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FRANN: Fuzzy Rough Set Based Artificial Neural Network for Prediction of Rainfall Status 5

FRANN: Fuzzy Rough Set Based Artificial Neural Network for P...

引用

5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Mallick, Bishnupriya Mantri, Jibendu Kumar Ray, Niranjan Kumar Mscb University Department of Computer Application India Deemed to be University School of Computer Engineering Bhubaneswar751024 India

ISBN: (纸本)9798331522100

Rainfall status prediction is a process of forecasting the future status or condition of rainfall for a particular region. Prediction of rainfall status is essential because heavy or low rainfall may have many adverse effects on agriculture, transportation, environment, living conditions and economic strength. So, accurate prediction of rainfall status is helpful in designing better action plans for water resource management, disaster risk reduction, sustainable development, emergency management and improving living conditions. Computational intelligence and approximate reasoning of conventional machine learning models are limited by incomplete or imperfect knowledge. However, the soft computing techniques have the capabilities to handle uncertainty, imprecision, partial truth and approximate reasoning to extract information in a number of ambiguous situations. In this aspect, a fuzzy rough set (FRST) based artificial neural network (ANN) model is proposed to predict the future rainfall status. Incorporating FRST method in ANN model can improve the prediction accuracy and reduce the overheads associated with both memory as well as computation time by selecting the dominant attributes and handling inconsistent data present in the information system. Rainfall status prediction accuracy of the proposed hybrid model is compared with two other hybrid models namely FRST based decision tree (DCST), FRST based support vector machine (SVM) and three other existing classification based prediction models namely DCST, SVM and ANN. The findings of the result suggest that the proposed model outperform its counterparts. © 2025 IEEE.

关键词： Risk management

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SVR: Feature Selection Based Support Vector Regression for Humidity Data Prediction 5

SVR: Feature Selection Based Support Vector Regression for H...

引用

5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

ISBN: (纸本)9798331522100

Weather data like humidity is a key indicator in meteorology which is influenced by multiple climatic factors. Higher humidity levels become a threat to human health, cause storms and also lead to increase in rainfall, temperature and air borne pollutants. Therefore, prediction of humidity can be helpful in taking necessary action plans for protection of both human life and environment. Weather data can be predicted by multiple regression methods using explanatory variables. However, the effectiveness and prediction accuracy of the multiple regression methods are affected by the irrelevant or non-informative features and the uncertainties lie with the predictions. Feature selection (FS) methods can improve the effectiveness of prediction models and also reduce the uncertainties of predictions by reducing the dimensionality of the dataset by selecting more explanatory variables. On this basis, we have proposed feature selection-based support vector regression (SVR). The proposed hybrid model incorporates Random Forest (RF) model-based FS method in SVR to predict the future humidity data. Performance of RF model-based FS method is compared with two other feature selection methods like correlation-based method and recursive feature elimination (RFE) method. The prediction accuracy of the SVR model is compared with two other multiple regression models: artificial neural network (ANN) regression and decision tree regression (DTR). © 2025 IEEE.

关键词： Multiple linear regression

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A Random Walk-Based Approach for Clustering of Food Items 2nd

A Random Walk-Based Approach for Clustering of Food Items

引用

2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Das, Priyanka Bhattacharya, Rupam Department of Computer Application Institute of Engineering and Management West Bengal Kolkata India

ISBN: (纸本)9789819735938

Most of the recipe sites have a distinct page structure comprising a title, list of ingredients, preparation instructions, comments, and thus it is very challenging to cluster similar recipes. The work presented here initially starts with the formation of a recipe network and finding ‘leading’ nodes from the network. To the best of our knowledge, there doesn’t exist any research that addresses the concept of finding ‘leading’ nodes from a recipe network and subsequently clustering similar recipes based on the ingredients. In order to fill this gap, we have proposed a method RWRC (Random Walk-based Recipe Clustering). To determine the ‘leading’ nodes, the proposed approach incorporates data from the breadth-first search tree. To create a breadth-first search (BFS) tree, the graph is explored in level-order, starting with each node as the root node. At each level of the traversal, we have computed the degree centrality, betweenness centrality and closeness centrality of each node. The ‘leading factor’ has been considered as the average of these centrality measures for all the nodes present in the network. A random walk-based graph clustering approach has been addressed that initiates from the root node of the original recipe network. If the ‘leading factor’ of the next visiting node in the random walk is equal to or greater than the average ‘leading factor’ of the whole network, the agent moves to its neighbours;otherwise, the stopping requirement is satisfied. Further, these nodes discovered by this random walk are organised into a cluster. When a walk comes to an end, the visited nodes from the initial graph are extracted, and the subsequent walk begins in the remaining graph. The random walk continues until there are no undiscovered node(s) left. These extracted nodes form the clusters of recipes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Trees (mathematics)

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A Crime Knowledge Discovery Scheme Based on Entity Recognition, Relation Extraction, and Development of Criminal Profiling Using Modus Operandi 11th

A Crime Knowledge Discovery Scheme Based on Entity Recognit...

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11th International Conference on Intelligent Computing and applications, ICRTC 2023

作者： Das, Priyanka Dhar, Sayan Banerjee, Shreedatri Basak, Shirsha Nandi, Sneha Saha, Sneha Department of Computer Application Institute of Engineering and Management West Bengal Kolkata India

ISBN: (纸本)9789819717231

In this paper, a criminal knowledge discovery scheme is proposed for analysing the crime situation in India. The approach pulls information regarding crime against women in Indian states and union territories from a sizable corpus of newspaper reports and then uses the retrieved corpus to identify named entities. India has experienced an increase in crime in recent years, and daily, several crime reports are published. However, it can be difficult to manually access the crime reports and extract relevant information that can aid in the analysis of crime trends by law enforcement. Even while there are many research on the analysis of crime patterns, relatively few of them include entity recognition, relation extraction, and features of modus operandi. This study focuses on phrase identification to discover the rules for determining modus operandi from police records. The effectiveness of the method is evaluated using precision and recall. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crime

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Brain Tumor Radiogenomic Classification using multilevel contrastive information fusion 6

Brain Tumor Radiogenomic Classification using multilevel con...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Asmita Mittal, Praveen Department of Computer Engineering Application GLA University Uttar Pradesh Mathura281406 India

Brain tumors present significant challenges in diagnosis and treatment due to their complex and diverse nature. Radiogenomic analysis, which combines genomic and radiological data, has become important for advancing brain tumor research, with deep learning methods, particularly fusion-based approaches, making notable progress. However, current approaches face challenges in capturing both sequential and spatial features, as well as selecting effective fusion techniques. The research introduces a multi-fusion framework that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers using contrastive learning to enhance feature representation. This approach processes medical images through a neural network alongside a transformer backbone, merging outputs at various stages to improve dark knowledge differentiation between classes. While CNNs extract spatial features, transformers focus on patch-specific information, collectively boosting the model's discriminative power. The method demonstrates a 2-3% performance improvement over state-of-the-art models, with significant advancements in glioblastoma diagnosis and treatment. An ablation study further confirms that each component positively contributes to the classification task. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Convolutional neural networks

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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques 2

Review on Predicting the Landslide Susceptibility using Mach...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sinha, Abhishek Giri, Shivam Kumar Yadav, Himanshu Prasad, Sanjeev Kumar Mishra, Priya School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generaliz ability challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI 2

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Tripathi, Bharat Lal Saini, Madan Jain, Manish Saxena, Apoorva Pal, Narendra Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (纸本)9798331530389

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids, learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion. © 2025 IEEE.

关键词： Data privacy

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Examining Driver Sleepiness Detection Based on Visual Actions Using Haar Cascading and CNN 3

Examining Driver Sleepiness Detection Based on Visual Action...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Rawat, Vandana Manral, Pratibha Gupta, Lakshya Joshi, Nidhi Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Department of Computer Application Dehradun India Graphic Era Hill University Department of Computer Application Dehradun India

ISBN: (纸本)9798331519582

Every hour, there are numerous road accidents all over the world. Some of these accidents are caused by drunk driving, lack of sleep, inattention behind the wheel, and a variety of other factors that can be dangerous for both passengers and other road users. The most frequent circumstance is sleeping deprivation, which can cause the driver to be reckless while operating a motor vehicle. Driver drowsiness detection systems are particularly effective in identifying drowsiness by measuring the size of the driver's eyes and the rate at which they blink using a camera and software that is connected to it. The driver fatigue detection system works utterly offline, leveraging the Haar Cascade and Perclos machine learning methodologies, and can warn the driver via alarm if he or she is feeling drowsy. © 2025 IEEE.

关键词： Time division multiple access

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Exploring Deep Learning Techniques for Sentiment Analysis on IMDb Movie Reviews Dataset

Exploring Deep Learning Techniques for Sentiment Analysis on...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Sharma, Madhav Saini, Hukum Chand Sikarwal, Pushpendra Kumar Jaipur India Department of Engineering and Technology Jaipur India Department of Computer Application Jaipur India

ISBN: (纸本)9798350378047

Deep learning methods have changed the way we deal with natural language processing (NLP), and now our models can generate Textual Data almost as well like humans, but it was recent advances that introduced these revolutionized mechanisms for understanding complex human languages. The task of sentiment analysis-identifying the sentiment in text like movie reviews- is a fundamental one for NLP. This paper focuses on utilizing deep learning techniques, more specifically recurrent neural networkswith Long Short-Term Memory (LSTM) cells for sentiment analysis in IMDb movie reviews dataset. The model proposed in this study uses word embeddings [1], LSTM layers which are integrated as the temporality and gives a way to understand temporal dependencies important for analyzing meaning representations of sequential text data for sentiment classification. We start by pre-processing the IMDb data - cleaning and tokenizing text-data, after which we convert words into dense vector representations using pre-trained word embeddings. These embeddings are then processed by the LSTM layers, which helps to understand the context and sequential information [2]. © 2024 IEEE.

关键词： Spatio-temporal data

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Multi-class Classification Approach for Smart Intrusion Attack Detection Using AI and ML Classifiers 8th

Multi-class Classification Approach for Smart Intrusion Atta...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Rathod, Dushyantsinh B. Patel, Yesha Jethava, Archana Gohel, Namrata Suthar, Dhruvi Prajapati, Ramesh Rathod, Ghanshyam Computer Engineering Department Ahmedabad Institute of Technology Gujarat Ahmedabad India Department of CSE Faculty of Engineering and Technology GLS University Gujarat Ahmedabad India Computer Engineering Department Shree Swaminarayan Institute of Technology Gujarat Gandhinagar India Department of Computer Application Parul Institute of Computer Application Baroda India

ISBN: (纸本)9789819766772

Due to increased network traffic and security concerns, intrusion detection system (IDS) research has received a lot of attention in computer science. Advanced intrusion detection systems have arbitrary incursion categories and demand a high processing power usage. Notwithstanding the fact that there is a wealth of literature on IDS issues, our objective is to provide a more thorough review. We offer the taxonomy to differentiate between modern IDSs based on the extensive survey and intricate organization. In addition, the architecture we provided in the content aids the reader in grasping the whole idea of IDSs with ease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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