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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Garugu, Suresh Kumar Madhuri, P. Shyamala Atchana, Venu Gopal Penmetsa, Gopala Krishnam Raju Bandaru, Venkata Naga Rani Sastry Eemani, V.R.J. Department of Computer Science and Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031770807

The Dynamic Emotion-Adaptive Attention Mechanism (DEAAM) offers a groundbreaking framework for analyzing emotions in real-time video streams, leveraging a state-of-the-art convolution neural network (CNN) for rapid and accurate emotion detection. By integrating an adaptive attention mechanism, DEAAM dynamically adjusts video sampling rates and focuses on areas of emotional intensity, optimizing computational resources for the most expressive moments. This system intricately combines facial landmark detection with saliency mapping to pinpoint critical expressive features, enhancing the depth and precision of the analysis. Furthermore, DEAAM incorporates temporal emotional coherence tracking, utilizing advanced recurrent neural networks to capture the evolution of emotional states, adding a rich contextual layer to the emotion recognition process. This comprehensive approach not only increases the accuracy of emotion analysis but also ensures efficiency by focusing on key emotional expressions. Ideal for applications in virtual communication, mental health assessment, and any domain requiring nuanced emotion recognition, DEAAM sets a new standard in empathetic technology, offering a sophisticated, contextually aware system for extracting real-time emotional insights from video feeds. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Recurrent neural networks

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Phish Check: Content-based Phishing Website Detection Web App 6

Phish Check: Content-based Phishing Website Detection Web Ap...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ghadiyaram, Anil Kumar Reddy, G. Srikanth Basith, Mohammed Abdul Reddy, Naini Rithvik Department of Computer Science and Engineering Vignana Bharathi Institute of Technology India

ISBN: (纸本)9798331522667

This research proposes an innovative method for categorizing phishing and legitimate websites based on content features extracted from their HTML structure. Leveraging data collection with the requests library and HTML parsing using the Beautiful Soup module, our study scrutinizes 26,584 websites sourced from "***" and "***." Employing seven diverse machine learning classifiers from the sci-kit-learn library and implementing k-fold cross-validation, this research study assesses the efficacy of different models in accurately distinguishing phishing websites. Evaluation metrics include confusion matrices, accuracy, precision, and recall scores. The proposed approach refrains from using URL-based attributes, focusing solely on the characteristics of HTML content. This study presents the results and comparisons of various classifiers, highlighting the effectiveness of our methodology. © 2025 IEEE.

关键词： Phishing

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Detection and Navigation of Unmanned Vehicles in Wooded Environments Using Light Detection and Ranging Sensors

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Sensors and Materials 2023年第11期35卷 3637-3654页

作者： Zhang, Zhiwei Jhang, Jyun-Yu Lin, Cheng-Jian School of Electronic Information Engineering Hebei University of Technology Tianjin300401 China Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan Department of Computer Science & Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan

With the advancement of automatic navigation, navigation control has become an indispensable core technology in the movement of unmanned vehicles. In particular, research on navigation control in outdoor wooded environments, which are more complex, less controlled, and more unpredictable than indoor environments, has received widespread attention. To realize the movement control and obstacle avoidance of unmanned vehicles in unknown environments, in this study, we use light detection and ranging (LiDAR) sensors to sense the surrounding environment. By plane meshing the point cloud reflected from LiDAR, we can instantly establish feasible regions. At the same time, using the artificial potential field algorithm, a stable obstacle avoidance and navigation path is planned for use in an unknown environment. In an actual woods navigation experiment to evaluate our proposed LiDAR detection method, we used an independently developed unmanned vehicle with Ackermann steering geometry. Experimental results indicate that the proposed method can effectively detect obstacles. The accuracy requirement is within 30 cm from the navigation target, and the experimental results show that the average navigation success rate of the proposed method is as high as 85%. The experimental results demonstrate that the system can stably and safely navigate in scenarios with different unknown environments. © MYU K.K.

关键词： Navigation

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A comparative analysis of using Deep Learning and Machine Learning technologies for intrusion detection for effective network traffic analysis 7

A comparative analysis of using Deep Learning and Machine Le...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Divya, S.B. Mary, S. Prince Department of Computer Science and Engineering Sathyabhama Institute of Science & Technology Chennai India Mangalam College of Engineering Department of Computer Applications India

ISBN: (纸本)9798350372816

The broad utilization of interconnectivity and interoperability of processing frameworks have turned into an irreplaceable need to improve our everyday activities. All the while, it opens a way to exploitable weaknesses that work out positively past human control capacity. The weaknesses consider network protection instruments fundamental to accepting correspondence trade. Secure correspondence requires safety efforts to battle the dangers and necessities headways to safety efforts that counter advancing security dangers. So, emerging technologies like Deep Learning/ Machine Learning (DL/ML) come in handy for integration and better prediction. So, in this paper, we depict various deep learning/ML strategies used in detecting the imbalance in network traffic. The document also includes research that has been proposed on the topic of intrusion detection in network traffic over the previous four years (2020-2024) by a variety of research specialists. The main achievements of the security measures are recognized in this review together with their quantitative and qualitative success indicators. Additionally, as a guide for upcoming discoveries, this research study examines the major discoveries and the rationale for the lessons discovered. © 2024 IEEE.

关键词： Network intrusion

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Illicit Events Evaluation on the Dark Web Using NSGA-2 Algorithms Based on Energy Consumption

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Informatica (Slovenia) 2024年第18期48卷 77-96页

作者： Rawat, Romil Rajavat, Anand Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India

The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant features. The objectives and hypotheses tested using NSGA-II and MOEA are to develop predictive models that best fit the problem of traffic analysis, analyse the set of features associated with the dataset, and apply NSGA techniques to reduce the data dimensionality. The work used the datasets and considered the Transnational Terrorist Hostage Event (TTHE) and Counter Trafficking Data Collaborative (CTDC) datasets, which relate to global human trafficking (HTr). The method used is focused on analysing the energy consumption (Econ) with the carbon footprint (CFP) of the proposed algorithm for different values of the main parameters on different hardware platforms, where the quality of each individual is computed based on ML algorithms. The result represents the "F-measure" metric obtained from support vector machines (SVM) and the number of features in order to determine the dimensionality, the execution time, the Econ, and the CFP during the feature engineering process. To estimate performance, the parameter values are computed using *** proposed work concludes that cybercrime employs and represents susceptible behaviour on the OSN Channels network. We identified the threat patterns by using NSGA-II and Econ analysis to establish connections between the variables in the dataset. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

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TruChain: A Blockchain-based Access Control to Improve the Security of Smart Water Grid Systems 17

TruChain: A Blockchain-based Access Control to Improve the S...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mondal, Susmita Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Water is a significant resource in day-to-day life, and it usually requires technological association for comprehensive management. Smart Water Grids (SWG) typically use cyberphysical systems (CPS) to monitor several parameters associated with water quality and distribution, leveraging the possibility of performing remote operations. For robust architecture, remote operations rely on application-specific sensors, communication networks, edge control, and cloud computation. Needless to say, the centralized control of edge and cloud infrastructure is prone to attacks and failures, such as unauthorized access or single-point-of-failure. We propose TruChain, a Blockchain-based access control mechanism to eliminate the probability of access takeover and aggregate quorum-based validation on sensory data for transparent provenance reliability. We also provide access revocation to strengthen security by restricting access to sensitive data stored in a privately distributed cloud. We believe our solution has empirical decentralization and a verifiable authorization method by maintaining a tamper-evident, auditable trail of interactive events, building trust without third-party intervention. © 2025 IEEE.

关键词： Cloud security

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Learning Non-cooperative Driving Practices and its Impact on Road Traffic Dynamics 17

Learning Non-cooperative Driving Practices and its Impact on...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Osho Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Cooperative driving that prioritizes global safety and efficiency over self-interest is crucial in ensuring smooth traffic flow and enhanced road safety. However, experiencing noncooperative driving is common under heterogeneous and chaotic on-road traffic conditions. While non-cooperativeness is often context-dependent and assessed during direct vehicle interactions, it can be inferred through longitudinal analysis of driving patterns from vehicular kinematics data. This behavior not only adversely affects the vehicle itself but also has a cascading impact on nearby vehicles, ultimately disrupting overall traffic flow. In this study, we quantify a vehicle's non-cooperativeness using kinematic data and further model its cascading effects on nearby cooperative vehicles. Experimental analysis was conducted using the SUMO traffic simulator and the TraCI interface to assess the impact of non-cooperativeness on overall traffic dynamics. This research is among the first to examine the cascading effects of a vehicle's non-cooperativeness on its cooperative nearby vehicles. © 2025 IEEE.

关键词： Motor transportation

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Enhancing DNA Sequence Analysis for Disease Diagnosis and Prediction Using Parallelised Hash-Based Approach 2

Enhancing DNA Sequence Analysis for Disease Diagnosis and Pr...

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

作者： Sreeram, Anusha Bhukya, Raju Department of Computer Science and Engineering National Institute of Technology Warangal India

ISBN: (纸本)9798331530389

DNA sequencing is a critical tool in genetics, helping to identify disease-causing mutations, predict disease risks, screen for genetic diseases, and monitor disease progression. By determining the order of nucleotides that make up DNA, DNA sequencing allows for the identification of genetic information in specific DNA fragments, entire genomes, or complex microbiomes. It has transformed disease diagnosis and treatment and has applications in biotechnology, forensic science, and evolutionary biology. Comparing DNA sequences helps researchers identify genetic disorders, cancers, and antibody repertoires, leading to tailored treatments for individual patients. However, the diagnosis of diseases using DNA requires an efficient algorithm due to the large number of nucleotides in DNA sequences. The hash-based approach, an optimal string pattern-matching algorithm, is used to identify relevant patterns in DNA sequences. This approach has shown significant improvement in speed compared to the single pattern matching approach for finding multiple string patterns and a parallelized approach for hash based using GPU is also proposed. © 2025 IEEE.

关键词： DNA sequences

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Optimized CNN Model to Develop a Decision-Making System for Spraying on Cotton Crop 3rd

Optimized CNN Model to Develop a Decision-Making System for ...

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3rd International Conference on Intelligent Systems and Applications, ICISA 2024

作者： Sudhir, Supriya Kumar, Kalyani S. Reddy, P. Santosh Department of Computer Science & Engineering BNM Institute of Technology Bangalore India

ISBN: (纸本)9789819798384

Dynamic crop morphology capture aids real-time precision in field spraying decisions. Ultrasonic radar and costly LiDAR can't maximize their high precision. They may slow down decision speed because the current spraying amount control needs to include more information about branches and leaves, which highly relies on the statistical features of regional morphology. Since most agricultural tools in China are medium in size and relatively inexpensive, it makes sense to create a unique identification system that combines deep learning with cheap monocular vision. A crop's growth cycle was a helpful gauge for direct spraying because of the knowledge gained. For this reason, let's examine cotton as an example: This study aims to develop a single-camera acquisition system and propose an algorithm based on the Convolutional Neural Network (CNN) to identify different growth stages of cotton plants. An optimal CNN model structure is chosen among nine possible method structures using an optimization procedure based on the matrix of confusion and recognition efficiency, and its dependability is established by repeatedly switching the training and test sets by the k-fold test concept. This CNN model has an accuracy of 94.27%, an F1-score of 96.39%, a recall of 95.76%, a precision of 95.31%, and a recognition speed of 72.46 ms per picture. Also, the convolution neural network, the model suggested in this study, performs better than those of VGG16 and Alex Net. Finally, spraying deposition tests were conducted over three distinct cotton growth stages to validate the accuracy of the developed identification method and the viability of the sprayed decision-making process based on CNN. The findings of the studies confirm that the ideal spray parameters were applied during distinct growth times correspondingly, leading to an increase of up to 63.24% in the optimum index over the operations that did not discriminate growth periods, yielding an overall increase of 43.29%. © The Author(

关键词： Cotton

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