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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Pratik Shrote Priya Dasarwar Snehlata Dongre Prashant Khobragade Intern Center of Excellence Data Science GH Raisoni College of Engineering Nagpur India CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Computer Science and Engineering GH Raisoni College of Engineering Nagpur India Dept. of Computer Science and Engineering H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The population of earthquake-prone areas is growing, as is the number of people killed and injured by earthquakes. The problem of the increasing number of populated regions that are vulnerable to earthquakes is being addressed by a shortage of experts in earthquake engineering. This implies the requirement for automated intelligence-monitored mitigation and warning systems. Predicting earthquakes is still a major problem for disaster management around the globe. The objective of this work is to build a machine learning (ML) approach that uses geographic coordinates (latitude, longitude) and elevation (height) as inputs to forecast earthquake magnitude. The estimated magnitude is used to classify the seismic risk level for a given site into high, low, or no risk categories using Richter's scale. The study offers a thorough analysis of the methodology, covering feature engineering, data collecting, preprocessing, and model construction. A variety of machine learning methods are investigated, and relevant measures are used to assess their effectiveness.

关键词： Seismic measurements Prevention and mitigation Earthquake engineering Disasters Earthquakes Machine learning Real-time systems Hazards Stakeholders Resilience

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Chaotic Crude Oil Time-Series Prediction Using Multi-Objective Artificial Vulture Optimization Algorithm-Based Bidirectional Gated Recurrent Unit 2

Chaotic Crude Oil Time-Series Prediction Using Multi-Objecti...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kala, S. Padma Sharath, S.M. Raghavendra, K. Gurumoorthy, Sasikumar Husseen, Ahmad Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of Cse Tamilnadu Chennai India Jnnce Department of Ece Navule Shivamogga India Department of Electronics and Communication Engineering Telangana Hyderabad India School of Engineering and Technology Dhanalakshmi Srinivasan University Computer Science and Engineering Tamil Nadu Tiruchirappalli India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

Crude oil is a resource of non-renewable liquid petroleum in existence by nature which is extracted from the earth and evolved into various products. However, accurately predicting crude oil prices is difficult due to non-stationarity and market noise. In this research, the Multi-Objective Artificial Vulture Optimization Algorithm-based Bidirectional Gated Recurrent Unit (MOAVOA-BGRU) to accurately predict the chaotic crude oil time-series price using Deep Learning (DL). Initially, the data is gathered from West Texas Intermediate (WTI) dataset and min-max normalization is performed to enhance the model performance. The technical indicators approach is used to extract the features and MOAVOA technique is established to select the appropriate features. At last, BGU is utilized to predict the crude oil price cost. The existing techniques like Variational Mode Decomposition-Recurrence Plot-BGU (VMD-RP-BGRU), Empirical Mode Decomposition-Autoregressive Integrated Moving Average-based Long Short-Term Memory (EEMD-ARIMA-LSTM), and Local Mean Decomposition-Stochastic and Deterministic-ARIMA-LSTM (LMD-SD-ARIMA-LSTM) are compared with proposed technique. The MOAVOA-BGRU achieves better Mean Average Error of 1.024, 1.0207, and 0.012 in terms of weekly, daily, and monthly spot prices compared to VMD-RP-BGRU, EEMD-ARIMA-LSTM, and LMD-SD-ARIMA-LSTM respectively. © 2024 IEEE.

关键词： Long short-term memory

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Energy-Efficient-Enabled Edge-AI-IoT Integrated Traffic Incident Analysis and Avoidance of Secondary Incidents

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IEEE Internet of Things Journal 2025年

作者： Chavhan, Suresh Deepika, Illa Sai Gupta, Deepak Rodrigues, Joel J P C Indian Institute of Science Education and Research Thiruvananthapuram School of Data Sciences India Indian Institute of Management Mumbai India Maharaja Agrasen Institute of Technology Dept. of Computer Science and Engineering Delhi India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Amazonas State University AM Manaus Brazil

Intelligent transportation systems (ITS) use information communication and technologies to provide road safety, traffic control, traffic congestion, accident avoidance, etc. Traffic accidents cause huge disruption of vehicle movements, road blockages, traffic jams, etc., known as secondary traffic incidents. These incidents in turn lead to huge CO2 emissions and fuel consumption, which directly impact the environment, reduce vehicle mileage, and unnecessary fuel waste. To reduce or avoid secondary traffic incidents, in this paper, we propose an Edge-AI-IoT integrated energy efficient system to detect, analyze, and predict the primary and secondary incidents. The proposed system uses the existing sensor technology like accelerometer, tilt, etc., to detect the accident and severity levels using Edge-AI-IoT, locally it analyzes and predicts the secondary incidents. The proposed system has been exhaustively simulated using real-time scenarios in OMENT++, Veins, and SUMO, and it was tested using the NVIDIA Jetson AGX Xavier edge device integrated with the ThingSpeak cloud platform. The proposed system is tested with performance parameters such as clearance time, accident detection, density of vehicles, speed of vehicles, CO2 emissions, and fuel consumption at different times of day. The simulation and real-time tested results show its real-time deployment. © 2025 IEEE.

关键词： Traffic congestion

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Integrating Simulation and Optimization: A Case Study in Pamplona for Self-Collection Delivery Points Network Design 35

Integrating Simulation and Optimization: A Case Study in Pam...

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35th European Modeling and Simulation Symposium, EMSS 2023

作者： Izco, Irene Serrano-Hernandez, Adrian Sawik, Bartosz Faulin, Javier GILT-OR Group Institute of Smart Cities Dept. Statitistics Computer Science and Mathematics Public University of Navarra Campus Arrosadia s/n Pamplona31006 Spain Department of Business Informatics and Engineering Management AGH University of Science and Technology Mickiewicza 30 Kraków30059 Poland

ISBN: (纸本)9788885741881

The disruptions experienced by the processes in the last mile delivery during the SARS-CoV-2 pandemic raised the dilemma of up-to-date last mile approaches for Urban Logistics (UL) issues. Self-Collection Delivery Systems (SCDS) have been proved to be an improvement for all the players of the SC, providing flexibility of time-windows and reducing overall mileage, delivery time and, consequently, gas emissions. Differing from previous works involving hybrid modeling for automated parcel lockers (APL) network design, this paper brings a System Dynamics Simulation Model (SDSM) to forecast online shopping demand in the Spanish city of Pamplona. A bi-criteria Facility Location Problem (FLP) is solved by means of an e-constraint method, where e is defined as the level of coverage of the total demand. The experiment run considers 90% of demand coverage, in order to obtain the most complex network possible. The simulation and demand forecast was carried out using Anylogic simulation software and the optimization procedure makes use of the Java-based CPLEX API solver. © 2023 The Authors.

关键词： Complex networks

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Fresnel zone plate outperforms the metalens at high NA

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Optics Express 2025年第11期33卷 23313-23321页

作者： Majumder, Apratim Doughty, John A. Hayward, Tina M. Smith, Henry I. Menon, Rajesh Dept. of Electrical & Computer Engineering University of Utah Salt Lake CityUT84112 United States Dept. of Electrical Engineering & Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States LumArray Inc. SomervilleMA02143 United States Oblate Optics Inc. San DiegoCA92130 United States

Rigorous simulations challenge recent claims that metalenses outperform conventional diffractive lenses, such as fresnel zone plates (FZPs), in focusing efficiency at high numerical apertures (NAs). Across various lens diameters, FZPs exhibit a pronounced asymmetry in the shadow effect, leading to significantly higher focusing efficiency when optimally oriented. Extending this analysis, we show that conventional blazed gratings also surpass meta-gratings in efficiency. Since any linear optical element can be decomposed into local gratings, these findings broadly underscore the superiority of blazed structures over binary metastructures. Experimental characterization of an FZP with a diameter = 3 mm, a focal length = 0.2 mm, operating at λ = 634 nm, confirms the dependence of efficiency on illumination direction. We attribute this sensitivity to the axial symmetry of the FZP geometry–a feature not present in the metalens. Our results emphasize the need for rigorous, direct comparisons between meta-optics and traditional diffractive optics to ensure accurate performance assessments. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Focusing

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

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Future Machine Learning and Data science (FMLDS), IEEE International Conference on

作者： Md. Habib Ehsanu Hoque Mrittika Mahbub Mohd Razali Bin Md Tomari Rezaul Bashar Md. Golam Rashed Dipankar Das Dept. of Information and Communication Engineering University of Rajshahi Bangladesh Dept. of Computer Science and Engineering Pundra University of Science and Technology Bangladesh Dept. of Electronic Engineering University Tun Hussein Onn Malaysia (UTHM) Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification.

关键词： Training Road accidents Virtual assistants Traffic control Streaming media Real-time systems Robustness Road safety Shearing Vehicles

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Web3 Based Funding Platform

Web3 Based Funding Platform

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： J Godwin Ponsam Harsh Shaw Shubham Patil Dept. of Networking and Communications SRM institute of Science and Technology Chennai India Dept. of Computer Science And Eng. SRM institute of Science and Technology Chennai India

The emergence of Web3 technologies has led to the development of decentralized funding platforms, which allow individuals and organizations to raise funds without the need for intermediaries such as banks and venture capitalists. The underlying technology powering these platforms is blockchain, known for its ability to offer a reliable and transparent method of recording transactions and overseeing financial operations. This research paper aims to explore the Web3 funding platform landscape, its benefits and challenges, and the potential impact it could have on traditional funding models. The paper also examines the key features and functionalities of these platforms, including smart contracts, tokenization, and decentralized governance. It also presents the design and development of a platform for investors to invest in startups using cryptocurrencies. The platform is designed to address the challenges faced by startups in accessing funding, while providing investors with new investment opportunities in the fast-growing world of cryptocurrencies. Through a review of relevant literature and case studies, this research paper provides insights into the current state of Web3 funding platforms, their adoption and growth trajectory, and the regulatory environment that surrounds them. Overall, the platform offers a new investment opportunity for investors, while also providing startups with access to much-needed funding. The platform has the potential to disrupt traditional funding models and provide a more efficient and transparent way for startups to access funding.

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Advanced Machine Learning Frameworks for Multimodal Image and Speech Signal Processing 3

Advanced Machine Learning Frameworks for Multimodal Image an...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ramesh Naidu, P. Shandilya, Saurabh Srividya, C.N. Gujar, Shantanu Sudhir Devi, Supriya Dankan Gowda, V. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560064 India Poornima College of Engineering Department of Advance Computing Rajasthan Jaipur India BGSIT Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of ECE B G Nagara571448 India Rochester Institute of Technology Golisano College of Computing and Information Sciences Department of CyberSecurity RochesterNY14623 United States Alliance University Department of Mathematics Karnataka Bangalore562106 India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354171

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework;the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework's efficiency in predicting the multimodal input and demonstrate superiority over the prior methods. © 2024 IEEE.

关键词： Deep learning

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An Analysis and Investigation of InfoStealers Attacks during COVID'19: A Case Study 2

An Analysis and Investigation of InfoStealers Attacks during...

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2nd International Conference on Secure Cyber Computing and Communications, ICSCCC 2021

作者： Sharma, Ravi Sharma, Nonita Mangla, Monika Dr. B. R. Ambedkar National Institute of Technology Dept. of Computer Science and Engineering Jalandhar India Lokmanya Tilak College of Engineering Dept. of Computer Science and Engineering Navi Mumbai India

ISBN: (纸本)9781665444156

A COVID-19 pandemic is a rare event that distorted the lives of many civilians worldwide stemming from what is commonly referred to as the new-normal regarding communal standards and the way we live and work. Apart from the devastating influence on humanity and business, the pandemic also spawned a set of unprecedented cyber-attacks. These cyber-attacks during such critical time have traumatized the society. The rise in cyber-attacks is triggered due to rising apprehensions due to this pandemic like never before. Resultantly, a significant increase in the quantity and variety of cyber-attacks is observed since emergence of Covid-19. Cybercriminals promptly leveraged this pandemic premise to rebrand general attack vectors. These attacks are typically info stealers and vary from attacks of minimal intricacy such as 404 Keylogger to the latest and more frequent attacks such as Lokibot. This manuscript analyses the COVID-19 pandemic from the perspective of cyber-attack and establishes that a huge surge has been recorded in the cyberattacks during this pandemic across the globe. In this manuscript, authors analyze the cyberattacks from the perspective of critical macro events to divulge the modus-operandi of cybercrimes. This analysis helps to employ the USA as a case study to determine how cyber-criminals gained access to vital information related to significant events. As a result, this also helps to create and project cyber-crime campaigns meticulously. © 2021 IEEE.

关键词： COVID-19

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Investigation of High Performance Conducting Polymer Parameters for Supercapacitor Electrode

Investigation of High Performance Conducting Polymer Paramet...

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2021 International Telecommunications Conference, ITC-Egypt 2021

作者： Hassan, A.M. Ebrahim, Sh. Elshaer, A.M. Institute of Graduate Studies and Research Dept. of Materials Science Alexandria Egypt Higher Institute of Engineering and Technology Dept. of Computer Engineering El-Boheira Egypt

ISBN: (纸本)9781665445733

Polypyrrole film was electrodeposited onto nickel alloy and stainless-steel substrates to be assessed as a supercapacitor electrode. Double layer supercapacitor electrodes were electrochemically characterized by electrochemical impedance spectroscopy, cyclic voltammetry, and charge/discharge. It was observed that the polypyrrole electrodeposited onto stainless steel and nickel alloy electrodes provided an outstanding specific capacitance of 1400 F/g and 1030 F/g and energy density of 583.33 Wh/kg and 429.16 Wh/kg, respectively. The electrode structure and morphological properties of polypyrrole films was studied by the scanning electron microscope. © 2021 IEEE.

关键词： Polypyrroles

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