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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rongali, Aruna sr. Saxena, Shweta Salma, Umi Alkhammash, Manal Mishra, Isa Aich, Shibani C Ph.D University of The Cumberlands Sr. Software Engineer Information Technology Emphasis/Information Technology United States University amity business school Amity university madhya pradesh Madhya pradesh gwalior India Jazan University Department of Computer Science Jazan Saudi Arabia KIIT Deemed to be University School of Management College KIIT School of Management Bhubaneswar India Amrita Vishwa Vidyapeetham Department of English Amritapuri India

ISBN: (纸本)9798350370249

This look investigates the utility of blockchain technology for reinforcing consideration in Wi-Fi networks. Networks are verbal exchange networks shaped on-the-fly, requiring no predefined infrastructure for dependable verbal exchange of facts. With the boom in IoT gadgets, wireless sensors, linked buses, and drones, the quantity of networks is anticipated to increase. The security and trustworthiness of networks rely on the trustworthiness of the nodes within the network, and this turns into a chief difficulty in ad-hoc networks. The proposed approach looks at the usage of the benefits of the blockchain era along with disbursed garages, clever contracts, and cryptography to enhance consideration in networks. The blockchain generation can validate the beginning and integrity of facts and assist an agreed/popular device, which would provide a straightforward manner for the nodes in an ad-hoc network to believe every difference. Wi-Fi ad-hoc networks are prone to malicious attacks because of the decentralized and allotted environment.. © 2024 IEEE.

关键词： Ad hoc networks

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Automated Classification of Medical Images Using Convolutional Neural Networks 15

Automated Classification of Medical Images Using Convolution...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pillai, N. Muthuvairavan Manimala, S. Rongali, Aruna sr. Gugnani, Anchit Kumar, Senthil A. sr.ramakrishnan, G.V. Computer Science and Business Systems R.M.D Engineering College Thiruvallur district Kavarapettai India Computer science and engineering University SJCE JSS Science and technology University Karnataka Mysore India Sr. Software Engineer Department Information Technology Emphasis/Information Technology Ph.D University The country University of The Cumberlands United States Physiotherapy Amity University Rajasthan Jaipur India Artificial Intelligent department Shri Vishnu Engineering College for Women AP Bhimavaram India Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort. ©2024 IEEE.

关键词： Convolutional neural networks

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (sr.) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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Enhancing Trust in Wireless Ad-Hoc Network Applications Using Blockchain technology

Enhancing Trust in Wireless Ad-Hoc Network Applications Usin...

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International Conference on Computing and Networking technology (ICCNT)

作者： Aruna sr. Rongali Shweta Saxena Umi Salma Manal Alkhammash Isa Mishra Shibani C Aich Sr. Software Engineer Information Technology Emphasis/Information Technology Ph.D University of The Cumberlands US University amity business school amity university madhya pradesh gwalior madhya pradesh india Department of Computer Science Jazan University Jazan Saudi Arabia KIIT Deemed to be University School of Management College KIIT School of Management Bhubaneswar Department of English Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

关键词： Wireless communication Wireless sensor networks Scalability Standardization Ad hoc networks Blockchains Sensors Communication system security Reliability Wireless fidelity

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Improving Multi-Organ Cancer Diagnosis through a Machine Learning Ensemble Approach 7

Improving Multi-Organ Cancer Diagnosis through a Machine Lea...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Khan, Mohammad Shahbaz Dixit, Rohit R Majumdar, Anurima Madhavi Koti, Valli Bhushan, Sushant Yadav, Vipul Operations and Regulatory Affairs Children's National Hospital Sr R & D DevOps Engineer WashingtonDC United States Senior Data Scientist Siemens Healthineers India Guru Nanak Institute of Technology Department of Electronics and Communication India GIET Degree College Department of Computer Science India Mewar University Department of Computer Science Chittorgarh India Software Developer Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350340600

Numerous methods for feature selection and classification have been developed, and the process of identifying cancer becomes challenging when the quantity of the information is enormous. The machine learning-based ensemble technique was created to address the weaknesses of the previous *** poor survival rates after diagnosis, cancer is a common cause of death that is reported in many industrialized and developing nations. The probability that cancer will be successfully treated rather than allowed to get worse increases with early identification and therapy. Identification of the afflicted regions and significant factors that influence cancer growth is the foundation for the majority of cancer diagnoses. There are several techniques for feature selection and classification, and when there is a large amount of data, finding cancer becomes difficult. The machine learning-based ensemble methodology was developed to solve the shortcomings of the earlier approach. This research has the potential to bring about significant advancements in healthcare by enhancing diagnostic accuracy, personalizing treatment, and improving patient outcomes while addressing ethical and fairness considerations. Its impact extends beyond the research domain, benefiting patients, healthcare systems, and the broader field of artificial intelligence in healthcare. © 2023 IEEE.

关键词： Feature Selection

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications

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Procedia Computer Science 2025年 252卷 231-240页

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Sr. Principal Systems Engineer in C&RS Platform services DT Amtrak USA University of south alabama Department of electrical engineering Masters in electrical engineering Senior Software Engineer in Cloud platform services City Of Philadelphia Pennsylvania USA Vice president Neuberger Berman US Software Development & Engineering in Content Discovery & AI Comcast USA Troy University Montgomery Campus AL. Department of computer Science Masters In Computer Science Department of Information Technology GEICO - Chevy Chase Chevy Chase MD 20815 United States

关键词： Resource Management Industrial Internet of Things Applications Complex Decision Making Adaptive Deep Reinforcement Learning Ship Rescue Optimization

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Development of Multi Objective Approach for Validation of PID Controller for Buck Converter

Development of Multi Objective Approach for Validation of PI...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Arun Kumar K Gopi Chand Vegineni C. Suresh Bhagath Chandra Chowdari Marella Sireesha Addanki Palak Chimwal Big Data Architect India Sr.Software Engineer Enrollment and Eligibility Team India Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Samayapuram Trichy Department of Financial Services Insights & Data Capgemini India Principal software developer System soft Technologies llc Information Technology India Department of Electrical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Buck converters are a common converter of DCDC used for high energy conversion and power sources. These regulators are utilized in electronic gear to alter the result voltage because of changes in the heap current and information voltage. Therefore, in order to react to enquiries in real time, an efficient control system is required. In this work, an upgraded Sine Cosine Algorithm (ISCA) with proportional-integral-derivative (PID) controller is proposed for Mult objective optimization of a DC-DC buck converter. Through two distinct modifications, the limitations of the traditional SCA were therefore addressed, leading to the innovative use of nonlinear equations in conjunction with instrumental mechanisms to update the average location. By contrasting its response for frequency, response for increment, uncertainties of parametric, statistics of time-space indispensable error execution, objective capability, and resistance from aggravation with the previous methods-based PID for buck converters of DC-DC, the suggested ISCA-PID technique's robustness was *** results obtained later revealed that the suggested approach outperformed its algorithmic predecessors in terms of the DC-DC buck converter's remarkably improved transient responsiveness.

关键词： Buck converters Regulators Machine learning algorithms Uncertainty Heuristic algorithms Velocity control Power system stability Real-time systems Voltage control Optimization

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An innovation analysis of LZ77 and LZ78 Compression Algorithms for Data Compression & Source Coding

An innovation analysis of LZ77 and LZ78 Compression Algorith...

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International Conference on Computing and Networking technology (ICCNT)

作者： Rakesh Kumar Giri Abhinav Mishra Aruna sr. Rongali Laxmi A. Bewoor R Raghavendra M. S. Kavitha Department of Computer Science & Engineering Sunrise University Alwar India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Information Technology Emphasis/Technology Software Engineer Ph.D University of The Cumberlands US Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA College of Computing Sciences And Information Technology Jain (deemed to be) University Bangalore Karnataka India Department of ECER. M. K. Engineering College RSM Nagar Kavaraipettai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper evaluates innovation on two of the most widely used compression algorithms — the LZ77 and the LZ78 algorithms — for information compression and supply coding programs. A detailed assessment of these algorithms presents insights approximately their design philosophy, technical traits, and performance metrics. Additionally, the studies offer a complete overview of their packages and implementations. The paper investigates the prominent considerations for implementing compression algorithms and analyzes their relative blessings and downsides. An exploration of the constraints of every algorithm is likewise undertaken. Furthermore, the research explores distinctive design elements of the algorithms, such as data structure, compression engine, and set of rules optimization. Eventually, the paper assesses the algorithms by reading the exchange-offs between their performance and complexity. This study facilitates practitioners and developers to apprehend the results of using those algorithms for diverse compression and supply coding applications. It can assist practitioners in identifying the first-class compression set of rules for their unique application needs. This paper gives an innovative evaluation of the LZ77 and LZ78 Compression Algorithms. These algorithms are algorithms designed for statistics compression and source coding. They’re used to take a given series of characters (or bits) and encode them right into an area-green representation. The innovation evaluation presents a valuable perception of how the algorithms improve compression ratios and shop area. It also discusses the effect of positive parameters such as window length, repeat cycle, and buffer size on compression efficiency. ultimately, the paper will speak to future studies’ challenges in this region and the capability packages of those algorithms.

关键词： Measurement Technological innovation Image coding Codes Philosophical considerations Source coding Green products Approximation algorithms Compression algorithms Optimization

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An Investigation Method Incorporates with Contextual information and Transformer Residual BiLSTM to Improve the Accuracy of Detecting Sensitive Content in Various Contexts

An Investigation Method Incorporates with Contextual Informa...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Department of Electrical Engineering Masters in Electrical Engineering Sr. Principal Systems Engineer in C&RS Platform Services DT Amtrak University of South Alabama USA Senior Software Engineer in Cloud Platform Services City Of Philadelphia Pennsylvania USA Vice President US AL. Department of Computer Science Masters In Computer Science Software Development & Engineering in Content Discovery & AI Comcast Troy University Montgomery Campus USA Department of Information Technology GEICO - Chevy Chase MD United States

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

In the modern era, the internet is an emerging technology with a rapidly growing user. Sensitive content uploaders consider this platform as a way to transmit disturbing visual content, such as animated cartoon videos to circulate unsuitable content among youngsters. Previous researches show that the interaction of online sensitive information has substantial offline effects on historically impoverished communities. As a result, it is strongly recommended that social media platforms have an automatic real-time sensitive content filtering method for detecting contextual information. In this proposed model, a novel deep-learning framework is developed for detecting sensitive content from contextual information. For this, the suggested architecture performs under three stages (i) text collection, (ii) preprocessing, and (iii) detection. Initially, the required texts from the contextual information are gathered from the standard available data resource. The collected text is subjected to text preprocessing, where the text is cleaned and prepared for further analysis. Finally, in the detection phase, the developed Transformer Residual Bidirectional Long Short-Term Memory (TR-BiLSTM) is employed to learn the effective representation from the preprocessed text and perform the sensitive content detection. Furthermore, performance comparisons are carried out between the proposed frameworks with other standard approaches. The developed architecture achieved better results in detecting the sensitive content from the text.

关键词： Visualization Accuracy Recurrent neural networks Noise Transformers Real-time systems Long short term memory Standards Context modeling Videos

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Improving Multi-Organ Cancer Diagnosis through a Machine Learning Ensemble Approach

Improving Multi-Organ Cancer Diagnosis through a Machine Lea...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Mohammad Shahbaz Khan Rohit R Dixit Anurima Majumdar Valli Madhavi Koti Sushant Bhushan Vipul Yadav Sr R & D DevOps Engineer Operations and regulatory affairs Children's National Hospital Washington DC USA Senior Data Scientist Siemens Healthineers Department of Electronics and Communication Guru Nanak Institute of Technology Department of Computer Science GIET Degree College Department of Computer Science Mewar University Chittorgarh Software Developer Vishwakarma Institute of Information Technology Pune

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