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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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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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Development of Green and Non-green Product Manufacturing Model Under Carbon Emission and Price Dependent Demand Using Multi-objective Optimization

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International Journal of Applied and Computational Mathematics 2023年第5期9卷 1-12页

作者： sahoo, Deepanjali Manna, Amalesh Kumar Pati, Jitendra Kumar Tripathy, Arun Kumar Department of Mathematics C. V. Raman Global University Bhubaneswar 752054 India Department of Basic Science and Humanities University of Engineering and Management Kolkata 700160 India KIIT International School Bhubaneswar Odisha 751024 India Department of Mathematics S.S.B College Mahakalapara Odisha Kendrapara India

A large number of customers in the developed countries are aware of regarding the pollution effects of manmade products on environment and human body. Keeping this issue in mind, the manufacturer is produced both green (eco-friendly with environment) and non-green (harmful for environment and human being) products. Motivated from this fact, an inventory model for non-green and green production process with carbon emission investment is developed. The production firms inspect produced non-green and green products to separate perfect and defective items. A part of defective items is reworked by replacing or repairing defective components to reduce the manufacturer average cost. In this study, customers’ demand is considered as a function of selling prices of green and non-green products as well as green level of the product. The aim of this work is to construct multiple objective functions related to the green production inventory model and solve these objective functions by metaheuristic algorithms. Also, the optimal values of production rates, selling prices of the green and non-green products, green level of the green product, carbon emission investment level, production periods of green and non-green products are determined which is minimizing the manufacturer average costs. Finally, numerical experiment is considered to examine the proposed model. © 2023, The Author(s), under exclusive licence to springer Nature India Private Limited.

关键词： Carbon emission Green production Meta-heuristics algorithms Multi-objective optimization

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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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Hand Talk: sign Language to Text Converter using CNN

Hand Talk: Sign Language to Text Converter using CNN

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International Conference on I-sMAC (IoT in social, Mobile, Analytics and Cloud) (I-sMAC)

作者： N. shirisha D. b. v. Jagannadham Padma Parshapu sivanaga Malleswara Rao singu v. sravani Kumari D. Abdus subhahan Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of E.C.E G.V.P College of Engineering Visakhapatnam India Department of Computer Science and Engineering (DS) CVR College of Engineering Hyderabad Vensai Technologies Inc. Cumming GA USA Department of CSE Godavari Institute of Engineering & Technology Rajahmundry Department: CSE B V Raju Institute of Technology

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research presents a novel system for converting sign language gestures into text, bridging the communication gap between non-sign language speakers and the deaf and dumb community. The system utilizes advanced image processing and machine learning techniques to accurately interpret and translate sign language. The proposed system employs a Convolutional Neural Network (CNN) classifier to process captured hand gestures and convert them into text. Through rigorous training and testing, the svM classifier achieved a 96.7% accuracy rate for one-minute intervals. This research contributes to the development of accessible and efficient sign language interpretation systems, facilitating communication and promoting inclusivity. by bridging the language barrier, this technology can enhance social interactions and improve the quality of life for individuals with hearing impairments.

关键词： Training support vector machines sign language Text recognition Auditory system Machine learning vectors Convolutional neural networks Research and development Testing

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DeepFake Detection Through Key video Frame Extraction using GAN

DeepFake Detection Through Key Video Frame Extraction using ...

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Automation, Computing and Renewable systems (ICACRs), International Conference on

作者： Lalitha s Kavitha sooda Department of Computer Science & Engineering B.M.S. College of Engineering Bangalore

ISBN: (纸本)9781665460859

The emergence of deepfake videos in recent years has made image falsification a real threat. A deepfake video uses deep learning technology to substitute a person's face, emotion, or speech with the face, emotion, or speech of another person. Finding such deceptive deepfake videos on social media is the first step in preventing them. A robust neural network-based technique to identify false videos is presented in this paper. An important video frame extraction approach is used to speed up the process of finding deep fake videos. A model made up of a convolutional neural network (CNN) and a classifier network consisting of GAN technology is provided. Resnet, Resnext50 and LsTM were passed over in favor of the Confusion Matrix when deciding which structure to pair with the classifier while detecting the fake video. The model is a method for detecting visual artefacts. The subsequent classifier network uses the feature vectors from the CNN module as this is the input to categorize the video whether it is fake or real one. The dataset is considered from DeepFake Detection Challenge to get the best model. The key goal is to get high accuracy without using a lot of data to train the model. In comparison to earlier efforts, the key video frame extraction method dramatically decreases computations by achieving 97.2% accuracy using the Deepfake Detection Challenge dataset.

关键词： Deep learning Deepfakes visualization Renewable energy sources social networking (online) Computational modeling Generative adversarial networks

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A miniaturized UWb monopole antenna for sub-6 GHz 5G wireless applications

A miniaturized UWB monopole antenna for sub-6 GHz 5G wireles...

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2023 IEEE silchar subsection Conference, sILCON 2023

作者： Panja, Aparna De, Arnab bhattacharjee, Koyndrik Maity, somnath bhattacharya, Ankan Roy, bappadittya sarkar, Partha Pratim bhattacharjee, Anup Kumar National Institute of Technology Deparment of Ece West Bengal Durgapur713209 India Vadlamudi Dept. of Advanced Computer Science and Engineering Vignan's Foundation for Science A.P Guntur India Dr. B C Roy Engineering College Department of Civil Engineering West Bengal Durgapur713206 India Government Women's Polytechnic Deparment of Ece Jharkhand Jamshedpur832108 India Hooghly Engineering & Technology College Dept. of Electronics and Communications Engineering West Bengal Hooghly India VIT-AP University School of Electronics Engneering Amaravati India University of Kalyani Deparment of Engineering and Technological Studeies Kalyani India

ISBN: (纸本)9798350314144

This article describes a miniaturized, ultra-wideband monopole antenna with dimensions of 23.00 x 20.00 x 1.60 mm3 (0.23λ0 x 0.23λ0 x 0.016λ0) designed for sub-6 GHz 5G wireless applications. The suggested layout is compatible with sub-6 GHz 5G, including satellite X-band (7.5-8.5 GHz), ITU n77 (3.3-4.2 GHz), and n79 (4.4-5 GHz) bands, 502.11a WLAN (5.15 - 5.85 GHz), and WiMAX bands (3.5/5.5 GHz). The suggested structure uses a fusion shape of rectangular, triangular, and trapezoidal slits on the ground plane and isosceles triangle, octagonal slot on the metal patch to improve the radiation characteristics and impedance bandwidth. The addition of rectangular slits which are incorporated into the bottom plane just below the microstrip line and trapezoidal-shaped slots shifts the lower cut-off frequency towards the left, while the octagon-shaped slot and the isosceles triangular slit on the radiating patch shift the higher cut-off frequency towards the right. The suggested structure operates over the range from 3.10 to 18.87GHz, having an equivalent fractional bandwidth of 143.56% and a peak gain of around 5.37 dbi. The antenna has a radiation efficiency of 80.00% and displays a decent s11 ≤-10 db throughout the bandwidth. Results from measurement and simulation are good in agreement. © 2023 IEEE.

关键词： Monopole antennas

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A Comparative study about Content-based Image Retrieval using Features from Deep Convolutional Neural Networks

A Comparative Study about Content-Based Image Retrieval usin...

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Communication, security and Artificial Intelligence (ICCsAI), International Conference on

作者： budi srikrushna Pinaki Meka Dheeraj Reddy srisatya Kapardi Department of Electronics and Communication Engineering M.V.S.R Engineering College Osmania University Hyderabad India Department of Computer Science and Engineering Vasavi College of Engineering Osmania University Hyderabad India

"Content-based Image Retrieval (CbIR)" is a popular approach that is frequently used to handle the image retrieval problem. Despite the fact that the CbIR approach has seen tremendous improvements, the fundamental challenge of finding and retrieving pertinent photos with high levels of accuracy remains. A lot of demand exists for the creation of efficient CbIR systems that utilise modern techniques. Convolutional neural networks (CNN) were used, which led to a substantial paradigm shift in machine learning and outstanding performance. This study's major objective is to ascertain whether deep CNN models can be employed to boost picture retrieval effectiveness. In a range of image-processing tasks, such as object identification, classification, and clustering, among others, CNNs excel very well. This work proposed the CbIR methodology, which implements the extraction of features by using a range of pre-trained CNN models. For this experimental research project, radiology and meteorological datasets were used. The findings demonstrated that pre-trained deep CNN models' learnt features may be quickly applied to image retrieval tasks and outperform hand-crafted features utilized in conventional CbIR techniques in terms of performance. A CbIR framework using features from a "pre-trained deep CNN model (vGG16)" was provided in accordance with the study's findings. Put the recently created CbIR algorithm through its paces using a "multi-class dataset" made up of images connected to the weather. The innovative CbIR technique resulted in an average retrieval precision of 86.73%. The standard CbIR design, which relies on manually created features (such color, texture, and form attribute), has produced an average retrieval precision of 73.25% in comparison to this. The unique methodology outperformed the traditional method, which showed a 13.48% larger gain in average precision.

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An Architecture-based self-Typing service for Cloud Native Applications

An Architecture-based Self-Typing Service for Cloud Native A...

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smart Electronics and Communication (ICOsEC), International Conference on

作者： srinivasa Rao Mandalapu Kalaimagal sivamuni Chitra Devi D Upendra singh Aswal s. Irin sherly N. Alangudi balaji Department of Computer Science and Business Systems R.V.R. & J.C. College of Engineering Chowdavaram India Department of Computer Science and Engineering MK College of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering S.A.Engineering college Thiruverkadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Information Technology Panimalar Engineering College Poonamallee India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

This research study analyzes Kubow, a service that automatically adjusts to new circumstances, built on a modular framework for cloud-native applications. To accommodate Kubernetes and Docker containers, the Rainbow self-adaptation framework was upgraded to become Kubow. This research provides an overview of Kubow's architecture, highlights its main design decisions, and illustrates it with a simple example of how to set it up and utilize it. It takes more than just installing software on virtual machines to get apps running in the cloud. Cloud applications need continuous management so that they can 1) adjust their resources according to the demand, and 2) respond to temporary challenges by duplicating and restarting the components to offer resilience on unstable infrastructure. To give programmed and responsive responses to disappointments (wellbeing the board) and changing natural conditions (auto-scaling), and framework boundaries.

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