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2022 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2022

作者： Puri, Vikram Kataria, Aman Solanki, Vijender Kumar Rani, Sita Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam CSIR-CSIO Chandigarh India CMR Institute of Technology Dept. of Computer Science and Engineering TS Hyderabad India Guru Nanak Dev Engineering College Dept. of Computer Science and Engineering Punjab Ludhiana India

ISBN: (纸本)9781665487016

End-user Internet of Things (IoT) devices, including security cameras, smart appliances, home monitors, and thermostats, are becoming more prevalent in households. Additionally, the proliferation of devices facilitates the propagation of security concerns like DoS and spoofing. However, it is difficult for conventional rule-based security systems to recognize IoT assaults due to the development of heterogenous devices in the IoT ecosystem. Artificial Intelligence (AI) techniques can be a solution which enables the creation of an effective security model based on actual data from each device. In this work, IoT botnets are detected and classified using machine learning (ML) and deep learning (DL) based algorithms. Six ML models and three DL models are used to assess the system's performance. The best-performing model is also implemented as an API. © 2022 IEEE.

关键词： Internet of things

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An Analysis of Frequency Response in Integrated Power System Using Power Electronics Controller Tuned With Whale Optimization Technique 3

An Analysis of Frequency Response in Integrated Power System...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Ganguly, Somnath Choi, Joon-Ho Mudi, Joyti Kundu, S. Roy, Aloke Kumar Mukherjee, V. Chonnam National University Department of Electrical Engineering Gwangju Korea Republic of Bankura Unnayani Insstitute of Engineering West Bengal Bankura India Bankura Unnayani Institute of Engineering Department of Electrical Engineering West Bengal Bankura India Bharat Institute of Engineering and Technology Dept. of EEE Hyderabad India Bankura Unnayani Institute of Engineering Department of Computer Science and Engineering West Bengal Bankura India Indian Institute Technology Indian School of Mines Department of Electrical Engineering Jharkhand Dhanbad India

ISBN: (纸本)9798350391565

The performance of any isolated hybrid power system is characterized by the type of controller being used. The considered power generating units are the solar thermal power plant (STPP), wind turbine generator (WTG), and diesel engine generator (DEG), whose frequency stabilization has been done with the utilization of proportional-integral-derivative (PID), integral-tilt-derivative (I-TD), and tilt-integral-derivative (TID) controllers. Tuning controller gains using the whale optimization algorithm (WOA) and the quasi-oppositional based whale optimization algorithm (QOWOA) involves optimizing the parameters to achieve the best performance for a given hybrid system. A comparative frequency response of the system under the individual influence of PID, TID, and I-TD controllers has been shown with different types of load perturbations. The simulation result shows that QOWOA-TID outperforms the other controllers. The study addresses the critical need for optimizing control strategies in hybrid power systems to ensure stability, efficiency, and reliability, thereby contributing to the advancement of sustainable energy solutions. © 2024 IEEE.

关键词： Diesel engines

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Vehicle Re-Identification for Intelligent Transport System Using DOLGNet Attention Model 8

Vehicle Re-Identification for Intelligent Transport System U...

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8th International Conference on Image, Vision and Computing, ICIVC 2023

作者： Masum, S M Injamamul Haque Sheikh, Md. Naim Rahman, Mim Islam, Mohammad Shohidul Islam, Md Atiqul Siddika, Sultana Easmin Beihang University Electronic & Information Engineering Beijing China Green University of Bangladesh Computer Science & Engineering Dhaka Bangladesh American International University Bangladesh Computer Science & Engineering Dhaka Bangladesh Bangldesh Space Research and Remote Sensing Organization Atmospheric Research Dept. Dhaka Bangladesh Beihang University Mechanical Engineering Beijing China Beihang University Economics and Management Dhaka Bangladesh

ISBN: (纸本)9798350335231

Vehicle re-identification is an important task that involves identifying a specific vehicle from multiple images in computer vision. In recent years, deep learning-based approaches have shown promising results in this field. However, due to the significant variations in vehicle appearances caused by different viewpoints, occlusions, and lighting conditions, it remains a challenging problem. This paper proposed a Deep Orthogonal Local and Global Network (DOLGNet-Attention) for vehicle re-identification, which uses ResNet as the backbone and integrates self-attention and dilated convolution layers to effectively capture local and global features. Our approach combines local and global features by incorporating self-attention and dilated convolution layers into the network architecture. The self-attention mechanism focuses on relevant components within the local feature maps. At the same time, the dilated convolution layers capture information from multiple scales, which enhances the network's ability to handle variations in vehicle appearance. The results show that DOLGNet-Attention achieves top performance in terms of accuracy and robustness. Testing and training accuracy are done to determine the accuracy of the model. It determines image matches and non-match images compared to query images. Similarity values greater than 90% indicate that the image is similar to the query and the vehicle is re-identified. The DOLGNet-Attention model represents a significant advancement in vehicle re-identification, with the potential to improve the accuracy and efficiency of applications such as traffic management, surveillance, and security. © 2023 IEEE.

关键词： Vehicles

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Exploring Voice-Activated AI's Impact on Workplace Productivity with Virtual Assistants

Exploring Voice-Activated AI's Impact on Workplace Productiv...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Veeramalai Sankaradass S. Sudhakar Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Anna University Chennai Tamil Nadu India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

This paper presents a revolutionary computer desktop computer aide encouraged by GPT-3, a cutting-edge language version along with support discovering strategies. The chatbot functioning as a detailed device for computer individuals, effortlessly incorporates AI to give user-friendly support in jobs varying from standard procedures to progressed data administration and also setups changes. By using the capacities of GPT-3 the chatbot involves individuals in all-natural language discussions, supplying thorough reactions and also carrying out jobs with very little individual input. In addition, an assistance discovering based suggestion system adjusts to individual actions, continually refining ideas to maximize individual experience. This paper likewise represents a substantial innovation in individual computer showcasing the transformative possibility of AI in boosting performance coupled with individualizing the computer experience. With this cutting-edge technique we picture a future where AI-driven individual aides redefine the requirements of customer communication and also effectiveness in computer procedures.

关键词： Training Productivity Technological innovation Refining Employment Personal voice assistants Reinforcement learning Chatbots Reliability Standards

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review

Artificial Intelligence to Enhance the Security of Internet ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Dheekshaa V M Harsha V Sindhu M S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity.

关键词： Technological innovation Reviews Decision making Solids Real-time systems Internet of Things Security Artificial intelligence Protection Anomaly detection

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Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware

arXiv

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arXiv 2025年

作者： Walton, Brandon J. Khatun, Eshita Ghawaly, James M. Ali-Gombe, Aisha Dept. of Computer Science & Engineering Louisiana State University Baton Rouge United States

Malware analysis is a complex process of examining and evaluating malicious software’s functionality, origin, and potential impact. This arduous process typically involves dissecting the software to understand its components, infection vector, propagation mechanism, and payload. Over the years, deep reverse engineering of malware has become increasingly tedious, mainly due to modern malicious codebases’ fast evolution and sophistication. Essentially, analysts are tasked with identifying the elusive needle in the haystack within the complexities of zero-day malware, all while under tight time constraints. Thus, in this paper, we explore leveraging Large Language Models (LLMs) for semantic malware analysis to expedite the analysis of known and novel samples. Built on GPT-4o-mini model, MalParse is designed to augment malware analysis for Android through a hierarchical-tiered summarization chain and strategic prompt engineering. Additionally, MalParse performs malware categorization, distinguishing potential malware from benign applications, thereby saving time during the malware reverse engineering process. Despite not being fine-tuned for Android malware analysis, we demonstrate that through optimized and advanced prompt engineering MalParse can achieve up to 77% classification accuracy while providing highly robust summaries at functional, class, and package levels. In addition, leveraging the backward tracing of the summaries from package to function levels allowed us to pinpoint the precise code snippets responsible for malicious behavior. Copyright © 2025, The Authors. All rights reserved.

关键词： Android malware

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3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap

arXiv

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arXiv 2025年

作者： Yang, Minmin Ren, Huantao Velipasalar, Senem Electrical Engineering and Computer Science Dept. Syracuse University SyracuseNY United States

Existing zero-shot 3D point cloud segmentation methods often struggle with limited transferability from seen classes to unseen classes and from semantic to visual space. To alleviate this, we introduce 3D-PointZshotS, a geometry-aware zero-shot segmentation framework that enhances both feature generation and alignment using latent geometric prototypes (LGPs). Specifically, we integrate LGPs into a generator via a cross-attention mechanism, enriching semantic features with fine-grained geometric details. To further enhance stability and generalization, we introduce a self-consistency loss, which enforces feature robustness against point-wise perturbations. Additionally, we re-represent visual and semantic features in a shared space, bridging the semantic-visual gap and facilitating knowledge transfer to unseen classes. Experiments on three real-world datasets, namely ScanNet, SemanticKITTI, and S3DIS, demonstrate that our method achieves superior performance over four baselines in terms of harmonic mIoU. The code is available at Github. © 2025, CC BY.

关键词： Semantic Segmentation

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LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Puthucode Ganesh Vignesh Nithin Kumar S Senthil Kumar T Jeyakumar G Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments.

关键词： Privacy Accuracy Laser radar Three-dimensional displays Sensors Indoor environment Convolutional neural networks Fall detection Older adults Monitoring

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Balance Relation-Aware Attention Embedding Model for Knowledge Graph Completion

Balance Relation-Aware Attention Embedding Model for Knowled...

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IEEE Punecon

作者： Anish Khobragade Shashikant Ghumbre Vinod Pachghare Dept. of Computer Science and Engineering COEP Technological University Pune India Dept. of Computer Science and Engineering Government College of Engineering and Research Avasari Khurd Pune India

Predicting missing links has great significance in knowledge graph completion because it helps improve the accuracy and completeness of the information in the graph. The recent models employ an attention mechanism to capture the graph network. Previous attention-based models treat similar relation semantics for associated pairs of entities, but relations might have similar names but represent different semantics. Thus, models fail to capture the semantic dissimilarity of relations involved within the triplet entity pair. This paper proposes a new graph attention model capable of effectively learning embeddings by combining relationship features with triplet entity embeddings to balance relationship contributions while capturing meaningful graph structure. The model incorporates relational compositional embeddings to determine distinct attention for neighboring nodes during computation and update the entity embedding. This novel approach outperforms the previously established models on the benchmark dataset.

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Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models

Comparative Advances in Financial Sentiment Analysis:A Revie...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Manish Barath Mahendran Aswin Kumar Gokul Poornima Lakshmi S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). Natural language processing (NLP) has made significant strides in comprehending and categorizing sentiment in intricate financial texts, especially with the use of Large Language Models (LLMs). The application of various LLMs, such as Bidirectional Encoder Representations form Transformers(BERT) and Financial BERT (FinBERT), as well as distilled models, such as DistilBERT and DistilRoBERTa, on a variety of financial datasets, including Financial Phrase Bank and LexisNexis news articles, was the main focus of this review article. highlighting different approaches such as model fine-tuning, zero-shot and few-shot learning, and prompt engineering. The study focuses on practical model predictions through a case study of sentimental analysis of cryptocurrencies. While FinBERT, a financial variant of BERT, exhibits high accuracy and robustness, other LLMs exhibit varying degrees of success based on the dataset and domain requirements. The analysis concentrates on the difficulties, compromises, and potential paths for improving LLMs for financial sentiment analysis.

关键词： Sentiment analysis Technological innovation Reviews Large language models Decision making Bidirectional control Market research Encoding Risk management Indexing

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