase, aiming at shortcomings related to AI-based Machine and Deep learning, IoT, security, datasets, and methodologies employed in existing research. This work also proposes a smart farming framework based on 6G technology, utilizing these identified research gaps. Additionally, the work conducted a comparative analysis between this survey and existing studies, revealing that the survey offers a more comprehensive overview in various aspects. © 2024 IEEE.

关键词： Smart agriculture

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