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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...

引用

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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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques 2

Review on Predicting the Landslide Susceptibility using Mach...

引用

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...

引用

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

Exploring Deep Learning Techniques for Sentiment Analysis on...

引用

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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Efficient net-based deep learning: A deep-fake image detection 3

Efficient net-based deep learning: A deep-fake image detecti...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Singh, Rohan Sharma, Dilip Kumar Kumar Jain, Praphula Gla University Department of Computer Science and Engineering Mathura India Gla University Department of Computer Engineering and Application Mathura India Mits Gwalior Department of Computer Science and Engineering MP India

ISBN: (纸本)9798331528201

With the substantial volume of social media data generated daily and the concurrent emergence of powerful multimedia modification techniques, detecting manipulations in photographs and videos has become an important task. This study examines the strength of a deep learning-based efficient net model for detecting manipulation in multimedia images. This study examines the publicly accessible benchmark dataset DFDC to prove its efficiency. We also highlighted the real-world challenges and emerging trends to combat deep-fakes. The research experiment resulted in and confirmed the efficiency of Efficient-Net as it performed well in both the training and testing stages, exhibiting more accuracy and reduced loss. The test accuracy varies between 50 and 95, with epochs spanning from 0 to 10. © 2024 IEEE.

关键词： Deep Learning Deepfakes Efficient Net Fake Image Video

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Crime Analysis Using Graph-Based Feature Selection 11th

Crime Analysis Using Graph-Based Feature Selection

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

作者： Das, Priyanka Dutta, Arindam Das, Bikash Kar, Madhuja Chakraborty, Sudipta Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India

ISBN: (纸本)9789819717231

The current study extracts all potential crime attributes from reported offenses and selects just those that are needed for crime pattern assessment. To accomplish this, before using a crime report, it is processed, and criminal features are gathered to build feature vectors for each report. Based on a cosine relationship score, a weighted and undirected graph evolved, with the distinguishing qualities as vertices and the inverse of the similarity rating as the weight of the accompanying edge. The threshold number is determined by averaging all edge weights. Two separate subgraphs have been formed based on the threshold. The resulting two subgraphs could be a collection of several components that were used individually as input to the next iteration of the clustering operation. Each component’s threshold has been changed independently, and they have been further partitioned into more compact subgraphs. The Silhouette Index, a cluster validation metric, is computed at each level of iteration, and the technique occurs over and over just in case the cluster quality improves. Eventually, the graph clustering method categorizes features. Thus, the primary improvement of this paper is feature selection, which not only improves the efficiency but also the veracity of the following mining process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crime

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Ontology Based Agriculture Data Mining using IWO and RNN 6

Ontology Based Agriculture Data Mining using IWO and RNN

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Saraswat, Deepak GLA University Department of Computer Engineering and Application Mathura India

ISBN: (纸本)9798350346961

An ontology is a machine-interpretable formal description of domain knowledge. In current years, ontologies have risen to prominence as a key tool for demonstrating domain knowledge and a key element of several knowledge management systems, decision-support systems (DSS) and other intelligent systems including in agriculture. However, a study of the current literature on agricultural ontologies suggests that the majority of research that suggest agricultural ontologies lack a clear assessment mechanism. This is unwanted because this is impossible to assess the value of ontologies in research and practise without well-structured assessment mechanisms. Furthermore, relying on such ontologies and sharing them on the Semantic Web or amongst semantic-aware apps is problematic. This paper presents a framework for selecting appropriate assessment techniques for Ontology Based Agriculture Data Mining utilizing Invasive Weed Optimization (IWO) and Re-current Neural Network (RNN) that appears to be absent from most recent agricultural ontology research. The framework facilitates the selection of relevant evaluation techniques for a particular ontology based on its intended user.. © 2023 IEEE.

关键词： Data mining

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Heart Disease Evaluation with Deep Learning and Machine Learning 6

Heart Disease Evaluation with Deep Learning and Machine Lear...

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Upadhyay, Sachin Singh, Sanjiv Kumar Goswami, Jayati Krishna Singh, Shiv Shanker GLA University Department of Computer Engineering and Application Mathura India

ISBN: (纸本)9798350346961

All over the world is affected by the disease which is named as heart disease. The main reason behind the heart disease is our busy life, as the person is affected not only by office work but also by personal problems. This mortality rate is too high. We can predict this disease with the help of Machine Learning (ML) and Deep Learning (DL) prediction models. In this paper, to reach the accuracy we worked on three ML & DL models. For this paper we use ML models named as: SVM (Support Vector Machine), LR (Logistic Regression) & Naïve Bayes. DL models named as: CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) & LSTM (Long Short Term Memory). The accuracy obtained in this study is made up of the 85% accuracy of Logistic Regression, the 89% accuracy of SVM, and the 85% accuracy of Naive Bayes. LSTM has an accuracy of 83%, RNN has an accuracy of 91%, and CNN has an accuracy of 83%. The study's findings indicate that the RNN model is the most accurate, coming in at 90%, and from this we can say that it is the best at predicting heart disease.. © 2023 IEEE.

关键词： Support vector machines

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Urban Growth Estimation of Moradabad City Using Sentinel-2 Images

Urban Growth Estimation of Moradabad City Using Sentinel-2 I...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Srivastava, Saurabh Ahmed, Tasneem Kumar, Rajeev Moradabad Institute of Technology Department of Computer Science & Engineering Moradabad India Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350379716

Accurate monitoring and assessment are necessary due to the enormous challenges posed by rapid urbanization to sustainable development and resource management. This study utilizes Sentinel-2 satellite images of Moradabad City, located in Uttar Pradesh, India, to analyze urban growth through a machine learning approach. Sentinel-2 imagery is widely used for land cover monitoring, and images from 2017 and 2023 were selected to assess the rapid expansion of urbanization. Various techniques, including SVM, ANN and minimum distance (MD), were employed for land surface classification. Among these, SVM produced the most reliable results for estimating urban areas, based on both the Kappa coefficient (Kc) and overall accuracy (O.A.). The SVM estimates that in 2017 about 8.308% of the region was classified as an urban area, and from 2023 that number had increased to about 11.64%, which could be a serious concern for Moradabad city's residents. The motivation behind this study to explore the potential applications of machine learning methods for small-scale urban dynamics monitoring and analysis through satellite image analysis. The research advances knowledge of urbanization processes and offers insightful advice for sustainable urban growth in quickly expanding places. © 2024 IEEE.

关键词： ANN MD Sentinel-2 SVM Urban Area Estimation Urban Growth estimation

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