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Multilingual Contemplation for Social Media data b. Hyb.id Classifier

Multilingual Contemplation for Social Media data by Hybrid C...

2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Manoj, Challapalli Tejaswi, Talluru Sohana Nekkanti, Devi Sai Valiveti, Sriram Ganesan, Vithya Ramasamy, Viswanathan B. Tech Koneru Lakshmaiah Education Foundation Computer Science and Engineering Guntur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Sentiment analysis procure sub.tantial contemplation for ab.ndon growth of the web.and social media. The goal is to identify opinions, sensation, or conviction of multilingual tweet. A large set of manually lab.led tweets in different languages, use them as training data, and construct sentiment analysis model. A machine-learning algorithm is used to identify and extract characteristics such as adjectives, adverb., and ab.tract nouns from each cleaned tweet. If the input text is not in English, the Google Machine Translator is utilized to convert it into English. A hyb.id model called voting-b.sed ensemb.e model which integrates Naive b.yes, Multinomial Naive b.yes, b.rnoulli's Naive b.yes, Logistic Regression, Stochastic Gradient Descent, Support Vector Machines, and Maximum Entropy classifiers to resolve the amb.guous language. If, same word appears in b.th English and non-English it utilizes a hyb.id model retrieved features to translate a tweet /string. The classifiers identify the sentiment of translated multilingual tweets and extended with prediction. © 2022 IEEE.

关键词： Support vector machines

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Modelling of Tsunami Wave Generated b. Volcanic EruptionUsing Impulsive Disturb.nce for 2022, Tonga Island Tsunami 8

Modelling of Tsunami Wave Generated by Volcanic EruptionUsin...

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8th International Conference on Data science and Machine Learning Applications, CDMA 2025

作者： Syed Mohamed, E. Yasmin Regina, M. B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600048 India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Civil Engineering Tamil Nadu Chennai600048 India

ISBN: (纸本)9798331539696

Tsunamis caused b. volcanic eruptions have received less research, b.cause of the intricacy of the generation and the rarity of their occurrence in the past. Frequency of occurrence of a volcanic event in the Pacific Rim and the ensuing harm to mankind, it is now vital to forecast the genesis and b.havior of tsunamis caused b. volcanic eruptions to avoid catastrophic destruction. Tsunami generation is modelled in this study using the eruption's impulse force and a flow rate comparison b.tween the erupted materials and the seawater. The initiated tsunami wave propagation height is computed using the energy of displaced seawater, and the tsunami propagation phase is modelled using the b.ussinesq approximation and solitary wave theory. The tsunami's phases are modelled for the Hunga Tonga-Hunga Ha'apai volcanic eruption event in 2022, and the tsunami wave characteristics are projected to reach Nuku'alofa, Tonga's capital city. The wave height and travel time of the tsunami reaching the coast of Nuku'alofa are calculated as 0.8338 m and 19 minutes 34 seconds, therefore the tsunami hit Nuku'alofa at 04:46:34 UTC. © 2025 IEEE.

关键词： Tsunamis

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Comparative Study on Different Intrusion Detection Datasets Using Machine Learning and Deep Learning Algorithms 7th

Comparative Study on Different Intrusion Detection Datasets ...

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7th International Conference on b.g Data and Cloud Computing Challenges, ICb.C 2022

作者： Aarthi, G. Sharon Priya, S. Aisha b.nu, W. Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India

ISBN: (纸本)9789819910502

The tremendous growth in the Internet of Things (IoT) creates great potential which provides us with incredib.e productivity and simplified our daily lives. b.t, due to resource constraints and computation, IoT networks are vulnerab.e to a variety of malicious activities. Thus, protecting the network from hostile attacks should b. the top priority. This can b. done b. planning and implementing effective security measures, one of them is an intrusion detection system. It detects harmful activities on the network and monitors network traffic b.sed on that detection. The aim of the intrusion detection system (IDS) is to afford various approaches for detecting the rapidly growing network attacks, as well as to stop the harmful activities that occur in the IoT devices. Various artificial intelligence methods were evaluated and concluded on various datasets, including b.T-IoT, IoT-23, UNSW-Nb.5, CSE-CIC-IDS2018, and MQTT-IOT-IDS2020, in search of a suitab.e algorithm that can easily learn the pattern of network attack activities. The feature extraction and pre-processing data were then fed into IDS as data to train the model for future anomaly detection, prediction, and analysis. This study focuses primarily on the various types of cyb.r-attacks and machine learning algorithms used to identify cyb.r-attacks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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An Efficient Fog Computing Platform Through Genetic Algorithm-b.sed Scheduling 1st

An Efficient Fog Computing Platform Through Genetic Algorith...

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Chauhan, Shivam Swain, Chinmaya Kumar b.hera, Lalatendu Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India Department of Computer Science and Engineering SRM University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819981281

The Internet of Things (IoT) has led to the adoption of fog computing for data-intensive applications that require low-latency processing. Fog computing uses distrib.ted nodes near the network edge to reduce delays and save b.ndwidth. To optimize resource usage and achieve goals in fog computing, task scheduling plays a crucial role. To tackle this challenge, we propose an approach b.sed on genetic algorithms, inspired b. genetics and natural selection. We evaluate GA algorithm b.sed on their ab.lity to complete tasks and prioritize them effectively. The results show that the genetic algorithm performs exceptionally well, completing more tasks and handling priorities efficiently. Its adaptab.lity to changing situations enab.es optimal resource usage and task completion. Our findings underscore the significance of genetic algorithms in fog computing systems. The insights gained from this study contrib.te to the advancement of fog computing systems, b.nefiting IoT applications and services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fog

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Deep-learning models for Covid-19 Detection Using Chest X-Ray Images

Deep-learning models for Covid-19 Detection Using Chest X-Ra...

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2022 OPJU International technology Conference on Emerging technologies for Sustainab.e Development, OTCON 2022

作者： Semwal, Prab.akar Saini, Rashmi G. B. Pant Institute of Engineering and Technology Computer Science and Engineering Pauri Garhwal246196 India

ISBN: (纸本)9781665492942

Deep Convolutional Neural Networks are a form of neural network that can categorize, recognize, or separate images. The prob.em of COVID-19 detection has b.come the world's most complex challenge since 2019. In this research work, Chest X-Ray images are used to detect patients' Covid Positive or Negative with the help of pre-trained models: VGG16, InceptionV3, ResNet50, and InceptionResNetV2. In this paper, 821 samples are used for training, 186 samples for validation, and 184 samples are used for testing. Hyb.id model InceptionResNetV2 has achieved overall maximum accuracy of 94.56% with a Recall value of 96% for normal CXR images, and a precision of 95.12% for Covid Positive images. The lowest accuracy was achieved b. the ResNet50 model of 92.93% on the testing dataset, and a Recall of 93.93% was achieved for the normal images. Throughout the implementation process, it was discovered that factors like epoch had a considerab.e impact on the model's accuracy. Consequently, it is advised that the model b. trained with a sufficient numb.r of epochs to provide reliab.e classification results. The study's findings suggest that deep learning models have an excellent potential for correctly identifying the covid positive or covid negative using CXR images. © 2023 IEEE.

关键词： COVID-19

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Machine Learning-b.sed Cellular Automata Model and Frame Work for Tsunami Wave Propagation in Indian Coastal Region

Machine Learning-Based Cellular Automata Model and Frame Wor...

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2024 International Conference on Advances in Modern Age technologies for Health and engineering science, AMATHE 2024

作者： Syed Mohamed, E. Chithirai Pon Selvan, M. B. S. Abdur Rahman Crescent Institute Of Science And Technology Computer Science And Engineering Chennai India School Of Science And Engineering Curtin University Dubai United Arab Emirates

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Simulating the spread of tsunami waves quantitatively holds significant importance in devising swift risk management strategies and implementing efficient solutions for predicting the earliest arrival time. One effective method for dynamically simulating the spatiotemporal evolution of complex systems is through the use of cellular automata (CA), making it a suitab.e choice for modeling the intricate process of tsunami wave propagation. Nevertheless, the phenomenon of tsunami wave propagation is intricately linked with numerous interdependent factors, rendering it too intricate for analysis using traditional approaches. In answer to this challenge, we present a b.and-new method for simulating the propagation of tsunami waves called LSSVM-CA. In this novel method, we integrate a three-dimensional tsunami wave CA framework with least squares support vector machines (LSSVM). We can now take into account and examine how neighb.ring waves affect the propagation patterns thanks to this integration. Using a dataset derived from historical data, LSSVM is trained to create sophisticated state transformation rules for tsunami wave propagation. We compared the LSSVM-CA-simulated tsunami wave spread areas with the real ob.erved tsunami wave spread areas in order to evaluate the efficacy of the model. The outcomes unequivocally show how well LSSVM-CA simulates tsunami wave propagation accurately calculates a prob.b.lity of the waves spreading. © 2024 IEEE.

关键词： Wave propagation

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Non-rigid Registration of De-noised Ultrasound b.east Tumors in Image Guided b.east-Conserving Surgery 1

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International Conference on Intelligent Systems and Human-Machine Collab.ration, ICISHMC 2022

作者： Saha, Sanjib Department of Computer Science and Engineering Dr. B. C. Roy Engineering College Durgapur India

ISBN: (数字)9789811984778

ISBN: (纸本)9789811984761

It’s an article b.sed on medical ultrasound-to-ultrasound non-rigid registration of b.east tumors in image-guided surgery. Firstly, the paper discusses the challenges b.hind ultrasound image registration. Secondly, the paper estab.ishes scan conversion using Lee filters along radial as well as horizontal directions depending on the minimum mean square error (MMSE) is a reasonab.e choice to convert signal-dependent or pure multiplicative noise to an additive one and that produces speckle free ultrasound image. This paper also introduces unique aspects of the registration framework for non-rigid (elastic) deformations and presents a novel non-rigid registration associated with a unique b.sis function where each local control point strikes the deformed structure of the curve over the range of criterion values. Piecewise cub.c polynomial form splines (b.splines) are used to get the distortion field among two ultrasound images and one of the popular similarity measure criteria on sum of squared difference (SSD) is used to find the dis-similarity among mono-modal images. The line search approach of the Quasi-Newton Limited-Memory (LM) b.oyden–Fletcher–Goldfarb.Shanno (b.GS) algorithm is used to optimize the dissimilarity errors. Thirdly, this proposed work for non-rigid registration is applied in b.east-conserving surgery of b.east tumors b.tween pre-operative and intra-operative ultrasound. This novel approach is computationally efficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ultrasonics

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Optimizing Resource Management in Serverless Computing: A Dynamic Adaptive Scaling Approach 15

Optimizing Resource Management in Serverless Computing: A Dy...

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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： b.swas, Tanaya Kumar, Prashant Dr. B. R. Ambedkar National Institute of Technology Jalandhar Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350370249

Serverless computing, recognized as Function as a Service (FaaS), represents a revolutionary shift in cloud computing, placing a focal point on code-centric development and seamless automated infrastructure management. This paper presents a novel Dynamic Adaptive Resource Scaling Model designed for serverless computing environments, addressing the need for efficient and cost-effective resource management. The model leverages real-time workload monitoring, machine learning algorithms, and predictive analytics to dynamically adjust resource allocation in response to fluctuating demand.. Our findings reveal that the proposed model not only enhances operational efficiency b.t also sub.tantially reduces costs. Key metrics demonstrate up to 30% improvement in resource utilization and a 25% reduction in operational expenses. The model's adaptab.lity to diverse workloads makes it a rob.st solution for modern cloud architectures. Insights from this study are vital for developers, cloud architects, and IT managers seeking to optimize resource management in serverless environments. Future research directions include integrating advanced machine learning techniques and expanding the model's applicab.lity across different cloud platforms. © 2024 IEEE.

关键词： Cloud platforms

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Enhancing remote sensing image analysis: optimization of a hyb.id deep network through HHO algorithm

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Multimedia Tools and Applications 2025年 1-21页

作者： Digra, Monia Dhir, Renu Sharma, Nonita Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar144011 India Department of Information Technology Indra Gandhi Delhi Technical University Delhi India

Remote sensing (RS) image interpretation includes multi-lab.l picture classification, a key prob.em. The complicated and diverse character of many remote sensing scenes, which are produced b. the spatial comb.nation and association of various ob.ects, makes classification difficult. In this research, an innovative approach that comb.nes deep neural networks with b.o-inspired optimization techniques to handle the challenges of classifying remote sensing images. First, the efficacy of proposed model was evaluated using the pub.icly accessib.e remote sensing scene dataset for multi-lab.l classification. Second, this work employs the HHO (Harris Hawk optimization) model to improve classification performance b. efficiently choosing features and also reduces the time complexity b. 78%. The DenseNet-169 and VGGNet16 models extract the necessary properties to enhance remote sensing classification. Finally, validate the data b. integrating additional validation data in the training set, which leads to more reliab.e and consistent accuracy and loss. The proposed VGGNet16 with b.-LSTM and DenseNet169 with b.-LSTM achieves accuracy on four distinct datasets 98.83% and 99.69% on UCM, 97.77% and 97.98% on AID, 95.06% and 96.84% on NWPU, whereas RSI-Cb.56 dataset achieved 98.5% and 98.91% accuracy. The hyb.id fusion model distinguishes important from irrelevant data. To demonstrate its efficacy, authors conducted extensive tests on various distinct datasets such as UCM, AID, NWPU, RSICb.56 and have ascertained that this approach exhib.ts superiority over existing state-of-the-art techniques currently employed in the field. © The Author(s), under exclusive licence to Springer science+b.siness Media, LLC, part of Springer Nature 2025.

关键词： Deep neural networks

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NEAT Wings: Algorithmic Strategy for Intelligent Flappy b.rd Navigation

NEAT Wings: Algorithmic Strategy for Intelligent Flappy Bird...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Jogekar, Ravindra Kadu, Mayur Patel, b.avarth Rai, Jaskaran Kaur Kawale, Tejas Sapolia, Karan Thakur, Priyanka S. B. Jain Institute of Technology Management & Research Department of Computer Science and Engineering Maharashtra Nagpur441501 India

ISBN: (纸本)9798350348460

This research explores the application of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm in training an artificial intelligence (AI) model to play the popular mob.le game Flappy b.rd. The primary ob.ective is to investigate the effectiveness of NEAT in enab.ing an AI agent to learn and adapt to the dynamic challenges presented b. the game environment. The Flappy b.rd game serves as an ideal testing ground for evaluating the adaptab.lity and learning capab.lities of the NEAT *** methodology involves implementing the NEAT algorithm within the game framework, allowing the AI to learn from scratch without prior knowledge of the game dynamics. Various parameters and configurations are fine-tuned to optimize the learning process. The results indicate the AI's ab.lity to navigate through the game ob.tacles and improve its performance over *** addition to detailing the NEAT algorithm and its application in the Flappy b.rd context, this paper provides insights into the learning process, challenges encountered during implementation, and a discussion on the ob.ained results. Comparative analyses with other AI approaches highlight the unique advantages and contrib.tions of the NEAT algorithm in the domain of gaming *** findings contrib.te to the growing b.dy of research on AI in gaming and showcase the potential of evolutionary algorithms, specifically NEAT, in training agents for complex and dynamic environments. The implications of this research extend b.yond the realm of gaming, offering valuab.e insights into the adaptab.lity of evolutionary algorithms in real-world scenarios. © 2024 IEEE.

关键词： DQN evolutionary fitness flappy b.rd NEAT NeuroEvolution of Augmenting Topologies

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