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Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： Pushan Kumar Dutta Pronaya Bhattacharya Sushil Kumar Singh El-Sayed M. El-kenawy Richa Pandey School of Engineering and Technology Amity University Kolkata Computer Engineering Department Marwadi University Rajkot Gujarat India Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Computer Science and Engineering Graphic Era Hill University Haldwani Uttarakhand

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The proliferation of Internet of Things (IoT) devices has led to unprecedented challenges in data management and transmission efficiency. This paper introduces a novel dual-level approach to data compression and reduction in IoT networks, addressing these challenges by optimizing data handling at both sensor node and gateway levels. Unlike conventional single-level techniques, our method provides a more comprehensive solution to data optimization in IoT ecosystems. At the sensor node level, we implement Run Length Encoding (RLE) compression, while at the gateway level, we integrate progressive data clustering and reduction strategies. This dual-level approach demonstrates significant improvements in data reduction compared to single-point optimization methods. Our comparative analysis reveals the synergistic effects of combining node-level compression with gateway-level data reduction, resulting in enhanced overall system performance. We evaluate the scalability and adaptability of our approach across diverse IoT applications, considering energy efficiency implications for resource-constrained devices. Additionally, we discuss alternative data reduction techniques such as attribute reduction, canonical polyadic decomposition, and edge computing-based filtering, contextualizing our method within the broader landscape of IoT data optimization. The paper also examines the impact of our approach on network bandwidth utilization and explores future directions for multi-level data optimization in complex IoT architectures. Our findings suggest that the proposed dual-level method offers a promising solution for managing the growing data challenges in IoT networks, paving the way for more efficient and scalable IoT systems.

关键词： Temperature measurement Wireless communication Wireless sensor networks Data compression Bandwidth Logic gates Encoding Energy efficiency Internet of Things Artificial intelligence

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A Concise Review Of MRI Feature Extraction And Classification With Kernel Functions

A Concise Review Of MRI Feature Extraction And Classificatio...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Jagendra Singh Chandrakala Arya Nagendar Yamsani Mohit Kumar Prabhishek Singh Vivek Kumar Bhagat School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Computing Graphic Era Hill University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Amity School of Engineering and Technology Amity University Noida India

Magnetic resonance imaging is a common medical imaging tool for the identification and monitoring of brain diseases. Accurate classification of MRI brain pictures is extremely beneficial for early diagnosis and therapy planning. In this study, a method for MRI classification and feature extraction using a Support Vector Machine (SVM) and hybrid RBF which is radial Basis function kernel is proposed. Some of the interconnected steps in the suggested methodology include pre-processing, hybrid RBF kernel creation, feature extraction, SVM classification, feature selection, model evaluation, and model optimization. The GLCM technique is utilized to derive texture characteristics from MRI images. A blend of multiple RBF kernels with different parameter settings yields a hybrid RBF kernel. Performance indicators are used to assess the SVM classifier after it has been trained on a training dataset. The hybrid RBF kernel and the SVM's parameters are altered using model optimization. The suggested methodology intends to classify MRI brain pictures appropriately for enhanced treatment planning and disease diagnosis.

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Investigation of SLA Management in Cloud Computing and Future Directions 2

Investigation of SLA Management in Cloud Computing and Futur...

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2nd International Conference on Computational Methods in science and technology, ICCMST 2021

作者： Singh, Mandeep Bhushan, Shashi Rani, Shanky Chandigarh Engineering College Department of It Mohali India Ikpgtu Kapurthala India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Bihar Patna India

ISBN: (纸本)9781665458665

Cloud computing is an on-demand service provisioning technology in which computer and networking resources like software applications, computation power, storage space and software based network devices are provided to the customer under a service level agreement (SLA). In dynamic environment of cloud computing service level agreement and its management is very crucial component from customer's trust and long term business opportunity point of view. The SLA management process includes many activities such as SLA Negotiation, SLA Pricing and SLA Monitoring etc. Many SLA management approaches have been proposed by researchers. This paper systematically investigates different research publications branching form SLA management into different directions and assesses the relative extent of research done so far in each direction. The analysis reveals that latest trends in SLA related research are SLA violations, Auditing, and development of SLA frameworks. © 2021 IEEE.

关键词： Cloud computing

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SSVBT: Secure Certificateless Aggregate Signature Scheme in VANETS with Block Chain technology

SSVBT: Secure Certificateless Aggregate Signature Scheme in ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ruchi Mittal Varun Malik Manisha Aeri Kuldeep Singh Kaswan S Vikram Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun Uttarakhand Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The proliferation of Vehicular Ad-hoc Networks (VANETs) highlights the crucial need for secure, privacy-protecting inter-vehicle communication. By integrating blockchain technology with a safe certificate-less aggregate signature process, this research presents a novel approach to enhancing VANET security. Traditional VANET signature systems that rely on PKI and certificates have their limitations solved by the suggested approach. Using a certificate-less paradigm eliminates the requirement for a certificate authority when creating keys, making key exposure and compromise less probable. By combining several signatures into one smaller one, the aggregate signature method significantly decreases network communication cost. The paper introduces blockchain technology as an extra component to the certificate-less aggregate signature, further ensuring the honesty and openness of the VANET. The blockchain records and validates transactions, aggregate signatures, key alterations, and more using its distributed and immutable ledger. This not only improves the signature scheme but also creates a VANET environment that is trustworthy and robust.

关键词： Privacy Costs Aggregates Merging Vehicular ad hoc networks Public key Market research

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People Counting in Public Spaces using Deep Learning-based Object Detection and Tracking Techniques

People Counting in Public Spaces using Deep Learning-based O...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： N Krishnachaithanya Gurdit Singh Smita Sharma Rangisetti Dinesh Sumeet Ramsingh Sihag Kamna Solanki Abhishek Agarwal Mrinalini Rana Ujjwal Makkar School of Computer Science and Engineering Lovely Professional University Kapurthala Punjab India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Rohtak Haryana Department of Mechanical Engineering Lovely Professional University Kapurthala Punjab India

Recent advancements in deep learning and machine learning have enabled exact people counting in various applications including crowd management, security, and retail analytics. Deep learning algorithms have proven tremendous promise for accurate and efficient people counting in difficult contexts. This paper offers a technique for counting people that utilises deep learning with MobileNet SSD, centroid tracking, and trackable object script. Gathering and preparing a labelled dataset, training a MobileNet SSD model, implementing centroid tracking and the trackable object script, increasing system performance, testing it on real-world scenarios, and deploying it in a production environment are all part of the approach. The recommended approach provides a wide framework for creating and deploying a deep learning-based people-counting system that can be customized and tuned to match a number of applications and purposes. Additionally, we have added alerts on maximum capacity, timely scheduling and input feed from the internet.

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Study and Analysis of Offloading in Mobile Cloud Computing

Study and Analysis of Offloading in Mobile Cloud Computing

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2021 International Conference on Technological Advancements and Innovations, ICTAI 2021

作者： Singh, Puneet Singh, Pankaj Rajpoot, Subhadra Singh, Devang Pratap Military College of Telecommunication Engineering Faculty of Communication Engineering M.P Mhow India Birla Institute of Technology Department of Computer Science and Engineering Rajasthan Pilani India Amity School of Engineering and Technology Department of Applied Sciences Greater Noida India Amity School of Engineering and Technology Faculty of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9781665420877

The Mobile Devices (Smart Mobile Phones, Tablets PCS) are becoming a computing and service access devices at anytime and anywhere. These devices are constrained by the CPU workload, memory capacity and battery life. MCC is the technology which enhances the power and capabilities of mobile devices by extending the services and resources of computational clouds to smart mobile devices on demand bases. The offloading mechanism gives the provision to enrich the mobile devices with sufficient resources. This paper explains the offloading mechanism in MCC domain. The objective of this paper is to investigate the issues and challenges for the partitioning of the application. Further it highlights few research questions on how offloading can be done efficiently so that performance improvement as well as energy saving can be achieved. © 2021 IEEE.

关键词： Mobile cloud computing

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Depsobm: Block Matching Algorithm Based on Differential Evolution and Pso for Video Sequences

SSRN

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SSRN 2022年

作者： Dixit, Abhishek Mani, Ashish Department of Computer Science Amity School of Engineering and Technology Amity University Noida India Department of EEE Amity School of Engineering and Technology Amity University Noida India

The block Matching method is an extremely robust and efficient approach for motion estimation of video sequences. For motion estimation, evolutionary algorithms are being used efficiently recently. In this paper, we have proposed a unique method of motion estimation by applying differential evolution and a PSO-based block matching algorithm(DEPSOBM) for motion estimation. The effectiveness of the proposed work is tested using 4 well-known test video sequences. The test video sequences applied for our experiment have all the necessary features like distinct resolutions, different formats, and the frame count which are required in an input video sequence. Additionally, we have also evaluated the performance of DEPSOBM on seven 4K120p sequences from the UVG dataset for exhaustive analysis. The proposed approach is compared with standard block matching algorithms and classical DE and PSO algorithms by comparing with parameters like Structural Similarity (SSIM), Average Number of Search Points, Peak Signal to Noise Ratio (PSNR). The experimental results show that our proposed approach shows better performance as compared to other traditional block matching algorithms. © 2022, The Authors. All rights reserved.

关键词： Motion estimation

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Optimized Dilated Convolutional Neural Network with Quantum Self-Attention Based on Hyper Spectral Remote Sensing Image Classification 4

Optimized Dilated Convolutional Neural Network with Quantum ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Santhosha, T. Ali, H. Mohammed Seeniappan, K. Yamsani, Nagendar Maranan, R. Kaliappan, S. T.S Medchal district501301 India Srm Institute of Science and Technology Department of Mechanical Engineering Ramapuram Campus Chennai89 India Amity University Department of Mechanical Engineering Dubai345019 United Arab Emirates Sr University School of Computer Science & Artificial Intelligence Telangana Warangal506371 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Lovely Professional University Division of Research and Development Punjab Phagwara144411 India

ISBN: (纸本)9798331518981

Hyperspectral remote sensing (RS) involves capturing and analyzing data across numerous, narrow spectral bands in the electromagnetic spectrum. With this technology, it is possible to identify what has been on the Earth surface with unprecedented depth that cannot be done with the mere multispectral images. A Hyperspectral sensor is able to capture hundreds of bands of data, and is therefore able to distinguish between very small differences in surface materials that we cannot even see or conventional multispectral sensors. Hyperspectral remote sensing is widely used in applications like agriculture, environmental monitoring, geology, and defence. To overcome the limitations of current approaches, new strategies are needed. In this manuscript Optimized Dilated Convolutional Neural Network with Quantum Self-Attention Based on Hyper Spectral Remote Sensing Image Classification (DCNN-QSA-PDOA) is proposed. The Indian Pine dataset is where the data is first gathered. The multidirectional shearlet transform based feature extraction (MDST) model is then utilized to extract the hyperspectral image (HSI) features. Following the completion of the feature extraction procedure, the Dilated Convolutional Neural Network with Quantum Self-Attention (DCNN-QSA) is trained on the feature extracted images and used to classify the category information. Here, (PDOA) is utilized for tuning hyper parameters of DCNN-QSA. The proposed DCNN-QSA-PDOA method provides 37.23%, 18.74% and 24.84% higher accuracy compared with existing method like Classifying images from hyper spectral RS using a DCNN-HIS, Dense residual three-dimensional convolutional neural networks are used for hyper spectral RS picture classification (DR-3DCNN-HIS), Fully convolutional neural networks are used for the categorization of hyper spectral remote sensing pictures (CNN-HIS) respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting Purchases and Personalizing the Customer Journey with Artificial Intelligence

Predicting Purchases and Personalizing the Customer Journey ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Varun Malik Ruchi Mittal Rishi Chaudhry Suman Avdhesh Yadav Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Institute of Management Studies and Research Maharshi Dayanand University Rohtak Haryana India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

A mastery of consumer behaviour analysis and the ability to personalise the customer experience are prerequisites for success in today's cutthroat business climate. In order to improve the customer experience and make purchase predictions, this research explores the world of artificial intelligence. Using a systematic three-step technique, this study aims to improve consumer purchase prediction and provide individualised customer experience. The dataset is preprocessed using Enhanced Z-score normalisation first. This will ensure uniformity and mitigate the impact of extreme cases. After a better collection of features is found using Recursive Feature Elimination (RFE), the dataset is optimised for classification tasks. Lastly, state-of-the-art technology is used in the research via the usage of Enhanced Long Short-Term Memory (LSTM) networks for classification and prediction. Better LSTM networks may be able to detect nuanced changes in customer behaviour, resulting in more accurate predictions. Our study aims to contribute to the advancement of AI applications in CRM by following this rigorous process. We want to help companies better their strategy, offer more personalised experiences, and boost the possibility of consumers completing a purchase by delivering relevant insights.

关键词： Systematics User experience Reliability Artificial intelligence Task analysis Long short term memory Optimization

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Blockchain and Machine Learning for Advanced Pattern Recognition in Biometric Security Systems

Blockchain and Machine Learning for Advanced Pattern Recogni...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： K Mayuri B S Smitha Ginni Nijhawan Bolleddu Devananda Rao Shubhi Gupta Ali Albawi Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Amity School of Engineering Technology Amity University Greater Noida Uttar Pradesh India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Blockchain and machine learning allow biometric security systems to recognize complex patterns. This study analyzes how two unique technologies may improve fingerprint system safety and reliability. To demonstrate blockchain and machine learning in biometrics. The main reason for this study is that fingerprint protection is becoming more important in today’s digital world. There needs to be strong name verification right away because there is more sensitive data and privacy issues. Traditional biometric systems have come a long way, but there are still problems with data security, privacy, and being able to grow. This study suggests important worries about how blockchain technology and machine learning might be able to improve pattern recognition, make security measures stricter, make data unchangeable, and reach the level of scalability needed for widespread use. Upon careful study of related approaches and works, biometric security could be improved by combining the decentralized and unchangeable record of blockchain with the pattern recognition abilities of machine learning. Ten important ways are looked at, and each one has its own pros and cons. A trio of CNNs, SVMs, and RNNs are used in the method we suggest. The suggested blockchain and machine learning fusion is shown to be better in a detailed analysis. The study ends with compared tables that show the parameters and performance evaluations of $\mathbf{1 0}$ similar systems.

关键词： Performance evaluation Data privacy Scalability Machine learning Transforms Fingerprint recognition Blockchains Security Reliability Protection

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