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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Kavya, B.M. Mallikarjunaswamy, S. Sharmila, N. Shilpa, M. Komala, M. Shivaji, R. Balachandra, P. Sheela, S. Pavithra, G.S. Ks Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jagadambha college of Engineering & Technology Dept Electronics and Telecommunication Engineering India Sjb Institute of Technology Dept of Mathematics Bengaluru India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

In the dynamic landscape of energy systems, integrating renewable energy resources into smart grids presents substantial challenges, notably due to the inherent variability of wind and solar power. This study unveils an advanced power management system for smart grids that harnesses machine learning algorithms (ApmSGML) to optimize energy distribution and storage, ensuring stability, efficiency, and environmental sustainability. This approach is distinguished from conventional methods such as Fixed Threshold Control and Rule-Based Scheduling, which often struggle to adapt to the unpredictable nature of renewable energy sources. By leveraging predictive models, the proposed system anticipates energy production and consumption patterns, facilitating proactive adjustments to maintain supply and demand equilibrium. Additionally, A comparison of the ApmSGML's performance against traditional power management techniques like Deterministic Load Forecasting and Static Energy Storage Management, underscoring the adaptive and continuous learning advantages offered by machine learning through techniques like deep learning and reinforcement learning. Our findings highlight significant advancements in energy efficiency and cost reduction, demonstrating the transformative potential of integrating machine learning into the energy sector. © 2024 IEEE.

关键词： Internet of things (IoT) Machine learning power management Renewable energy smart grid

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A Novel Deep Learning Framework for Intrusion Detection: Integrating Dual-Staged Attention and Squirrel Search Optimization for Enhanced Accuracy 2

A Novel Deep Learning Framework for Intrusion Detection: Int...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Mahaveerakannan, R. Poonguzhali, P.K. Murali Dhar, M.S. Vidhya, R. Natteshan, N.V.S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Hindusthan College of Engineering and Technology Department of ECE Coimbatore India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore641032 India School of computing Kalasalingam academy of research and education TN Krishnan koil India

ISBN: (纸本)9798331505745

The rapid development of computer networks, such as the internet, has caused a sea change in the ways in which people communicate and share information. The advent of widespread computer networks like the internet has caused a sea change in traditional modes of communication and information sharing. On the other hand, this technical progress has also opened the door for malicious actors to take advantage of loopholes and steal sensitive information, disrupt operations, etc. Therefore, intrusion detection systems (IDSs) are essential for screening harmful traffic and warding off frequent attacks. These algorithms used to work by comparing current attacks to those of the past or by following a predetermined set of rules. But now that data and computing power are more accessible than ever before, machine learning seems to be the way to go. In this study, to present DSAM-CGLSTM, a dual-staged attention mechanism based on conversion-gated Long Short Term Memory network, to capture both short-term mutation and long-term dependence info in the study dataset. To enhance the network's capacity to retrieve the short-term mutation information, hyperbolic tangent functions are incorporated into the input-gate and forget-gate of LSTM. Additionally, the network incorporates a dual-staged attention mechanism, which encompasses both input attention and temporal attention. Consequently, a novel algorithm for optimising the parameters of DSAM-CGLSTM, SSA, has been suggested in this research, which is based on metaheuristics. to use a single Jupyter notebook in the Google Colaboratory environment to conduct the entire experiment. to imported and then implemented the necessary software packages, including seaborn, pandas, matplotlib, keras, besides Tensor Flow, and to used the CICIDS 2017 dataset. During the model's training and validation processes, accuracy and loss were taken into account as performance indicators. With a 98.85% accuracy score, the deep learning model performed quit

关键词： Hyperbolic functions

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A study of different compression algorithms for multispectral images

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Materials Today: Proceedings 2023年 80卷 2193-2197页

作者： Vura, Swetha Patil, Premjyoti Patil, Shantakumar B. Department of Electronics and Communication Engineering School of Engineering and Technology CMR University Bengaluru India Research Scholar VTU Belagavi India B-108 Garuda Block M R Sannidhi Apartment Arehalli Utttarahalli Hobli Bengaluru India Department of Computer Science and Engineering Sai Vidya Institute of Technology Bengaluru India

Remote Sensing satellites acquire information about an area by analyzing the transmitted and reflected radio waves. The Earth Imaging satellites capture high resolution images which occupy more storage space on-board and consume a lot of bandwidth for downlink transmission. Multispectral sensors represent information of the images in multiple bands which are typically less than 15. The multispectral image compression algorithms aim to reduce the size of the images while preserving their quality. This paper involves a study of various algorithms used for compression of multispectral imagery. © 2021

关键词： Satellites

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Bi-Static Inverse Synthetic Aperture Radar Imaging Based on 5G NR Signal 23

Bi-Static Inverse Synthetic Aperture Radar Imaging Based on ...

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23rd International Symposium on Electrical Apparatus and Technologies, SIELA 2024

作者： Lazarov, Andon Kostadinov, Todor Nikola Vaptzarov Naval Academy Information Technology Department Varna Bulgaria Burgas Free University Computer Science and Engineering Faculty Burgas Bulgaria

ISBN: (纸本)9798350382365

This study addresses Bi-static Inverse Synthetic Aperture Radar (BISAR) concept with a 5G NR waveform. BISAR scenario with uncooperative transmitter and specially designed receiver, and flying target as helicopter is discussed. The positions of the transmitter, receiver and flying target are defined by vector kinematic equations. Mathematical models of 5G NR waveform and BISAR signal reflected from a target with complex geometry are described. Two dimensional (2-D) image reconstruction is realized by applying correlation with an emitted waveform or Fourier transform on a range direction, and Fourier transform on an azimuth direction. Simulation experimental results are provided to illustrate the correctness of mathematical models and imaging algorithms. © 2024 IEEE.

关键词： Inverse synthetic aperture radar

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Early Detection of Breast Cancer Subtypes with Convolutional Neural Networks 1

Early Detection of Breast Cancer Subtypes with Convolutional...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kadam, Bhagyashree Swapnil Dewangan, Leelkanth Parida, Prasanta Kumar Chaterjee, Tanushree Nahak, Kirti Sadashiv, Ebhad Swagat Sinhgad Institute of Technology and Science Narhe Pune It Department India Swangi Dept of Computer Science & Engineering Wardha India Kiit University Dept of Computer Science & Engineering Odisha Bhubaneswar751024 India Raipur Institute of Technology Department of Biotechnology Raipur India Kalinga University Dept of Computer Science & Engineering Chhattisgarh Raipur India Trinity Academy of Engineering Department of Engineering Science Kondhwa Pune India

ISBN: (纸本)9798350330915

Breast cancer continues to be a major worldwide health issue, demanding accurate subtype categorization for personalized treatment approaches. In order to accurately identify subtypes, this work introduces a unique hybrid classifier that uses multi-modal data integration as well as cutting-edge machine learning algorithms. The model uses a Convolutional Neural Network (CNN) for image-based feature extraction alongside machine learning techniques for genomic and clinical features, combining mammographic, histopathological, genomic, and clinical data. The interpretative framework, which depends on an interpretivist philosophy, makes it less difficult to comprehend subtype discrimination on a more in-depth level. With an overall accuracy of 92.5% across HER2-positive, estrogen receptor-positive, and triple-negative subtypes, the results clearly demonstrate the hybrid classifier's better performance. The model's sensitivity and specificity ratings regularly outperformed those of existing techniques, demonstrating its aptitude for picking up on fine subtype distinctions. Particularly, the classifier demonstrated a low incidence of false positives, which is critical for reducing pointless interventions. Key genetic markers, physical traits, and hormone receptor status have been recognized as important discriminators employing feature importance analysis. These discoveries broaden the understanding of the biological foundations of each subtype. The study offers a useful tool for accurate breast cancer detection and treatment planning in clinical practice. The hybrid classifier's integrative methodology makes use of several data sources in order to provide a thorough understanding of breast cancer subtypes. This work lays the groundwork for future oncology and multi-modal data analysis studies, with potential implications in personalized therapy and other cancer types. There is still much more to discover about ethical issues, model generalization, and therapeutic use in th

关键词： Breast Cancer Subtype Classification Convolutional Neural Networks (CNNs) Interpretivism Philosophy Multi-modal Data Integration Personalized Treatment Strategies

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Fortaleza: The Emergence of a Network Hub

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arXiv 2023年第1期3卷 272-279页

作者： Bragion, Eric Akter, Habiba Kumar, Mohit Xu, Minxian Abdelmoniem, Ahmed M. Gill, Sukhpal Singh School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Information Technology National Institute of Technology Jalandhar India Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected networks around the world. Cloud services have a better acceptance nowadays, and it is foreseen that this industry will grow exponentially in the following years. With more distributed networks that need to support customers in different locations, the model of one-single server in big financial centres has become outdated and companies tend to look for alternatives that will meet their needs, and this seems to be the case with Fortaleza, in Brazil. With several submarine cables connections available, the city has stood out as a possible hub to different regions, and this is what this paper explores. Making use of real traffic data through looking glasses, we established a latency classification that ranges from exceptionally low to high and analysed 800 latencies from Roubaix, Fortaleza and Sao Paulo to Miami, Mexico City, Frankfurt, Paris, Milan, Prague, Sao Paulo, Santiago, Buenos Aires and Luanda. We found that non-developed countries have a big dependence on the United States to route Internet traffic. Despite this, Fortaleza proves to be an alternative for serving different regions with relatively low latencies. © 2023, CC0.

关键词： Cloud data centre Latency Network hubs Fortaleza Roubaix Networking

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SIRR: Semantically Infused Recipe Recommendation Model Using Ontology Focused Machine Intelligence

SIRR: Semantically Infused Recipe Recommendation Model Using...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2022

作者： Anand, Mrinal Deepak, Gerard Santhanavijayan, A. R.V College of Engineering Bangalore India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9789819946334

In the current era, recipe recommendation is of extreme significance as there is no expert intelligence system specifically for recommendation. In the era of Web 3.0, semantically driven frameworks are mostly absent. As a result, semantically infused recipe recommendation model is highly important. This study proposes a semantically infused intelligent approach to recommend recipes using Ontology Focused Machine Intelligence to classify the documents. The semantic similarity was computed by hybridizing Normalized Compression Distance (NCD) and KL divergence under a cultural algorithm. The model uses both Recurrent Neural Network (RNN) and XGBoost at different locations to sort the data and compute semantic similarity. The model yielded an FDR of 0.04, and an accuracy of 97.09%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ontology

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RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers 1

RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using ...

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1st Workshop on Bangla Language Processing, BLP 2023

作者： Seth, Pratinav Goel, Rashi Mathur, Komal Vemulapalli, Swetha Dept. of Data Science & Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Computer Science & Engineering Dept. of Information and Communication Technology

ISBN: (纸本)9798891760585

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts(Hasan et al., 2023a;Islam et al., 2021;Hasan et al., 2023b). Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world’s sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://***/ptnv-s/RSM-NLP-BLP-Task2. © 2023 Association for Computational Linguistics.

关键词： Sentiment analysis

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Frame-Level Efficient Video Anomaly Detection Based on Prototype Networks

Frame-Level Efficient Video Anomaly Detection Based on Proto...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Wang, Kexin Lu, Baoli Zhou, Yuxi Wang, Congcong He, Yu Zhang, Jianhua Tianjin University of Technology School of Computer Science and Engineering Tianjin China Chinese Academy of Sciences AnnLab Institute of Semiconductors Beijing100083 China Chinese Academy of Sciences Technology and Engineering Center for Space Utilization Beijing China

ISBN: (纸本)9798350372601

In this study, we present a novel approach to enhancing video anomaly detection by integrating an Adaptive Prototypical Network (APN) with an Enhanced Meta-Prototypical Network (EMPN) within a 3D-convolutional neural network (CNN) encoder-decoder architecture. Our method addresses the limitations of traditional anomaly detection methods by effectively capturing the intricate spatio-temporal dynamics inherent in video data. By dynamically updating representations of normal states, our model facilitates rapid adaptation to new and unseen scenes with minimal computational overhead. Furthermore, we introduce a post-processing step utilizing a median filter to refine anomaly scores, reducing noise and false positives. Extensive experiments conducted on various benchmark datasets demonstrate that our approach outperforms state-of-the-art methods on two datasets, showcasing its superior performance. © 2024 IEEE.

关键词： Median filters

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Enhancing Road Safety and User Experience through Intelligent Tollgate Systems and Vehicular Communication Networks 5

Enhancing Road Safety and User Experience through Intelligen...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Jebaraj, Bharath Singh Nasreen, M.Ayeesha Sushmitha, S. Azarudeen, A. Subha, T.D. Vivekrabinson, V. Kalasalingam Academy of Research and Educatuion Department of Computer Science and Engineeing Sriviliputhur India R.M.D. Engineering College Department of Electronics and Communication Engineering Thiruvallur India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India R.M.K. Engineering College Thiruvallur Department of Electronics and Communication Engineering Thiruvallur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350367171

The automotive industry's growth, fueled by technology and job opportunities, has heightened global automobile sales. Despite this progress, road safety remains a concern, with numerous lives lost in car accidents annually. To address this, Vehicle-to-Vehicle (V2V) communication emerges, enhancing road safety through real-time information exchange. Roadside Communication Units (RCUs) provide vital services, offering traffic updates, weather notifications, and immediate alerts about incidents. Intelligent tollgate systems in vehicles streamline transactions, and speed control systems ensure safer driving. Leveraging Zigbee technology enables seamless communication with toll plazas, improving traffic flow. This study aims to enhance road safety through intelligent tollgate systems, V2V communication, and innovative technologies, emphasizing their potential to optimize traffic flow and provide a more comfortable driving experience. Identified are future research directions and development opportunities in this dynamic field. © 2024 IEEE.

关键词： Zigbee

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