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Enhanced Cybercrime Detection on Twitter Using Aho-Corasick Algorithm and Machine Learning Techniques

Informatica (Slovenia) 2024年第18期48卷 97-108页

作者： Rawat, Romil Raj, A. Samson Arun Chakrawarti, Rajesh Kumar Sankaran, Krishnan Sakthidasan Sarangi, Sanjaya Kumar Rawat, Hitesh Rawat, Anjali Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India MP Indore India Department od Electronics and Communication Engeneering Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Utkal University Bhubaneswar India Department of Business Management and Economics University of Extremadura Spain Department of Computer and Communication Technology University of Extremadura Spain

The proposed work objective is to adapt Online social networking (OSN) is a type of interactive computer-mediated technology that allows people to share information through virtual networks. The microblogging feature of Twitter makes cyberspace prominent (usually accessed via the dark web). The work used the datasets and considered the Scrape Twitter Data (Tweets) in Python using the SN-Scrape module and Twitter 4j API in JAVA to extract social data based on hashtags, which is used to select and access tweets for dataset design from a profile on the Twitter platform based on locations, keywords, and hashtags. The experiments contain two datasets. The first dataset has over 1700 tweets with a focus on location as a keypoint (hacking-for-fun data, cyber-violence data, and vulnerability injector data), whereas the second dataset only comprises 370 tweets with a focus on reposting of tweet status as a keypoint. The method used is focused on a new system model for analysing Twitter data and detecting terrorist attacks. The weights of susceptible keywords are found using a ternary search by the Aho-Corasick algorithm (ACA) for conducting signature and pattern matching. The result represents the ACA used to perform signature matching for assigning weights to extracted words of tweet. ML is used to evaluate Twitter data for classifying patterns and determining the behaviour to identify if a person is a terrorist. SVM (Support Vector Machine) proved to be a more accurate classifier for predicting terrorist attacks compared to other classifiers (KNN- K-Nearest Neighbour and NB-Naïve Bayes). The 1st dataset shows the KNN-Acc. -98.38% and SVM Accuracy as 98.85%, whereas the 2nd dataset shows the KNN-Acc. -91.68% and SVM Accuracy as 93.97%. The proposed work concludes that the generated weights are classified (cyber-violence, vulnerability injector, and hacking-for-fun) for further feature classification. Machine learning (ML) [KNN and SVM] is used to predict the occurrence and

关键词： Tweets

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Early Heart Failure Recognition for Infants using Machine Learning 5

Early Heart Failure Recognition for Infants using Machine Le...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Indumathi, N. Meera, J. Palani Upadhyay, Shrikant Pithamber, Khobragade Kumar, Manoj Gupta, Jaina Sumith Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sathyabama Instittue of Science and Technology Department of Computer Science and Engineering Chennai India Mlr Institue of Technology Department of Electronics and Communiction Engineering Hyderabad India Mlr Institute of Technology Department of Computer Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798331504403

Caring of newborn babies is one of major concern for the doctors and to take care of his/her life is very challenging and sometime critical. Cardiac attack is one the severe issue that require immediate medical support to the new born baby. Providing some shocks using some device is one the immediate treatment to save the life of babies. The computation and prediction of cardiovascular status is one of the challenging tasks. In this paper we try to analyze the cardia prior heart failure chances with the different levels using machine learning methodology. The target rate of cardiac arrest detected is 58.78% and level of no cardiac found is 46.67% is achieved and this prediction level which help to recognize the level of cardiac attack for new born child. Here, we also analyze the different parameters like false rate, false observation, stability rate etc., that will help for the proper analysis of the collected data with a stability rate of 1.67% and precision rate of 60.039%. This result may provide the healthcare professional to handle the different level of heart failure for new born child. © 2024 IEEE.

关键词： Machine learning

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Dynamic Inverse Reinforcement Learning for Feedback-driven Reward Estimation in Brain Machine Interface Tasks 46

Dynamic Inverse Reinforcement Learning for Feedback-driven R...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Tan, Jieyuan Wang, Yiwen Hong Kong Univesrsity of Science and Technology Department of Electronics and Computer Engineering Hong Kong Hong Kong Univesrsity of Science and Technology Department of Electronics and Computer Engineering Department of Chemical and Biological Engineering Hong Kong

ISBN: (纸本)9798350371499

Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performance of RL-based BMIs relies on the design of effective reward signals. Inverse reinforcement learning (IRL) offers an approach to infer subjects' own evaluation from the observed behavior. However, applying IRL to extract reward information in complex BMI tasks requires consideration of the dynamics of subjects' goal during the control process. This dynamic nature of subjects' evaluation requires the IRL method to be able to estimate a time varying reward function. Previous IRL methods applied in BMI systems only estimated a static reward function. Existing IRL algorithms for dynamic reward estimation employ optimization methods to approximate the reward map for each state at each time, which demands substantial amounts of data to achieve convergence. In this paper, we propose a dynamic IRL method to estimate the feedback-driven reward of subjects during BMI tasks. We utilize a state-observation model to continuously infer the reward value for each state, with sensory feedback serving as the external input to model the transition process of the reward. We evaluate our proposed method on a simulated BMI fetch task, which is a multistep task with a time varying reward function. Our method demonstrates improved reward estimation close to the ground truth value, and it significantly outperforms the existing dynamic IRL method when the map size exceeds 25(p © 2024 IEEE.

关键词： Reinforcement learning

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Enhancing link prediction in graph data augmentation through graphon mixup

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Neural Computing and Applications 2025年第8期37卷 6267-6282页

作者： Sultana, Tangina Hossain, Md. Delowar Morshed, Md. Golam Lee, Young-Koo Department of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships in graphs, leading to suboptimal predictions. To address this, we introduce a novel method called graphon mixup (GM), which leverages the power of graphons to enhance link prediction. The augmentation strategy involves generating a synthetic graph by combining the original graph with a graphon-based synthetic graph. This process, expressed as a weighted combination of adjacency matrices, strategically blends real and synthetic information, enriching the training dataset. GM formulates link prediction as a joint optimization problem, aligning the characteristics of the synthetic graph with the true underlying structure. The objective is to minimize cross-entropy loss between predicted and true edge probabilities. A detailed computational complexity analysis evaluates the time and space requirements, aiding in understanding the efficiency and scalability of GM across different datasets and network sizes. Empirical validation on benchmark datasets demonstrates GM’s effectiveness in consistently improving average precision across diverse network types. The proposed method enhances the generalization capabilities of link prediction models, providing a more robust framework capable of accurate predictions even in the presence of noise or unseen patterns. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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Query-Based Text Summarization: A Comparative Investigation and TextRank Implementation 5th

Query-Based Text Summarization: A Comparative Investigation ...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Unhale, Varad Udchan, Suyash Shah, Mohak Kadam, Bhakti D. Department of Electronics and Telecommunication Engineering SCTR’s Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819788606

Owing to the proliferation of internet and multimedia applications, there has been a huge increase in digital content in the form of text, images, audio and video worldwide. The various applications of text summarization has attracted the computer vision researchers to generate the optimal text summaries. Several methodologies have been reported in literature to generate text summaries. However, the key challenge is incorporating user’s preference as text summarization is a subjective task. User generated queries act as a guiding beacon in the summarization process. Instead of producing generic text summaries, query-based text summarization techniques offer user preferred responses. This paper begins by discussing the fundamental concepts and objectives of text summarization, emphasizing on the role of user queries in the summarization process. It proceeds to categorize existing query-based summarization techniques into distinct paradigms, including extractive, abstractive, and hybrid approaches, highlighting the advantages and limitations of each. This paper presents the evolution of query-based text summarization with developed techniques, available datasets, evaluation metrics, and performance comparison. After conducting a survey on query-based text summarization, a text summarization using the TextRank algorithm is implemented and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores are calculated for the generated summaries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

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Tsinghua Science and technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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Smart Farming System Using IoT for Efficient Crop Growth 3

Smart Farming System Using IoT for Efficient Crop Growth

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ganesh, Jetti Venkata Bisht, Saksham Reddy, Shashidhar Babu, A. Mohan SRM Institute of Science and Technology Ramapuram Chennai Department of Electronics and Computer Engineering India

ISBN: (纸本)9798350354171

The Shrewd Cultivating Framework utilizes a combination of sensors and IoT innovation to make a robotized checking and control framework for agrarian purposes. The utilization of sensors like ultrasonic, soil dampness, DHT11, and LDR, interfaces with a microcontroller, empowers real-time information collection on plant development, soil dampness substance, temperature, and light escalated. The system's integration with IoT encourages farther get to screen and oversee these parameters. In case of unfavorable conditions such as moo soil dampness, the framework triggers caution through a buzzer, guaranteeing incite consideration and activity to keep up ideal edit development conditions. This framework presents a arrangement to improve edit surrender by empowering productive and opportune intercession based on exact natural data. © 2024 IEEE.

关键词： Microcontrollers

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From Past to Present: A Comprehensive Review of Antenna technology in Modern Wireless Communication

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Journal of Engineering Science and technology Review 2024年第3期17卷 179-200页

作者： Mishra, Brijesh Singh, Aditya Kumar Satheesha, T.Y. Verma, Ramesh Kumar Singh, Vivek Department of Electronics and Communication Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology U.P. Greater Noida India School of Computer Science Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering IMS Engineering College Uttar Pradesh Ghaziabad India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Karnataka Bengaluru India

This research looks at microwave devices, specifically, patch antenna along with electromagnetic spectrum, shape, mechanism, analytical methods, simulation tools, and feeding procedures. Patch antennas are distinguished by their rectangular form, with a patch on one side and a ground plane on the other. Patch antennas work by exciting electromagnetic waves inside the patch, which are subsequently transmitted into the surrounding environment. The report also outlines numerous ways for evaluating the performance of microstrip patch antennas. The electromagnetic characteristics of the antenna are analyzed using the transmission line model, cavity model, and multiport network model (MNM). The integral equations that regulate the behavior of the antenna are solved using the method of moments (MoM) and the finite element method (FEM). The spectral domain technique (SDT) is used to analyze the antenna’s frequency response, while the finite difference time domain (FDTD) approach is used to analyze the antenna’s time-domain behavior. Overall, these methodologies give a thorough understanding of microstrip patch antenna performance and may be utilized to optimize their design. Furthermore, several patch antenna feeding methods, such as probe feed, microstrip line feeding, aperture coupling, proximity coupling, and CPW feed, are investigated. Attaching a microstrip line to the patch, which is subsequently linked to the RF source, is what microstrip line feeding entails. Aperture coupling entails making a hole in the ground plane that allows the RF source to feed the patch directly. Proximity coupling is accomplished by placing a probe near the patch, which creates an electromagnetic field on the patch. Patch antenna simulation software includes programmers such as HFSS, CST, and FEKO. These tools simulate the patch antenna’s performance, including its radiation pattern, gain, and input impedance. These simulations may be used to optimize the patch antenna design for specific a

关键词： Microstrip antennas

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A Comparative Study of Pure and Silver-Enhanced Graphene Antennas for Fat-Intrabody Communication 10

A Comparative Study of Pure and Silver-Enhanced Graphene Ant...

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10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024

作者： Subramaniam, Sree Aran Asan, Noor Badariah Aziz, Mohamad Zoinol Abidin Abd. Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Technology & Engineering Engineering Department Durian Tunggal Melaka Malaysia Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Technology & Engineering Technology Department Durian Tunggal Melaka Malaysia

ISBN: (纸本)9798350364163

This paper presents the design and comparative study of two implant antennas utilizing graphene material for fat-intrabody communication (fat-IBC) in the 5.8 GHz Industrial, Scientific, and Medical (ISM) band. Both antennas use pure graphene and silver-enhanced graphene as radiating elements, fabricated on a Taconic TLY-3 substrate. Copper-conducting elements were used as the substitute conductive material in CST Microwave Studio simulation for the optimized designs. These antennas were then measured in a fat-tissue-equivalent phantom model, and the S11reflection coefficients were recorded. These results show that the silver-enhanced graphene antenna has better performance in conductivity and resonance sharpness compared with the pure graphene antenna, which can be applied for the fat-IBC system. © 2024 IEEE.

关键词： Microwave antennas

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Overview of Canny Edge Detection and Hough Transform for Lane Detection

Overview of Canny Edge Detection and Hough Transform for Lan...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Shriwas, R.N. Bodkhe, Yash Mane, Anushka Kulkarni, Rahul SCTR's Pune Institute of Computer Technology Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798350373783

Lane detection and departure warning systems play a crucial role in ensuring driver safety and preventing accidents on roadways. In this paper, we propose a comprehensive approach for lane detection and departure using image processing techniques. Our methodology involves a series of steps, beginning with color thresholding to isolate relevant lane markings from the surrounding environment. Subsequently, a region of interest (ROI) is selected to focus the analysis on the relevant portion of the image, reducing computational complexity and improving accuracy. Canny edge detection is then employed to identify edges within the ROI, providing a more precise representation of lane boundaries. Finally, the Hough line transform is applied to detect and extract straight lines corresponding to lane markings from the edge-detected image. We conducted experiments on various realworld road scenarios to evaluate the performance of our proposed approach. The results demonstrate the effectiveness of our method in accurately detecting lane markings and providing timely departure warnings. Our approach exhibits robustness against variations in road surface textures, and lane markings of different shapes and sizes. Furthermore, the computational efficiency of our method makes it suitable for real-time implementation in onboard vehicle systems. Overall, our research contributes to the advancement of intelligent transportation systems by offering a reliable and efficient solution for lane detection and departure warning, ultimately enhancing road safety for drivers and passengers alike. © 2024 IEEE.

关键词： Hough transforms

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