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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Sandeep Kumar Saurabh Ankita Aggarwal Aryan Institute of Technology Ghaziabad India Department of Computer Science & Engineering Chandigarh College of Engineering Chandigarh Group of Colleges Mohali India Department of Electronics & Communication Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

There is no doubt that the main issue to address in these patients is the early diagnosis of Bladder carcinoma. This work aims at initiating a new path in this research and undertakes advanced artificial intelligence (AI) and machine learning to enhance bladder cancer diagnosis. Bladder cancer is one of the more widespread malignant neoplasms, it is more prevalent in the male population and particularly in male gerontocracy. Conventional diagnostic approaches are hypertrophied and crippled by constraints that lead to diagnostic imperfections and unkind lateness. There stands a great opportunity to deal with these challenges through the implementation of Artificial Intelligence in healthcare. By incorporating the modern artificial intelligence models such as CNN and RNN along with other modern techniques, this research intends to improve the diagnostic outcomes and provide strong prognosis. It is through a comprehensive method of data preprocessing and feature engineering, an incorporation of ethical factors, and the use of complex algorithms that this research hopes to advance the future of precision medicine. In this context, the customized solutions based on the most effective data also expand the scope of the diagnosis of bladder cancer and bring positive changes to the treatment of patients.

关键词： Recurrent neural networks Precision medicine Medical services Learning (artificial intelligence) Bladder Convolutional neural networks Artificial intelligence Prognostics and health management Neoplasms Cancer

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Optimizing Optical Attocells Positioning of Indoor Visible Light communication System

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computers, Materials & Continua 2023年第2期74卷 3607-3625页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at School of Electrical Engineering Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti Teknologi Malaysia(UTM)SkudaiJohorMalaysia Department of Electronics and Electrical Engineering Faculty of EngineeringInternational University of AfricaKhartoum12223Sudan Department of Communication Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu Pahat86400JohorMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC),which is a prominent emerging solution that complements the radio frequency(RF)technology,exhibits the potential to meet the demands of fifth-generation(5G)and beyond *** random movement of mobile terminals in the indoor environment is a challenge in the VLC *** model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio(SNR).As such,the optical attocells positions were optimized in this study with a developed try and error(TE)*** optimized optical attocells were examined and compared with previous *** novel approach had successfully increased minimum received power from−1.29 to−0.225 dBm,along with enhanced SNR performance by 2.06 *** bit error rate(BER)was reduced to 4.42×10−8 and 6.63×10−14 by utilizing OOK-NRZ and BPSK modulation techniques,*** optimized attocells positions displayed better uniform distribution,as both received power and SNR performances improved by 0.45 and 0.026,*** the results of the proposed model are optimal,it is suitable for standard office and room model applications.

关键词： Visible light communication(VLC) optical attocell received power signal-to-noise ratio(SNR) bit error rate(BER) coefficient of variation(CV)

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Artificial Intelligence and Li-Fi for Autonomous Vehicles and Future Transportation Systems

Artificial Intelligence and Li-Fi for Autonomous Vehicles an...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Sandeep Mishra Bipin Pandey Rajat Kumar Kaustubh Kumar Shukla Department of Computer Science Engineering Dronacharya Group of Institutions Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering Dronacharya Group of Institutions Greater Noida Uttar Pradesh India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

One of the major challenging task is to maintain the Autonomous vehicles as well as to analyze the future transportation systems. To solve such kind of issues proposed three different technologies, Artificial Intelligence, Light Fidelity and LoRa has been analyzed. AI and LiFi technologies have the potential to revolutionize transportation systems, especially for autonomous vehicles. LiFi uses visible light for data transmission, offering higher rates, security, and energy efficiency. AI is crucial for autonomous vehicles, enabling real-time data transmission, secure communication, intelligent traffic management, and cooperative driving. Challenges include cost, range limitations, and infrastructure integration. The research proposes an innovative system combining LiFi as well as LoRa technologies to improve navigation in congested urban environments. The LiFi transmitter uses visible light to transmit location information to LiFi receivers in vehicles, providing continuous, seamless navigation even in areas without service. The system also broadcasts vehicle location to the outside world via a LoRa module, providing long-range and low-power navigation in non-serviceable locations like terrines, valleys, and tunnels. AI, LiFi, and LoRa are crucial technologies for autonomous vehicles and transportation systems. AI provides the "brain" for autonomous vehicles, while LiFi offers high-speed, secure, and interference-free communication. LoRa is ideal for long-range, low-data-rate communication, enabling accurate decision-making, enhanced safety, and efficient transportation. These technologies are essential for a future autonomous vehicle-friendly future.

关键词： Navigation Transmitters Urban areas LoRa Receivers Light fidelity Real-time systems Security Artificial intelligence Autonomous vehicles

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Precise Identification and Segmentation of Brain Tumour in MR Brain Images Using Salp Swarm Optimized K-Means Clustering Technique 2

Precise Identification and Segmentation of Brain Tumour in M...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Mahendran, N. Muthuvel, P. Arunprasath, T. Pallikonda Rajasekaran, M. Bridget Nirmala, J. Kottaimalai, R. Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Biomedical Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India St. Michael Group of Institutions Tamilnadu kalaiyarkoil India

ISBN: (纸本)9798350347579

Brain tumour delineation is a challenging task from raw magnetic resonance images. To accurately delineate the different parts of tumours is the main aim of dissection process. Among the most common types of cerebral tumour, glioma that arises from glial cells. According to the World Health Organisation (WHO), tumour behaviours and microscopic images can be used to classify gliomas into four different levels. The popular imaging techniques used prior to and following surgical treatment is magnetic resonance imaging (MRI), which aims to provide vital details for the therapeutic plan. For effective tumour delineation from brain MRI, a novel combination of K-means and Salp Swarm Optimization (SSO) Algorithm is proposed. K-means clustering method groups the most similar pixels in to a single cluster. Salp Swarm Optimization Algorithm is one of the nature-inspired metaheuristic optimization algorithms based on the social and foraging behaviour of salps. In biomedical signal processing and control systems, SSO is used to tackle large-scale optimization problems. The proposed methodology's efficiency is validated through testing on various BraTS challenge datasets. The attained average computational time, MSE, PSNR, TC and DS are 16.9 Sec, 0.3787, 52.47 dB, 74.86 % and 83.44 %, respectively. © 2023 IEEE.

关键词： Tumors

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Dynamic Disaster Management with Real-Time IoT Data Analysis and Response

Dynamic Disaster Management with Real-Time IoT Data Analysis...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Dankan Gowda, V. Sharma, Avinash Prasad, K.D.V. Saxena, Rini Barua, Tarkeshwar Mohiuddin, Khalid Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India Symbiosis Institute of Business Management Hyderabad India Pune India King Khalid University College of Business Department of Business Informatics PO Box-3247 Abha61471 Saudi Arabia

ISBN: (纸本)9798350382723

As natural and manmade disasters grew in number, as a result the problem of how to quickly and effectively respond to disaster has become fresh. This is precisely the purpose of this research: Using IoT technologies and powerful data analysis techniques, to integrate them into existing disaster management systems. The beginning of the article contains a broadcasted statement to the effect that traditional methods are insufficient, and real-time data and preemptive response systems are needed. With the application of Internet of Things, an integrated system is proposed in which countless types of information such as weather conditions and seismic activity are gathered by sensors and actuators. Advanced machine learning algorithms and predictive modeling are used to analyze the gathered data. This allows us to make real-time decisions. The design and construction of an IoT-based disaster management system is the methodology behind the research. In particular, we will evaluate how effective it is at reducing response times and increasing overall resilience to disasters. The results show a high efficiency in response, which reflects the feasibility of the method. Finally, the paper discusses the problems encountered in implementing IoT and advanced data analysis of disaster risk and suggests future research avenues. There is no doubt that they will change the present disaster management practice forever. © 2024 IEEE.

关键词： Remote sensing

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Video Colorization Using Modified Autoencoder Generative Adversarial Networks 7th

Video Colorization Using Modified Autoencoder Generative Adv...

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7th International Conference on computer vision and Image Processing, CVIP 2022

作者： Agarwal, Rishesh Das, Manisha Gupta, Deep Radeva, Petia Department of Electronics and Communication Engineering VNIT Nagpur Nagpur India Universitat de Barcelona and Computer Vision Center Barcelona Spain

ISBN: (纸本)9783031314063

Colorization is a visual tool to convey ideas and emotions, especially when it comes to entertainment or film making. It is easy to perceive information from colored images rather than black and white or grayscale images. In the modern digitization era, colorization is even more important, because it helps in-person tracking, objects detection and provides an edge to the new state-of-the-art technologies. This paper presents a method for colorizing grayscale videos using Modified Autoencoder Gen-erative Adversarial Network. The proposed generative model uses modified autoencoders by embedding a DenseNet based architecture. Its property of concatenation of all previous layers helps to increase the color consistency with the increase in the selection of color for an object. Subjective and quantitative results analysis is done using several performance measures. Experimental results show that the proposed model provides an improved and consistent mapping of colors on the video dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Color

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Improving Beamforming in 5G Networks Using Reinforcement Learning

Improving Beamforming in 5G Networks Using Reinforcement Lea...

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2024 IEEE International Conference on communication, Computing and Signal Processing, IICCCS 2024

作者： Mahalakshmi, V. Alabdeli, Haider Devi, G Sandhya Kaur, Simranjit Kharya, Asheesh Bhatt, Vimal G. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Aditya Engineering College Department of Computer Science & Engineering India Chandigarh Group of Colleges Department of Computer Application Chandigarh Engineering College Punjab Mohali India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India A. D. Patel Institute of Technology Cvm University Department of It Gujarat India

ISBN: (纸本)9798350390759

This paper presents a study on training of Reinforcement Learning (RL) for balancing of beamforming in 5G network. Major challenges in giving directions in traditional beamforming collapse in dynamic surroundings: Beamforming which plays an essential role in increasing the strength and quality of a signal. RL provides a solution by allowing system to discover that actions that lead to a favorable outcome in the environment. The research process will involve first having a 5G network emulation, designing a DQN for beamforming, and finally having a comparison with traditional methods or other established algorithms. Effects that are observed on signal-to-noise ratio and throughput, and latencies further showed that RL based beamforming yield enhanced results. This paper also captures the possibilities of RL in handling aspects of 5G network and the development of wireless communication. © 2024 IEEE.

关键词： Reinforcement learning

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Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

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Intelligent Automation & Soft Computing 2023年第2期35卷 2151-2169页

作者： Wahidul Hasan Abir Faria Rahman Khanam Kazi Nabiul Alam Myriam Hadjouni Hela Elmannai Sami Bourouis Rajesh Dey Mohammad Monirujjaman Khan Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communication Engineering Brainware Group of Institutions-SDETBarasatKolkata700124West BengalIndia

Nowadays,deepfake is wreaking havoc on *** content is created with the help of artiﬁcial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded *** visual media manipulations are not new,the introduction of deepfakes has marked a breakthrough in creating fake media and *** manipulated pic-tures and videos will undoubtedly have an enormous societal *** uses the latest technology like Artiﬁcial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL)to construct automated methods for creating fake content that is becoming increasingly difﬁcult to detect with the human ***,automated solutions employed by DL can be an efﬁcient approach for detecting *** the“black-box”nature of the DL system allows for robust predictions,they cannot be completely *** is theﬁrst step toward achieving transparency,but the existing incapacity of DL to explain its own decisions to human users limits the efﬁcacy of these *** Explainable Artiﬁcial Intelligence(XAI)can solve this problem by inter-preting the predictions of these *** work proposes to provide a compre-hensive study of deepfake detection using the DL method and analyze the result of the most effective algorithm with Local Interpretable Model-Agnostic Explana-tions(LIME)to assure its validity and *** study identiﬁes real and deepfake images using different Convolutional Neural Network(CNN)models to get the best *** also explains which part of the image caused the model to make a speciﬁc classiﬁcation using the LIME *** apply the CNN model,the dataset is taken from Kaggle,which includes 70 k real images from the Flickr dataset collected by Nvidia and 70 k fake faces generated by StyleGAN of 256 px in *** experimental results,Jupyter notebook,TensorFlow,Num-Py,and Pandas were used as software,InceptionResnetV2,DenseNet201,Incep

关键词： Deepfake deep learning explainable artificial intelligence(XAI) convolutional neural network(CNN) local interpretable model-agnostic explanations(LIME)

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Entropy: A New Parameter for Image Deciphering

Entropy: A New Parameter for Image Deciphering

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International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020

作者： Cheggoju, Naveen Nawandar, Neha K. Satpute, Vishal R. Image Processing and Computer Vision Lab Department of Electronics and Communication Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (纸本)9789811572333

Data and information, these two terms may look quite similar and also sometimes used with the same meaning. But in-depth, these terms are completely different from each other. This paper mainly focuses on this difference to extract the encrypted information from the data (importantly image data). Here, every aspect of the study and the way of formulation are explained using "Einstein’s theory of relativity" and "Arrow of time" for better understanding. Einstein’s theory is used to explain "how similar things can be seen in different ways?" (here data and information) and arrow of time is used to explain "how the correct information can be extracted from the data?". To explain this concept, an image encrypted using a chaotic logistic map is used. The edge information present in this data is taken as the parameter to differentiate the unorganized data and organized data (correct information). Here, a new parameter called "Randomness parameter (Rp)" is introduced, which gives the entropy (randomness) of the data. The outcome of this parameter is used to differentiate the data and correct information. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Entropy

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A 180-Degree Phase Difference Output of Wilkinson Power Divider Using Microstrip Parallel Coupled Lines 6

A 180-Degree Phase Difference Output of Wilkinson Power Divi...

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6th International Conference on Power, Energy and Innovations, ICPEI 2024

作者： Phanomupatum, Pratya Phromloungsri, Ravee Tama, Rachata Pongthavornkamol, Tiwat Wongpatsa, Decha Sonasang, Somchat Nakhon Phanom University Faculty Industrial Technology Department of Electronics Nakhon Phanom Thailand Udon Thani Rajabhat University Computer and Communication Engineering Faculty of Technology Udon Thani Thailand National Electronic and Computer Technology Center National Science and Technology Development Agency Location and Automatic Identification System Research Team Communication and Network Research Group Pathumthani Thailand Faculty Industrial Technology Nakhon Phanom Thailand

ISBN: (纸本)9798350356779

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency. © 2024 IEEE.

关键词： Radio astronomy

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