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The importance of public support in the implementation of green transportation in the smart cities

Computational Intelligence 2024年第1期40卷 e12326-e12326页

作者： Mao, Jia sun, Qi Wang, Xi Muthu, balaAnand Krishnamoorthy, sujatha School of Transportation Jilin University Changchun China Institute of Economics Jilin Academy of Social Sciences Changchun China Department of Computer Science and Engineering V.R.S. College of Engineering & Technology Arasur India College of Science and Technology Wenzhou Kean University Wenzhou China

smart cities are increasingly evolving, initializing new strategies, and programs that have a significant influence on policy making and scheduling while coexisting with urban facilities. To recognize urban planning offers to a smarter city context, it is now necessary to understand the contribution of the smart city in overall urban planning and vice versa. Currently, transportation has been seen as a connection to all aspects of life across the world. This article presents the results of a survey testing into whether the public supports the concept of green transportation in the smart cities. The green urban mobility model has been proposed to investigate urban traffic information to characterize important features of smart mobility in the smart cities. The development of intelligent transportation systems using the proposed model that makes traffic easier in the city to transport safe and more comfortable. The experimental results suggest the required factors of green transportation and realistic behavior of smart mobility. © 2020 Wiley Periodicals LLC.

关键词： smart city

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A Novel Cell Density Prediction Design using Optimal Deep Learning with salp swarm Algorithm 7

A Novel Cell Density Prediction Design using Optimal Deep Le...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： balde, Govind Hanmantrao Ala Walid, Md. Abul srinivasan, P. Yella, venkata Rajesh soumya, M. Rastogi, Ravi N.T.V.S's G. T. Patil Arts Commerce and Science College Research and P.G. Department of Zoology M.S. Nandurbar425412 India Department of Computer Science and Engineering Khulna9203 Bangladesh Sona College of Technology Department of Ece Tamilnadu Salem India Koneru Lakshmaiah Education Foundation Department of Biotechnology Andhra Pradesh Vaddeswaram India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal India Nielit Electronics Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350397284

Cell density prediction can be defined as the process of predicting the number of cells in a given quantity of a culture or cell suspension. It is considered a common practice in cell biology since cell density had a significant impact on cell behavior and can be utilized for monitoring the health and growth of cell culture. Precise prediction of cell density was significant for a range of applications in cell biology., which includes bioprocessing, cell-based assays, and cell culture. Therefore, this article develops a novel Cell Density Prediction design using Optimal Deep Learning with salp swarm Algorithm (CDP-ODLssA) technique. The presented CDP-ODLssA technique predicts the cell densities accurately on the images of cell suspensions or cultures. To do so, the presented CDP-ODLssA technique employs Long short Term Memory-Autoencoder (LsTM-AE) model for prediction of cell densities. In addition, the hyperparameter tuning of the LsTM-AE model takes place by the use of salp swarm Algorithm (ssA). For experimental validation of the CDP-ODLssA technique, a wide range of simulations was taken place. The obtained values highlighted the superiority of the CDP-ODLssA technique compared to other approaches. © 2023 IEEE.

关键词： Long short-term memory

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Circumstantial Discussion on security and Privacy Protection using Cloud Computing Technology 2

Circumstantial Discussion on Security and Privacy Protection...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Lourens, Melanie Naureen, Ayesha Guha, shouvik Kumar Ahamad, shahanawaj Dharamvir Tripathi, vikas Durban University of Technology Department of Human Resources Management South Africa B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur502313 India The West Bengal National University of Juridical Sciences India University of Hail College of Computer Science and Engineering Hail City Saudi Arabia The Oxford College of Engineering Bommanhalli Dept of Mca Bangalore560068 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9781665437899

Cloud computing is becoming a demanding technology due to its flexibility, sensibility and remote accessibility. Apart from these applications of cloud computing, privacy and security are two terms that pose a circumstantial discussion. various authors have argued on this topic that cloud computing is more secure than other data sharing and storing methods. The conventional data storing system is a computer system or smartphone storage. The argument debate also states that cloud computing is vulnerable to enormous types of attacks which make it a more concerning technology. This current study has also tried to draw the circumstantial and controversial debate on the security and privacy system of cloud computing. Primary research has been conducted with 65 cloud computing experts to understand whether a cloud computing security technique is highly secure or not. An online survey has been conducted with them where they provided their opinions based on the security and privacy system of cloud computing. Findings showed that no particular technology is available which can provide maximum security. Although the respondents agreed that blockchain is a more secure cloud computing technology;however, the blockchain also has certain threats which need to be addressed. The study has found essential encryption systems that can be integrated to strengthen security;however, continuous improvement is required. © 2022 IEEE.

关键词： blockchain

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Translation Of Architectural sketch Images To building Images Using CycleGAN

Translation Of Architectural Sketch Images To Building Image...

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North Karnataka subsection Flagship International Conference (NKCon), IEEE

作者： I b Divya Kavitha sooda Computer Science Engineering Department B.M.S. College of Engineering Bengaluru India Computer Science sEngineering Department B.M.S. College of Engineering Bengaluru India

sketching and drawing is the hallmark of an architect's toolkit. Architect needs to somehow show his idea from his sketch and also designing a sketch image from a building image will help the architects to record the different designs. Hence, using image-to-image translation strategies, it is possible to convert architectural vector designs into actual building images and vice versa. Image-to-image translation is becoming more popular as a result of advancements in information technology and computer vision. In a class of vision and graphic problems known as “image-to-image translation”, the objective is to discover the relationship between an output image and an input image. The main objective of the model in this study is to convert architectural sketches into building images and vice versa using Image to image translation on unpaired image datasets to help reduce the communication gap between architects and their clients. A suitable GAN model called Cycle GAN has been used to train on collected unpaired image dataset. Utilizing the trained model, a web application is created that is open for everyone. A Python library called flask is used to create the web application's front end in order to provide a straight-forward and user-friendly interface.

关键词： Graphics computer vision Computational modeling buildings Generative adversarial networks Libraries Information technology

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A novel approach toward the bio-inspired synthesis of CuO nanoparticles for phenol degradation and antimicrobial applications

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biomass Conversion and biorefinery 2024年第15期14卷 17235-17250页

作者： Dabhane, Harshal Ghotekar, suresh Zate, Manohar Lin, Kun-Yi Andrew Rahdar, Abbas Ravindran, balasubramani bahiram, Dhanraj Ingale, Chetan Khairnar, bhushan sali, Deepali Kute, sagar Jadhav, Ghanshyam Medhane, vijay Department of Chemistry G.M.D. Arts B.W. Commerce and Science College Savitribai Phule Pune University Maharashtra Sinnar422 103 India Silvassa396 230 India Department of Physics S.V.K.T. Arts Commerce and Science College Savitribai Phule Pune University Maharashtra Nashik422401 India Department of Environmental Engineering & Innovation and Development Center of Sustainable Agriculture National Chung Hsing University 250 Kuo-Kuang Road Taichung Taiwan Department of Physics Faculty of Science University of Zabol P.O. Box. 35856-98613 Zabol Iran Department of Environmental Energy and Engineering Kyonggi University Gyeonggi-do Suwon-si16227 Korea Republic of Department of Medical Biotechnology and Integrative Physiology Institute of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamil Nadu Chennai602 105 India Department of Chemistry R.B. Narayanrao Borawake College Savitribai Phule Pune University Maharashtra Shrirampur413709 India Department of Microbiology S.V.K.T. Arts Commerce and Science College Savitribai Phule Pune University Maharashtra Nashik422401 India Department of Chemistry Loknete Vyankatrao Hiray Arts Science and Commerce College Nashik India Department of Physics G.M.D. Arts B.W. Commerce and Science College Savitribai Phule Pune University Maharashtra Sinnar422 103 India Department of Chemistry K.R.T. Arts B.H. Commerce and A.M. Science College Savitribai Phule Pune University Maharashtra Nashik422002 India Department of Chemistry Karmaveer Abasaheb alias N.M. Sonawane Arts Commerce & Science College Savitribai Phule Pune University Maharashtra Satana423 301 India

Nanotechnology has been a widespread field for the last decade, but the clean and sustainable development of nanomaterials is a stimulating task for researchers in the current situation. The green synthesis of nanomaterial using plant extract is the most practiced chore of researchers, but the use of inorganic and organic bases makes them valueless. Herein, we first report the biogenic synthesis of copper oxide nanoparticles (CuO NPs) using human urine as a reducing agent. The XRD analysis confirmed the crystalline nature and synthesis of CuO NPs, where the grain size was found to be 6.78 nm. The absorption-desorption study of human urine–mediated synthesized CuO NPs was done with the bET technique, and the pore volume and surface area were found to be 0.06 cm3/g and 92.64 nm, respectively. Finally, the spherical morphology was confirmed by sEM analysis, and elemental analysis confirmed the formation of CuO NPs using human urine. The well-characterized CuO NPs were used for the eco-friendly degradation of phenol in aqueous media under sunlight, and the removal efficiency was recorded using a spectrophotometer. The antibacterial activity of synthesized CuO NPs was tested against gram-positive and gram-negative pathogenic bacteria. Also, the antifungal activity was verified against different fungal strains. © The Author(s), under exclusive licence to springer-verlag GmbH Germany, part of springer Nature 2023.

关键词： Photocatalytic activity

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LeNet with bidirectional Long short-Term Memory for signal Detection in Underwater Optical Communication

LeNet with Bidirectional Long Short-Term Memory for Signal D...

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software, systems and Information Technology (ssITCON), International Conference on

作者： s. subhashini Mohammed Al-Farouni b. satish Kumar D. sharmiladevi D. R Anita sofia Liz Department of Computer Science and Engineering B S Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Underwater optical signal detection performance suffers from occlusion and turbidity in degraded environments. Due to complex underwater environment the high directionality of light beam and vibration transceiver incident optical power usually spans a very wide dynamic range. This paper, proposed LeNet with bidirectional Long short-Term Memory (biLsTM) technique performed efficiently in the signal detection in under water optical communication by effectively extracting spatial features and capturing temporal dependencies. The data collected for this research includes data bits of 72 vectors of transmitted signals through the Gulf seawater. The LeNet approach process the received optical communication signals and extract spatial features from convolutional layer, converting them into relevant signal to enhance the communication of underwater. The combination of LeNet feature extraction capabilities and bi-LsTM sequential learning the signal then enhances the model ability leading to achieve better performance in signal detection. The performance of proposed method is evaluated with various metrics such as accuracy, precision, recall, f1-score and bit Error Rate (bER) metrics based on sea water.

关键词： Measurement vibrations Accuracy Convolution bit error rate Turbidity Feature extraction Optical fiber communication vectors signal detection

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Performance Analysis of CPU Utilization in Cloud Environments using Machine Learning Models

Performance Analysis of CPU Utilization in Cloud Environment...

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Distributed systems, computer Networks and Cybersecurity (ICDsCNC), International Conference on

作者： Archana Naik Kavitha sooda Karunakara Rai b Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India Department of Electronics and Communications Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cloud environments offer extensive services, making them a preferred choice for businesses and startups to host and maintain databases. Efficient resource management, including automatic scaling, is crucial for sustaining virtual machine environments. This paper addresses the challenge of forecasting CPU workloads to optimize resource allocation. Terraform was utilized to create and launch instances in the cloud environment, specifically using Amazon Web services (AWs). A CPU-intensive application was executed on the server, and CPU utilization data was collected at regular intervals to serve as the dataset for the experiment. Three models—Gated Recurrent Unit, Long short-Term Memory Autoencoder, and seasonal Auto-Regressive Integrated Moving Average—were compared for short-term workload prediction. The results demonstrate that sARIMA outperforms the LsTM autoencoder by 2.91%, GRU by 1.78%, and LsTM autoencoder by 1.13% based on MAPE, MsE, MAE, and RMsE metrics.

关键词： Measurement Cloud computing Web services Autoencoders Predictive models virtual machines Performance analysis Resource management servers Long short term memory

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Enhancing IoT-based smart Farming security with a Hybrid GRU Intrusion Detection system

Enhancing IoT-Based Smart Farming Security with a Hybrid GRU...

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Interdisciplinary Approaches in Technology and Management for social Innovation (IATMsI), IEEE Conference on

作者： sivakumar Nagarajan s. vijay s. Karthi Rvs Praveen Ramesh vijaya babu K burri Naresh Technical Architect I & I Software Inc Bethlehem USA Department of Data Science Sacred Heart College (Autonomous) Tamilnadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamilnadu India Director Digital Engineering & Assurance LTIMindtree Limited Hyderabad Telangana India SRKR Engineering College Bhimavaram Andhra Pradesh India Department of CSE - Cyber Security CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The growing use of IoT devices in smart agriculture makes the industry vulnerable to several cyber risks. This study introduces an innovative hybrid Intrusion Detection system (IDs) utilising Gated Recurrent Units (GRUs) to improve the security of IoT-enabled smart agriculture. compared to current LsTM and CNN-based intrusion detection systems, the proposed model effectively incorporates sequential data dependencies, hence minimising false positives and enhancing detection accuracy. Experimental findings indicate that the suggested model surpasses conventional techniques, with an accuracy of 97.5%, an F1 score of 0.96, and a recall of 0.97. These developments create a strong, efficient, and scalable solution for safeguarding IoT-based agricultural systems from cyber-attacks.

关键词： smart agriculture Technological innovation Accuracy Computational modeling scalability Intrusion detection Internet of Things Object recognition Protection Long short term memory

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streamlined breast Cancer Detection Using Ultra Wideband Radar Without Invasive Measures

Streamlined Breast Cancer Detection Using Ultra Wideband Rad...

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International Conference on Advances in Information Technology (ICAIT)

作者： P Arunachalam Latha P Naduvathezhath Nessariose Jose T. Prabahar Godwin James vivekanandan G Aayushi Arya Department of Biomedical Engineering VelTech Rangarajan Dr. SugunthalaR&D Institute of Science and Technology Chennai TN India Department of Computer sciences and engineering V.S.B. Engineering Collage Karur Denken Solutions Department Of Computer Science And Engineering Computer Science And Engineering Department of CSE Sri Sairam Institute of Technology Chennai India School of Technology Woxsen University

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The paper proposes the new radar technology for the first stages of detection of the disease without the subjecting the patient to surgery. In this paradigm, such high-end radar technologies such as bistatic and monostatic patterns assist in identifying small tumours that are far from the surface of the breast tissue. One of them is applying the Ultra-Wideband 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 band pass Finite Impulse Response filters for the best data transfer and to eliminate interferences. The capability for sustaining practices recommended by the Federal Communication Commission concerning spectral standards allows signals to proceed safely through breast tissue. by modifying the amplitude and phase of the transmitted pulse in the study, it increases the power of the main lobe radiated as well as on the same note reduces the levels of sidelobes and losses in the channel as these are accomplished using radar antennas. It found that because of the characteristics of the backscattered signals, there are specific temporal and spectral patterns that can reliably indicate the existence of tumor. based on stimulations, there are huge disparities in electric field strength introduced in normal and malignant breast tissue, which supports the efficiency of the new radar system. Moreover, among the important information accumulating duties we can distinguish specific Absorption Rate (sAR) which specifies the tumour place as it being illustrated by the raised energy absorption in breast malignant tissue relatively to healthy tissue. similar experiments are also carried out with an actual heterogeneous breast phantom with tumors, and it can also be seen that the results are almost identical with the above simulation results and hence it can be understood that the enhanced radar technology is good enough for the earliest detection of breast cancer. At thi

关键词： band-pass filters simulation Radar detection Phantoms breast tissue Radar antennas specific absorption rate breast cancer Ultra wideband radar Tumors

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Machine Learning Models for Enhancing Time series Demand Forecasting in business Intelligence

Machine Learning Models for Enhancing Time Series Demand For...

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Emerging Technologies and Innovation for sustainability (EmergIN), International Conference on

作者： soummya Chowdhury shobha Mishra Neeraj sharma P. srinivas Reddy Yogesh Kumar sharma Ayushi Tyagi School of Business GITAM (Deemed to be) University-Hyderabad Campus Hyderabad India Department of Computer Science and Engineering Dewan V S Institute of Engineering and Technology Meerut India Mechanical Engineering Department CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

This research explores the efficiency of multiple machine learning models in the context of intelligent demand forecasting in the industry of business intelligence. The research is based on a dataset that presents the sales history of a textile company during a one-year time series. In particular, the research analyses four models: Artificial Neural Network, Long short-Term Memory, Decision Tree, and Linear Regression to forecast demand based on the sales in the past. Moreover, the analysis applies multiple preprocessing stages to clean the dataset, such as missing value imputation, outlier detection, and feature engineering. The models are trained and tested using a part of the dataset where 70% of data is used for training, and the remaining 30% is utilized for testing. The results reveal that ANN shows the most explicable results with the lowest data loss and the highest accuracy in demand forecasting. Meanwhile, LsTM is competitive but shows slightly worse accuracy results than ANN. The Decision Tree and Linear Regression models provide some insights into the issue but have higher data loss and lower accuracy. The results show that the use of advanced machine learning models, particularly ANN, is vital for the accurate forecasting of demand in the context of business intelligence. such forecasting helps companies to predict demand more accurately and optimize their inventory management and supply chain in general, which increases the efficiency of their work. Among the potential subject to further exploration are more complex ensembles or hybrid models that can increase the accuracy of demand forecasting in the rapidly changing business landscape. Overall, the research makes a valuable contribution to the understanding and application of machine learning in demand forecasting.

关键词： Accuracy Time series analysis Linear regression Demand forecasting Machine learning Predictive models Data models business intelligence Decision trees Long short term memory

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