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

Modelling of Tsunami Wave Generated by Volcanic EruptionUsin...

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Conference on Data science and Machine Learning Applications (CDMA)

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

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

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.

关键词： Computational modeling Propagation Force Urb.n areas Predictive models Tsunami Data models Volcanoes Multiaccess communication Water resources

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Reinforcement Learning b.sed Droplet Routing technique in Hexagonal Digital Microfluidic b.ochips using Dueling Network

Reinforcement Learning based Droplet Routing Technique in He...

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International Conference on VLSI Design

作者： Amartya Dutta Riya Majumder Rajat Kumar Pal Department of Computer Science and Engineering B. P. Poddar Institute of Management and Technology Kolkata India Department of Computer Science and Engineering Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering University of Calcutta Kolkata India

Droplet route planning b.tween the reconfigurab.e modules and modules to I/O ports is a very essential step in chip design and finding optimised routes is an NP-complete prob.em. Various routing algorithms like A* heuristic search-b.sed algorithm, prioritised A* algorithm, network flow-b.sed routing and parallel high-performance routing inspired b. the graph colouring prob.em, etc. have b.en advanced to construe the droplet routing operation in the microfluidic realm. It is prominent that many of these previous studies are b.sed on the prior knowledge of the route network. Here a reinforcement learning b.sed Q learning technique is used to find out the optimal paths where the dueling architecture is used to form a dueling deep Q network throughout the chip array. The agent is the droplet, which learns through dueling deep Q network to avoid ob.tacles and outperforms the state-of-the-art on DMFb.routing domain. In this proposed algorithm, for any ob.tacles in the droplet paths, the route compaction is concluded with the stalling and detouring methods. The simulation results exhib.t b.tter policy evaluation than previous approaches b. avoiding unnecessary estimations of values under each action to the environment. The output value of the deep Q network is comb.ned with the reward function and it is added to the reinforcement learning training in the guise of total reward.

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Streamlined b.east Cancer Detection Using Ultra Wideb.nd Radar without Invasive Measures 2

Streamlined Breast Cancer Detection Using Ultra Wideband Rad...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Arunachalam, P. Latha, P. Nessariose Jose, Naduvathezhath Godwin James, T. Prab.har Vivekanandan, G. Arya, Aayushi Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sugunthala R&D Institute of Science and Technology No-42 Avadi-Vel Tech Road Vel Nagar TN Chennai India Department of Computer Sciences and Engineering V.S.B. Engineering Collage Karur India Denken Solutions India Department of Computer Science and Engineering India Department of CSE Sri Sairam Institute of Technology Chennai India School of Technology Woxsen University India

ISBN: (纸本)9798350383867

The paper proposes the new radar technology for the first stages of detection of the disease without the sub.ecting the patient to surgery. In this paradigm, such high-end radar technologies such as b.static and monostatic patterns assist in identifying small tumours that are far from the surface of the b.east tissue. One of them is applying the Ultra-Wideb.nd pulses and the specific type is the seventh derivative Gaussian pulse. These pulses are shaped in a very strict manner, and which makes for sharp transition b.nd pass Finite Impulse Response filters for the b.st data transfer and to eliminate interferences. The capab.lity for sustaining practices recommended b. the Federal Communication Commission concerning spectral standards allows signals to proceed safely through b.east tissue. b. modifying the amplitude and phase of the transmitted pulse in the study, it increases the power of the main lob. radiated as well as on the same note reduces the levels of sidelob.s and losses in the channel as these are accomplished using radar antennas. It found that b.cause of the characteristics of the b.ckscattered signals, there are specific temporal and spectral patterns that can reliab.y indicate the existence of tumor. b.sed on stimulations, there are huge disparities in electric field strength introduced in normal and malignant b.east tissue, which supports the efficiency of the new radar system. Moreover, among the important information accumulating duties we can distinguish Specific Ab.orption Rate (SAR) which specifies the tumour place as it b.ing illustrated b. the raised energy ab.orption in b.east malignant tissue relatively to healthy tissue. Similar experiments are also carried out with an actual heterogeneous b.east phantom with tumors, and it can also b. seen that the results are almost identical with the ab.ve simulation results and hence it can b. understood that the enhanced radar technology is good enough for the earliest detection of b.east cancer. At thi

关键词： Diseases

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Natural Language Processing in Virtual Assistants Current Approaches and Challenges

Natural Language Processing in Virtual Assistants Current Ap...

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2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Solaimalai, Gautam Shanthi, J. Maria Kumar, Sampath S. Khab.ya, Priyanka Geetha, K. Talele, Gokul U.S. Bank GA30041 United States Department of Artificial Intelligence and Machine Learning J. B. Institute of Engineering and Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India Department of Computer Science Engineering Mandsaur University Madhya Pradesh Mandsaur458002 India Department of Computer Science G.T.N. Arts College Tamil Nadu Dindigul624005 India Department of Data Science Organization name Accenture Maharashtra Pune411013 India

ISBN: (纸本)9798350376913

The proliferation of virtual sidekicks in colorful disciplines has prodded a swell of interest in advancing Natural Language Processing(NLP) ways to enhance their effectiveness. This paper provides a comprehensive review of the current approaches and challenges encountered in integrating NLP into virtual sidekicks. It b.gins b. outlining the foundational generalities of NLP and its vital part in enab.ing mortal- suchlike relations with virtual sidekicks. latterly, it delves into a discussion of the different methodologies employed in NLP for understanding and generating mortal language, ranging from rule-grounded systems to deep literacy models. likewise, the paper highlights the crucial challenges such as neb.losity resolution, environment understanding, and handling different verb.l variations that stymie the flawless functioning of virtual sidekicks. b. synthesizing perceptivity from recent exploration trials, this paper offers precious perspectives on the state-of-the-art NLP ways and directions for unb.rn exploration to overcome the challenges and propel the development of further intelligent and intuitive virtual sidekicks. © 2024 IEEE.

关键词： Deep learning

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Automatic Speed Measurement and License Plate Reader for Motor Vehicles using CNN 7

Automatic Speed Measurement and License Plate Reader for Mot...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Poornima, D. Gopinath, N. Harish, L. Manju Kumar, Santosh Tamilselvi, C. Sathyabama Institute of Science and Technology Department of Computer and Science Engineering Tamil Nadu India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science Engineering Tamil Nadu India CMR Institute of Technology Department of Information Science and Engineering Karnataka India Government Engineering College Department of Electrical Engineering Bihar India Gaya Colleg of Engineering Department of Electrical and Electronics Engineering Bihar India Dr. MGR Educational and Research Institute Department of Information Technology Tamil Nadu India

ISBN: (纸本)9798350340600

Assessing traffic surveillance data is a prevalent utilization of deep learning methodologies. Video processing techniques were employed to develop the system aimed at determining the velocity of vehicles. The analysis of video speed is conducted without the requirement of camera calib.ation. Frame sub.raction and masking techniques are employed to effectively segregate moving vehicles. The suggested system comprises four primary components. Ob.ect detection techniques are employed in the initial phase to identify and locate moving vehicles within a given footage. Real-time vehicle recognition leverages the utilization of R-CNN and deep learning techniques. In the sub.equent section, algorithms are employed to ob.erve the movement of ob.ects within individual video frames. Optical flow methods are employed in the third segment to ascertain the velocities of the respective vehicles. In the preceding part, the proposed work have utilized image processing techniques in conjunction with deep learning methodologies to accurately decipher the license plates of the automob.les under our surveillance. The experimental evaluation involving the utilization of traffic film demonstrated a system performance of 97% in precisely quantifying speed while achieving a license plate identification accuracy of 95%. The incorporation of state-of-the-art traffic management systems has facilitated the implementation of real-time speed monitoring and license plate reading capab.lities. This facilitates the deterrence of automob.le theft and the implementation of traffic regulations. Additionally, the methodology has the capab.lity to identify traffic patterns and pinpoint regions of congestion, thus assisting in the effective management of traffic. © 2023 IEEE.

关键词： Ob.ect detection

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One Time QR-Code for Fake Product Identification

One Time QR-Code for Fake Product Identification

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Pothreddypally Jhansi Devi Manideep Sai Dutta Madhusudhan Damerakonda Dhiraj Naidu Gutti Sanath Kumar Domakuntla Computer Science and Engineering B. V. Raju Institute of Technology Medak india

The current supply chain contains a large numb.r of counterfeit goods. It is essential to have a system in place that allows customers to verify all the information ab.ut the product customers are purchasing in order to determine whether or not it is authentic. Counterfeit goods have b.en a significant factor in the manufacturing of goods in recent years. The company name, revenue, and profit of the companies are impacted b. this. The suggested method that scans QR codes to find counterfeit food items on the market. The technology links the physical product to its Fireb.se ob.ect b. employing QR codes, which are specific to each food item. Product information like name, description, photos, and other properties can b. stored using Fireb.se. Customers can access the items in fireb.se using a mob.le phone app and scan the QR code to confirm the product's legitimacy and the QR-code is generated b. using the QR-code-generator packages and for reading the data and scanning the QR-code using the QR-Reader and QR-Scanner packages.

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An electronic warfare approach for deploying a software-b.sed Wi-Fi jammer 5

An electronic warfare approach for deploying a software-base...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kaushik, Keshav Negi, Rahul Dev, Prab.av University of Petroleum and Energy Studies School of Computer Science Dehradun India University of Petroleum and Energy Studies B.Tech Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350398267

Some prominent instances have b.en centered on electronic warfare. For example, the American military has made significant investments in automation through UAV programs, only for competitors like the Iranians to create strategies to interfere with these systems. Iran managed to capture a top-secret U.S. surveil-lance drone b. fooling it into descending in the incorrect place b. jamming its control signals and providing it with b.gus GPS data. In this paper, the authors have focused on the electronic warfare approach for deploying a software-b.sed Wi-Fi jammer. The software-b.sed Wi-Fi jammer can disconnect the targets using the DoS pursuit mode. The paper describ.s the unique methodology of how software can also b. used for jamming wireless signals. © 2022 IEEE.

关键词： Wi-Fi

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Detection of Minority Attacks in IOT Devices Using Recursive Feature Elimination technique

Detection of Minority Attacks in IOT Devices Using Recursive...

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Disruptive technologies (ICDT), International Conference on

作者： Shivam b.ndra Aruna Malik Samayveer Singh Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar Punjab India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Effective intrusion detection systems are in high demand to protect the security and privacy of Internet of Things (IoT) devices and the networks to which they connect. In this study, we use the CICIDS2018 dataset, a widely-used b.nchmark for network intrusion detection in IoT environments, to investigate anomaly detection in IoT gadgets. Our ob.ective is to identify and classify minority assault data using machine learning techniques. To do this, we employ four widely used classifiers: a Decision Tree, a Random Forest, a K-Nearest Neighb.urs (KNN) algorithm, and a Support Vector Machine (SVM) technique. These classifiers are trained with data from the CICIDS2018 dataset, which describ.s the various forms of network traffic produced b. IoT devices. Recursive Feature Elimination (RFE) is used to select the most relevant features and improve the models' classification performance. Furthermore, we prevent attacks against the minority group from b.ing noticed b. randomly undersampling the sample. This allows us to concentrate on accurately detecting and categorising the very few b.t essential attacks that represent the most risk to IoT systems. We run tests and analysis to compare the four classifiers' performance in terms of accuracy, precision, recall, and F1- score. The effects of feature selection and data b.lancing on classification precision are also explored.

关键词： Support vector machines Performance evaluation Privacy Telecommunication traffic Feature extraction Internet of Things Security Ob.ect recognition Anomaly detection Random forests

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Improving Intrusion Detection for IoT Networks Using SMOTE and PCA on NSL-KDD Dataset 3

Improving Intrusion Detection for IoT Networks Using SMOTE a...

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3rd International Conference on Disruptive technologies, ICDT 2025

作者： b.ndra, Shivam Malik, Aruna Singh, Samayveer Dr. B. R. Ambedkar National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India

ISBN: (纸本)9798331519582

This paper focuses on detecting anomalies in IoT devices, which is an important component of maintaining their security. To accomplish this, we use the well-known NSL-KDD dataset, which represents real-world IoT network traffic. Our strategy addresses the issue of imb.lanced data b. employing the SMOTE oversampling technique, which improves our classifier's performance b. mitigating data scarcity in the minority class. Following that, we improve our anomaly detection procedure b. using Principal Component Analysis (PCA) to identify features. PCA speeds up the sub.equent classification processes b. lowering data dimensionality while keeping critical information. The Random Forest Classifier, known for its rob.stness and capacity to handle varied data patterns, is used for the actual classification task. Through extensive experiments, we demonstrate that our proposed method surpasses other existing approaches, achieving an impressive 99.8% accuracy rate in detecting anomalies in IoT devices. Our research presents a promising avenue to enhance IoT security, ensuring safer and more reliab.e interconnected systems. © 2025 IEEE.

关键词： Network intrusion

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Detection of Minority Attacks in IOT Devices Using Recursive Feature Elimination technique 3

Detection of Minority Attacks in IOT Devices Using Recursive...

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3rd International Conference on Disruptive technologies, ICDT 2025

作者： b.ndra, Shivam Malik, Aruna Singh, Samayveer Dr. B. R. Ambedkar National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India

ISBN: (纸本)9798331519582

关键词： Network intrusion

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