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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rajkumar, K. Ragupathi, T. Karthikeyan, S. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Department of Computer Science and Engineering Bangalore562112 India

ISBN: (纸本)9798350371055

The main objective of this project is to create a user-friendly intelligent chatbot for Medical guidance and support. This application has been classified into two different modes based on user preference, those are Online and Offline modes of interaction. In the Online mode of interaction, there is no need for any automation, there will be a real person at the other end answering user queries. Users can even ask for untrained data and will receive instant replies. This application is similar to a normal chat application, but the main disadvantage is that a real person is needed at the other end at all times. In the Offline mode of conversation which is entirely automated the user will be performing an interaction with the chatbot itself. The chatbot must consist of data sets and trained data collection, in this process there is no need for any real person, the chatbot itself will reply instantly based on the trained data. The chatbot will be more accurate when a large number of data is trained. This application is compatible with working in various browsers and varied networks. The Admin of the application plays a significant role. Admin can access all the given functions and is responsible for data training. data training plays a vital role in this application, the trained data will be used by the chatbot while answering user queries i.e. Offline mode. This chatbot application can be accessed by several users at the same time, where IP address plays the main role. The main function of this application is that user can post their corresponding queries and the application will be providing relevant information based on queries. The Latest Modern chatbots are used in applications where simple interaction is needed, for example, service and marketing technology to provide answers such as products, services, and consultancy. If the customer's queries don't match with the trained data sets, the customer will be escalated to manual interaction i.e. Online mode. © 2024 IE

关键词： Automated Intelligent Chat IP Address Offline mode Online mode Trained data

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Conversation Sentiment Analysis in League of Legends Game Community

Conversation Sentiment Analysis in League of Legends Game Co...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Pongkhan, Thutchaphong Khoosirirat, Phutanate Songmuang, Pokpong Kongkachandra, Rachada Thammasat University Data Science And Innovation College Of Interdisciplinary Studies Pathum Thani Thailand Thammasat University Faculty Of Science And Technology Department Of Computer Science Pathum Thani Thailand

ISBN: (纸本)9798350370096

Gaming communities are groups of people from different nations, religions, genders, and ages who come to play games and have discussions about the games. However, some gaming communities are plagued by toxic behavior of people in communities, which negatively affects to other players. A Conversation Sentiment Analysis in the League of Legends Game Community is about analyzing the toxicity of the League of Legends game community by using the Sentiment Analysis methods which have Bag-of-Words and Weight Score method, and then comparing it with three machine learning techniques: Logistic Regression, Support Vector Machine (SVM), and CatBoost, and showing how members in this community have interacted with other members in the community by using the Conversation Mapping Interactive method. © 2024 IEEE.

关键词： Sentiment analysis

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Offline Signature Forgery Detection Based on Geometric Measures Using Tensorflow Model 2

Offline Signature Forgery Detection Based on Geometric Measu...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Lakshmi, A. Anagha Reddy, G. Siddarth Reddy, M. Sowmya Kathirisetty, Nikhila Vardhaman College of Engineering Department of Information Technology Hyderabad India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The most popular method for identifying people from past signatures is through signatures. By using a TensorFlow model which is a deep learning algorithm, we created a new system to verify signatures on bank checks and other important documents. The main point is that a signature plays a crucial role in banking because it is necessary for money withdrawals. In banks there are no efficient systems to check if a signature is real or fake as it is the most frequently used bio-metric method to verify a person's identity. This can lead to bank fraud. The Project will make it easier to tell whether a signature is genuine or not. Online and offline verification are the two methods of verification. We're going to use various geometric measurements to accomplish offline verification. Python libraries were employed in this case. In the testing phase the model's performance is evaluated. To protect the integrity and legitimacy of handwritten signatures, signature forgery detection is an essential responsi-bility. The capacity to recognize forged signatures with accuracy has grown more important as a result of the rise in digital transactions and the growing reliance on electronic documents. The act of duplicating or copying another person's signature with the purpose to deceive or gain illegal access is known as signature forging. Manually spotting these forgeries can be difficult because expert forgers are adept at closely imitating the visual style and traits of real signatures. Therefore, there has been a lot of interest in the development of automated methods and algorithms to identify fake signatures. © 2024 IEEE.

关键词： data mining

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Optimizing Diabetes Prediction Models for Enhanced Health data Processing 5th

Optimizing Diabetes Prediction Models for Enhanced Health Da...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Chatterjee, Soham Gupta, Ritwika Das Ramadani, Lauresha Department of Statistics and Data Science CHRIST Deemed to be University Bangalore India Department of Computer Science AAB College Pristina Kosovo

ISBN: (纸本)9789819795147

Diabetes prediction is crucial for early intervention and personalized treatment. This study uses a multimodal strategy, including prediction algorithms, downsampling, feature engineering, exploratory data analysis (EDA), cross-validation, and classification techniques. EDA is used to understand diabetes-specific features, while downsampling ensures fair representation of instances with and without diabetes. Classification algorithms categorize people into appropriate diabetes risk groups using machine learning. Cross-validation evaluates predictive models in various data scenarios. The study emphasizes the value of specialized methods and domain-specific expertise in diabetes prediction, emphasizing the need for accurate risk assessment in healthcare decision-making and the potential for proactive interventions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data reduction

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Multi-Objective Genetic-Programming Hyper-Heuristic for Evolving Interpretable Flexible Job Shop Scheduling Rules 13

Multi-Objective Genetic-Programming Hyper-Heuristic for Evol...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Pang, Junwei Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand

ISBN: (纸本)9798350308365

The job shop scheduling problem is an important combinatorial optimisation problem in the real world. Genetic programming hyper-heuristic has been successfully applied to automatically evolve effective dispatching rules to make a schedule in real time without much domain knowledge. However, the interpretability of GP-evolved rules has been largely neglected, which could lead to the lack of reliability and trustworthiness of the evolved rules in practice. Current work related to interpretable genetic programming algorithms primarily uses the model size as the interpretability metric. This could not fully reflect the interpretability of evolved rules. To overcome the limitation, we employ structural complexity and dimension gap as more comprehensive interpretability measures. In addition, a new multi-objective genetic programming algorithm, which applies the a non-dominated sorting method to solve the objective selection bias issue, is proposed to optimise the makespan (scheduling objective), structural complexity and dimension gap simultaneously. A variety of experiments demonstrates the competitive performance of our proposed algorithm based on effectiveness, convergence and diversity. Furthermore, the semantics of evolved dispatching rules are analysed to show their better interpretability. © 2024 IEEE.

关键词： Genetic programming

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DeepKet- Quantum Space-Efficient Word Embedding Layer for Steganalysis 3

DeepKet- Quantum Space-Efficient Word Embedding Layer for St...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Roshan Ahmed, N. Shridevi, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Center for Advanced Data Science Chennai India

ISBN: (纸本)9798350372120

Text-based Statistical steganography is one of the most non-human detectable methods of embedding hidden messages in plain text format which is useful in concealing information. Steganalysis is its counter, the process of detecting if a text has any encrypted data in it. This paper applies quantum computing to create DeepKet Embedding, which optimizes the space requirements for word embeddings similar to Word2Vec. DeepKet is benchmarked against existing embedding layers and a significant size reduction is achieved while maintaining accuracy for steganalysis. © 2024 IEEE.

关键词： Embeddings

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Anatomization of Neural Networks based models for Semantic Analysis of Tabular dataset 9

Anatomization of Neural Networks based models for Semantic A...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bharath, B. Sai Bollineni, Jahnavi Mandala, Sandeep Preetham Ganguly, Tania Amudha, J. Shukla, Nikhil Joseph, Raj Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Intellectyx Data Science Pvt Ltd India

ISBN: (纸本)9798350394474

The Paper focuses on analysing neural network models that are used for semantically classifying tabular customer datasets. Additionally, we propose a custom neural network architecture to analyze tabular datasets and enable the model to extract and comprehend the underlying semantics within the data. The research focuses on three distinct neural network models: CharCNN, Bi-LSTM, and CNN+Bi-LSTM. Through comprehensive evaluation based on accuracy, and confidence scores of the models' performance, we determined that Bi-LSTM proved to be the best fit for this approach and dataset. The findings suggest that the custom CHAR-CNN model can be effective in classifying tabular data and can potentially be applied on various datasets considering both computational time and accuracy. This research contributes to the advancement in the field of semantic analysis for tabular dataset, opening avenues for further research on how to handle different kinds of Tabular datasets and enhancement of semantic models in NLP. © 2024 IEEE.

关键词： Neural network models

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An Efficient Partial Video Copy Detection for a Large-scale Video database 9

An Efficient Partial Video Copy Detection for a Large-scale ...

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9th International Conference on Big data Computing and Communications, BigCom 2023

作者： Luo, Zhan Zhang, Lan Lai, Jiewei Wang, Xinming Tang, Chen University of Science and Technology of China School of Data Science Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350331240

Partial video copy detection (PVCD) aims to discover copy segments of query videos from a video database, which plays an important role in video copyright protection, filtering, tracking, etc. For a large-scale video database, PVCD can be divided into two stages: the first stage involves searching for video-level copies of the query video in the database, and the second stage is to further localize the copy segments within the video-level copies. Thus, two major challenges arise: (1) efficiently and effectively calculating the similarity between videos;(2) localizing mixed-duration video pairs. To address the above challenges, we propose an efficient PVCD approach for a large-scale video database, based on the Bag-of-Words (BoW) framework, which decouples video-level similarity and copy localization into cell-level. This approach consists of two modules. The first is an efficient video similarity measurement (VSM) module for the large-scale video database. VSM aggregates cell-level similarity into video-level similarity, and with a dual index, it greatly improves retrieval speed while accurately measuring spatiotemporal transformations. The second is a greedy pattern detection (GPD) module for video copy localization. GPD quickly and accurately detects similarity patterns through a greedy strategy on the similarity matrix formed by matching frames in each cell, then aggregates them into complete predicted copy segments. On the comprehensive dataset self-SVD, VSM significantly outperforms state-of-the-art methods by 7.28% in mAP, and the retrieval speed is increased by over 318 times. Additionally, for short videos at the scale of hundreds of millions, the response speed can theoretically reach seconds. On the copy localization dataset MIX, composed of mixed-duration videos, GPD also achieves the best performance. © 2023 IEEE.

关键词： Aggregates

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RAM-CG: Relation-aware Adaptive Model for Continual Graph Learning 2

RAM-CG: Relation-aware Adaptive Model for Continual Graph Le...

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2nd Asia Conference on Advanced Robotics, Automation, and Control Engineering, ARACE 2023

作者： Shen, Qinghua Liu, Guiquan University of Science and Technology of China School of Data Science Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350328363

Neural networks excel at mining the static graph, but how to learn from streaming graphs without forgetting previous knowledge is an emerging challenge and well known as continual graph learning (CGL). Despite recent progress in this area, two significant challenges persist: 1) most of existing works only manipulate on the intermediate graph embedding and ignore intrinsic properties of graphs, which limits the learning method's pertinence. 2) recent attempts obscure the transferable knowledge and lack explicit description for the transfer process. It is non-trivial to tell what and how the information is transferred across graphs. In this paper, we point out that latent relations behind graph edges, as an intrinsic graph property, can be attributed as an invariant factor for the evolving graph sequence and lead a possible knowledge transfer. Motivated by this, we design a relation-aware adaptive model, dubbed as RAM-CG, which consists of a relation analysis module to explore latent relations behind edges for message-passing and a task-awareness masking classifier to account for the shifting knowledge in the graph sequence. Extensive experiments show that RAM-CG provides significant 2.2%, 4.2% and 6.6% accuracy improvements over the state-of-the-art results on CitationNet, OGBN-arxiv and TWITCH dataset. © 2023 IEEE.

关键词： Message passing

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Federated learning privacy incentives:Reverse auctions and negotiations

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1538-1557页

作者： Hongqin Lyu Yongxiong Zhang Chao Wang Shigong Long Shengnan Guo Guizhou Provincial Key Laboratory of Public Big Data Guizhou UniversityGuiyangChina College of Computer Science and Technology Guizhou UniversityGuiyangChina

The incentive mechanism of federated learning has been a hot topic,but little research has been done on the compensation of privacy *** this end,this study uses the Local SGD federal learning framework and gives a theoretical analysis under the use of differential privacy *** on the analysis,a multi‐attribute reverse auction model is proposed to be used for user selection as well as payment calculation for participation in federal *** model uses a mixture of economic and non‐economic attributes in making choices for users and is transformed into an optimisation equation to solve the user choice *** addition,a post‐auction negotiation model that uses the Rubinstein bargaining model as well as optimisation equations to describe the negotiation process and theoretically demonstrate the improvement of social welfare is *** the experimental part,the authors find that their algorithm improves both the model accuracy and the F1‐score values relative to the comparison algorithms to varying degrees.

关键词： artificial intelligence data privacy decision making internet of things

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