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International Conference on Computing and Networking Technology (ICCNT)

作者： Bias Bhadra Suchismita Chinara Abhijit Kundu Department of Computer Science Engineering National Institute of Technology Rourkela Odissa India Department of Electronics & Communication Engineering Chaibasa Engineering College Jharkhand India

In this paper, asymmetrical super-lattice SnTe/Pb 0.98 Sn 0.02 Te exotic p + -i-n + nano-scale photo-sensor has been reported with significant improvement of photo-responsivity and quantum efficiency in the near infrared wavelength region. The photo-electric characteristics of the proposed Device-Under-Test (DUT) are studied by developing self consistent Non-linear Quantum Modified Drift-Diffusion (NQMDD) Model with appropriate boundary conditions. The proposed NQMDD model is validated by comparing the simulated results with the experimental results under a-like operating-conditions. An excellent agreement, in between the simulated and experimental results, under a-like operating-conditions, reveals the validity of the proposed NQMDD simulator. The performance of the DUT is enhanced by incorporation of alternate thick – SnTe and thin- Pb 0.98 Sn 0.02 Te layers into the active region of the device. The extensive study proves that the photo-electric characteristics of the DUT have improved significantly compared to its Si counterpart. To the best of authors’ knowledge this is the first report on asymmetrical super-lattice SnTe/Pb 0.98 Sn 0.02 Te exotic p + -i-n + nano-scale IR photo-sensor. The proposed DUT will play an important role for device-engineering for the development of highly sensitive circuits in defence and bio-medical industries.

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Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

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computers, Materials & Continua 2022年第5期71卷 3145-3160页

作者： Vatsala Anand Sheifali Gupta Deepika Koundal Shubham Mahajan Amit Kant Pandit Atef Zaguia Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Systemics School of Computer ScienceUniversity of Petroleum&Energy StudiesDehradunUttrakhandIndia School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Biomedical image analysis has been exploited considerably by recent technology involvements,carrying about a pattern shift towards‘automation’and‘error free diagnosis’classification methods with markedly improved accurate diagnosis productivity and cost *** paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy *** proposed model has four convolutional layers,two maxpool layers,one fully connected layer and three dense *** the convolutional layers are using the kernel size of 3∗3 whereas the maxpool layer is using the kernel size of 2∗*** dermoscopy images are taken from the HAM10000 *** proposed model is compared with the three different models of ResNet that are ResNet18,ResNet50 and *** models are simulated with 32 batch size and Adadelta *** proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90,the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as ***,features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease *** model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.

关键词： Dermoscopy images CNN deep learning classification optimizer ResNet diagnosis skin disease

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A binary Bird Swarm Optimization technique for cloud computing task scheduling and load balancing

A binary Bird Swarm Optimization technique for cloud computi...

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2022 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2022

作者： Magesh Kumar, B. Kumar, M Sathish Shadrach, Finney Daniel Polamuri, Subba Rao Poonkodi, R. Pudi, Vasudeva Naidu Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Sathyamangalam India College of Engineering Department of Cyber Security Karpagam Tamil Nadu Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kakinada Institute of Engineering and Technology Department of Computer Science and Engineering Korangi Kakinada India Sri Eshwar College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Matrusri Engineering College Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Cloud computing is a new paradigm for highperformance computation that combines a wide collection of autonomous mixed devices with a flexible computational construction to manage and deliver services. High-performance cloud computation is a subset of cloud computation. When it derives to improving the overall performance of cloud computation, task preparation is one of the most difficult things to improve. This can include response time, make span, and the degree of imbalance. When it comes to reducing power usage, processing time and increasing profit for service providers, task scheduling is vital. Heuristic methods like Binary bird swarm optimization (BBSO) were created to address the problem of inefficient procedures. However, if these algorithms are not paired with additional experiential or meta-heuristic procedures, the optimal solution will not be produced. Because of their high temporal complexity, these algorithms are less useful in real-world scenarios. BSO's binary form is being proposed for cloud computing workload scheduling and balancing in the NP-problem. Our goal function determines if heterogeneous VMs have the greatest completion time difference by taking into account the updating and optimization limits discussed in this research. We developed a technique for updating particle positions in conjunction with load balancing. Metaheuristics and heuristics fail to outperform the proposed technique when it comes to job scheduling and load balancing. Achieved this level of success thanks to the deployment of an artificial neural network. In terms of resource allocation, ANN has shown encouraging outcomes. CNNs are more accurate and faster in predicting targets than multilayer perceptron networks. © 2022 IEEE.

关键词： Cloud computing

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Smart Antennas and Machine Learning: Enhancing Beamforming in Wireless communications

Smart Antennas and Machine Learning: Enhancing Beamforming i...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： A V V Sudhakar Upasana Sharma K. Visala K. Suresh Kumar Chirag Arora Kuldeep Chouhan Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communication Engineering ABES Engineering College Ghaziabad UP Computer Science Engineering Vignan's Institute of Information Technology Vishakapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Chennai Department of Electronics and Communication Engineering KIET Group of Institutions Ghaziabad U.P Department of Computer Science and Applications Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

One of the most effective and widely used technological advancements for increasing capacity, enhancing quality, and expanding coverage in wireless communications systems is the smart antenna. Smart antennas are essential for increasing the effectiveness and dependability of wireless communication systems because they make use of cutting-edge signal processing techniques. Smart antennas' beamforming capability, which concentrates signal transmission and reception in particular directions, increases signal strength, lowers interference, and expands network capacity. As wireless networks become more complicated, machine learning (ML) has become a potent tool for beamforming optimisation. ML algorithms can proactively adjust antenna characteristics by using real-time data, learning from fluctuations in the environment to optimise performance. In order to improve beamforming capabilities, this research investigates the incorporation of ML with smart antenna systems. We go over several ML techniques, such as supervised and reinforcement learning, for resource allocation, user localisation, and signal-to-noise ratio (SNR) optimisation. Additionally, we draw attention to how deep learning might enhance spatial filtering, facilitating more effective communication in 5G and beyond. The potential direction for next-generation wireless communication networks is presented by the convergence of ML and smart antennas.

关键词： Wireless communication Array signal processing OFDM Transmitting antennas Signal processing algorithms Interference Resource management Antennas Optimization Signal to noise ratio

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Adaptive IBVS Based Planar Non-Holonomic Target Tracking for Quadrotors

Adaptive IBVS Based Planar Non-Holonomic Target Tracking for...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Yogesh Kumar Sayan Basu Roy Sujit P.B. Department of Electronics and Communication Engineering Indraprastha Institute of Information Technology Delhi Delhi India Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents an adaptive image-based visual servoing (IBVS) technique for a quadrotor to track a moving planar non-holonomic target with unknown constant body fixed linear velocity and arbitrary angular motion. We propose a novel target velocity estimator augmented kinematic controller based on appropriately derived feature dynamics in a virtual image plane. Through a comprehensive theoretical analysis based on the Lyapunov direct method, we prove the convergence of feature tracking errors to zero for translational kinematics and to an ultimate bound for rotational kinematics despite the unknown target motion and parameter uncertainty in desired feature depth. The theoretical claims are validated using model-in-the-loop and software-in-the-loop (SITL) simulations.

关键词： Adaptation models Visualization Uncertain systems Target tracking Kinematics Visual servoing Stability analysis

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Resilient Framework for Next-Generation Heterogeneous Wireless Networks in Disaster Management

Resilient Framework for Next-Generation Heterogeneous Wirele...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ajisha Mathias J. Relin Francis Raj S R Raja R. Santhana Krishnan S. Balamurugan G.Vinoth Rajkumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Associate professor Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Associate Professor Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mathematics Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The growing occurrence and concentration of natural and artificial disasters necessitate the development of robust communication systems capable of maintaining reliable and efficient connectivity under adverse conditions. Heterogeneous wireless networks (HSNs) have emerged as a promising solution, offering the flexibility to integrate various wireless technologies and adapt to changing environments. However, the inherent complexity and dynamic nature of HSNs present significant challenges in ensuring resilience and reliability, particularly in disaster scenarios where network infrastructure may be severely compromised. This research investigates a Resilient Framework for Next Generation Heterogeneous Wireless Networks in Disaster Management, focusing on enhancing communication reliability and efficiency during critical scenarios. The study introduces Network Coding Enhanced COPE (NC-COPE), a novel algorithm integrating network coding principles into the COPE framework. Simulation analysis compares NC-COPE with existing algorithms: Optimized Link State Routing (OLSR), Coding-Aware Routing (CAR), and Dynamic Source Routing (DSR) using metrics such as Packet Delivery Ratio (PDR), Routing Overhead, and Resilience to Node Failures. NC-COPE demonstrates superior performance in maintaining high PDR, reducing routing overhead, and enhancing network resilience under node failures. The findings underscore NC-COPE's potential to mitigate communication challenges in disaster management scenarios, highlighting its role in future wireless network deployments.

关键词： Wireless networks Heuristic algorithms Disasters Focusing Disaster management Network coding Routing Automobiles Next generation networking Resilience

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AI-based Learning Techniques for Bladder Cancer Detection

AI-based Learning Techniques for Bladder Cancer Detection

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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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Recommendation of Crops and Fertilizer, Detection of Crop Weed, Pest and Diseases using Machine Learning

Recommendation of Crops and Fertilizer, Detection of Crop We...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S Mahalakshmi A Jose Anand T. Sampath Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering KCG college of Technology Chennai India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India

The term “smart farming” refers to the management of farms using contemporary information and communication technologies to improve product quality and quantity while diminishing the requirement of human labor and the possibility of undesirable results and loss of time and cost. Modern farmers are equipped with various technologies, including software, sensors, robotics, and data analytics. The farmers can use these to make farming a data-driven process. Conventional farming practices involve poor analysis, heavy labor, and time and financial waste. Therefore, we suggest a system that would assist farmers in combining their current farming practices with cutting-edge technologies like deep learning, machine learning, and remote sensing built using an open technology stack of Angular for the frontend, Flask for the backend, and MySQL and Google Earth for the database. Our application guides the farmers by recommending the best crop to grow and fertilizer to use using machine learning techniques, detects pests, weeds, and crop diseases using deep learning techniques, performs remote sensing analysis using GEE by obtaining the area name, and provides weather details by obtaining the city name.

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Enhancing Blockchain Scalability: Innovative Solutions for Optimized Performance in Decentralized Networks 7

Enhancing Blockchain Scalability: Innovative Solutions for O...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Srivastav, Akanksha Agarwal, Sheenu Jain, Arpit Tayal, Shikha Jain, Shalu Sharma, Khemraj Tula's institute U.K. Dehradun India Uttarakhand Technical University Department of Electronics and Communication Engineering U.K. Dehradun India Koneru Lakshamaiah Education Foundation Department of Computer Science & Engineering A.P. Vadessahwaram India Tula's Institute Department of Computer Science & Engineering Uttarakhand Dehradun India Maharaja Agrasen Himalayan Garhwal University Uttarakhand Pauri Garhwal India Nanhua University Program in Management Sciences Department: Department of Business Administration Dalin Chiayi62248 Taiwan

ISBN: (纸本)9798350350067

A substantial number of people are presently making use of decentralised cryptocurrencies that are built on blockchain technology. These crypto currencies have garnered a lot of interest over the course of the last few years and are currently being used by their users. Being the first use of blockchain technology, Bitcoin has been a resounding success, which drives further research and improvement in the sector concerned with blockchain technology. Bitcoin was the first usage of blockchain technology. Bitcoin, on the other hand, is susceptible to significant performance issues due to the fact that it has a high transaction latency and a limited throughput. The operations of the cryptocurrency are considerably impacted as a result of these concerns. As a result of the fact that other cryptocurrencies that rely on proof-of-work also carry over its deficiencies, there has been an increase in the number of concerns about the scalability of blockchain technology. The fact that this is the case makes scaling blockchain technology more challenging. All during the course of this investigation, an effort is made to analyse and classify the various scaling mechanisms that are now being used for blockchains. In addition to this, we are going to look at a variety of various approaches and present a list of possible solutions to the issue of scalability that the blockchain technology is now dealing. © 2024 IEEE.

关键词： Decentralized finance

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Optimized Decoupling Capacitor Estimation and Allocation for Supply Noise Reduction in SoC using Soft Computing and Machine Learning Techniques

Optimized Decoupling Capacitor Estimation and Allocation for...

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Devices for Integrated Circuit (DevIC)

作者： Rukhshar Khatun Partha Mitra Prama Naskar Angsuman Sarkar Department of Computer Science & Engineering Pailan College of Management and Technology Kolkata India Department of Civil Engineering Pailan College of Management and Technology Kolkata India Department of Electronics and Communication Engineering Klayani Government Engineering College Kalyani India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

In today’s VLSI design, the number of transistors has increased significantly. Implementing an efficient PDN is becoming a difficult issue with very high packing density designs. With ultra-high switching frequencies also contributes to variation of voltages in the electrical nodes, which contributes to supply noise. This noise voltage may result in malfunctioning of the integrated circuit. Therefore, reduction of supply noise is a major concern in system-on-chip. In this work PSN has been optimized using decoupling capacitors. The decap has been optimized using SOS and KNN algorithms. Finally, the integrated circuit has been portioned using pruning technique, floor planning using PSO algorithm and routing has been done using TSP algorithm. Simulation results show there has been overall improvement in the performance of the test circuits.

关键词： Symbiosis Machine learning algorithms Switching frequency Noise Capacitors Nearest neighbor methods Very large scale integration System-on-chip Transistors Physical design

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