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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Dash, Adyasha Sarkar, Ansh Chatterjee, Ashutosh Darshana, Subhashree Pandey, Manjusha Barik, Rabindra K. School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350303360

The RSA cryptosystem encompasses some of the most common and popular encryption-decryption algorithms. In conjunction with other encryption schemes, RSA encryption is often used as a method to prove the authenticity and integrity of messages. Due to its less efficient and resource-intensive nature, it is generally, not used to encrypt entire multi-line messages or files. In order to crack an RSA encrypted asset, it is important for attackers to be able to factorize humongous primes;which, with the advent of quantum computing is slowly turning into a possibility thereby causing the factoring problem to lose its relevance. The purpose of this paper is to examine the cryptosystem for potential correlations that might exist between factors such as 'the number of primes used' and the algorithm's 'level of security'. Classical RSA involves the use of a pair of prime numbers for the process of key generation. In this paper, we aim to propose a generalized algorithm for generating RSA keys using N distinct prime numbers. Previously, papers have been published on the RSA algorithm subject to changing 'the number of primes' used to generate the RSA modulus. However, the aim of this paper is to generalize this approach while simultaneously illustrating the changes in the classical algorithm's properties over a spectrum of different values of the factors involved. © 2023 IEEE.

关键词： Cryptography

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Large Scale Ontology Matching System (LSMatch)

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Recent Advances in computer Science and Communications 2024年第2期17卷 20-30页

作者： Sharma, Abhisek Jain, Sarika Patel, Archana Department of Computer Applications National Institute of Technology Kurukshetra136119 India School of Law Forensic Justice and Policy Studies National Forensic Sciences University Gujarat Gandhinagar India

Background: Ontology matching provides a solution to the semantic heterogeneity problem by finding semantic relationships between entities of ontologies. Over the last two decades, there has been considerable development and improvement in the ontology matching paradigm. More than 50 ontology matching systems have been developed, and some of them are performing really well. However, the initial rate of improvement was measurably high, which now is slowing down. However, there still is room for improvement, which we as a community can work towards to achieve. Method: In this light, we have developed a Large Scale Ontology Matching System (LSMatch), which uses different matchers to find similarities between concepts of two ontologies. LSMatch mainly uses two modules for matching. These modules perform string similarity and synonyms matching on the concepts of the ontologies. Results: For the evaluation of LSMatch, we have tested it in Ontology Alignment Evaluation Initiative (OAEI) 2021. The performance results show that LSMatch can perform matching operations on large ontologies. LSMatch was evaluated on anatomy, disease and phenotype, conference, Knowledge graph, and Common Knowledge Graphs (KG) track. In all of these tracks, LSMatch’s performance was at par with other systems. Conclusion: Being LSMatch’s first participation, the system showed potential and has room for improvement. © 2024 Bentham Science Publishers.

关键词： Ontology

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Ozone Concentration Forecasting: Assessing the Efficacy of MLP, DNN, and XGBoost in Environmental Bench-AQ Dataset

Ozone Concentration Forecasting: Assessing the Efficacy of M...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Mohamed Abdul Kader Jailani, N. Mara, Geeta C. Reva University School of Computer Science & Applications Bangalore India Reva University School of Computing & Information Technology Bangalore India

ISBN: (纸本)9798350359688

This study investigates the prediction of tropospheric ozone concentration, a critical aspect of air quality assessment. We utilized machine learning techniques, including Multilayer Perceptron (MLP), Deep Neural Network (DNN), and XGBoost, and analyzed extensive air quality datasets that incorporated ozone-specific metrics and environmental variables. The findings revealed that XGBoost notably excelled over MLP and DNN in predicting average ozone concentrations and Aot40 metrics, as evidenced by its lower Mean Absolute Error (MAE) values. This research highlights the importance of precise feature selection in machine learning and demonstrates the potential of advanced ensemble methods like XGBoost in environmental modeling. It suggests the need for future exploration in integrating a broader range of environmental variables and applying these findings in real-world scenarios, especially in urban planning and public health. © 2024 IEEE.

关键词： Multilayer neural networks

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Blockchain in Healthcare System Using Hyperledger Fabric- a Comprehensive Review 14

Blockchain in Healthcare System Using Hyperledger Fabric- a ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shinzeer, C.K. Bhagat, Avinash Kushwaha, Ajay Shriram Lovely Professional University School of Computer Applications Phagwara India School of Computer Science and Information Technology Bengaluru India

ISBN: (纸本)9798350335095

A key component of the society structure is now medical healthcare. Understanding how to handle personal health data appropriately is becoming into an intriguing research issue because it is a vital and sensitive asset for each patient. In this overview, the solicitations of the Hyperledger Fabric blockchain with IoT devices are shown in order to limit access and create a chain of trust for the healthcare industry. Through encryption protocols, access controls, and cryptographic methods, the Hyperledger Fabric is designed to be safe against unauthorized access and use. In this study, a blockchain-based solution is defined following an assessment of the specifications for interoperability and solitude in the sharing of medical data. Considering its enterprise-grade data processing capabilities and other solitude protection features the Hyperledger Fabric architecture was evaluated. Our evaluation of the study's objectives and solutions is intended to help in the creation of an architecture or model that addresses the problems mentioned in the article. Another exciting subject that calls for more study is the solicitation of "blockchain and Internet of Things (IoT)"technologies in the healthcare sector. Network scalability is supposed to be enhanced by supporting low-end devices with patient-centric apps under smart contracts. © 2023 IEEE.

关键词： Smart contract

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Detection of Leading CNN Models for AI Image Accuracy and Efficiency

Detection of Leading CNN Models for AI Image Accuracy and Ef...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Nayim, Meshal Mohan, Vishnu Pandey, Trilok Nath Dash, Bibhuti Bhusan Dash, Bibek Bikram Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to be University School of Computer Applications Bhubaneswar India HealthWorksAI Bangalore India

ISBN: (纸本)9798350360660

This research explores the challenge of distinguishing between authentic and AI-generated images using advanced convolutional neural network (CNN) architectures. Leveraging the CIFAKE dataset, which includes a diverse array of AI-generated and real images, we evaluated the performance of three pre-trained models: ResNet50, EfficientNetB0, and DenseNet121. Our study involved training these models with images of 32x32 and resized 64x64 pixel resolutions to assess their classification accuracy, precision, recall, and computational efficiency. DenseNet121 emerged as the most effective model, achieving an accuracy of 98.49% on the test dataset, significantly outperforming the other models. This work highlights the potential of deep learning methodologies in addressing the growing need for reliable AI image detection, providing a robust foundation for future advancements and applications in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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A Deep Convolutional Neural Network Approach to Recognize Bangla Handwritten Digits 2

A Deep Convolutional Neural Network Approach to Recognize Ba...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Dalui, Abhraneel Sarkar, Rahul Sharma, Suvam Ghosh, Akash Brahnam, Sheryl Dash, Satya Ranjan School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India Missouri State University United States

ISBN: (纸本)9798350370188

The demand for sorted recognition of manually written digits is increasing as a result of digitization in standard of living. Manually typed digit confirmation is essential for a few operations in several sectors. There was a great demand for a large and fair-minded dataset, which is why Bengali Handwritten Digit Acknowledgment may be an undervalued field. But in this paper, We have used the large and unbiased dataset of Bengali digits that is Numta DB. The challenging part of Numta DB is that it is unprocessed and highly augmented. In this paper, We have surfed through some of the techniques to pre-process the data and have trained it in our custom designed Deep CNN model. We have achieved a accuracy of 99.6% on the training set and 98.65% on the validation set. We have also created a test dataset of our own consisting of 289 samples. We achieved a accuracy of 87.20% on our own dataset. © 2024 IEEE.

关键词： computer vision

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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An Empirical Study of Deep Learning Techniques for Diabetic Retinopathy Detection 3

An Empirical Study of Deep Learning Techniques for Diabetic ...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Pandey, Trilok Nath Pattnaik, Shyam Sankalp Sharma, Sanyam Ravalekar, Vishvajeet Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar School of Computer Science and Engineering VIT Chennai India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350303360

Presently, a competent professional must perform a thorough manual examination of digital color fundus photographs of the retina in order to diagnose diabetic retinopathy (DR). The delayed results result in missed evaluation, inaccurate communication, and postponed therapy by the time technicians complete their evaluations, which is often a day or two later. By looking for lesions connected to the vascular anomalies brought on by the disease, doctors can spot DR. This strategy works well, but it uses a lot of resources. Where DR detection is most important and there is a high prevalence of diabetes in the area, there is typically a lack of the requisite information and resources. As the number of persons with diabetes increases, the infrastructure required to prevent DR-related blindness will become increasingly more insufficient. In order to diagnose and classify diabetic retinopathy, this paper aims to develop and deploy image processing and ML algorithms to analyze fundus images. © 2023 IEEE.

关键词： Deep learning

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Image Forgery Detection Using Deep Learning 7th

Image Forgery Detection Using Deep Learning

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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Koshy, Litty Chithu, K. Department of Computer Science and Engineering SCMS School of Engineering & Technology Ernakulam India Master of Computer Applications SCMS School of Engineering & Technology Ernakulam India

ISBN: (纸本)9789819908370

Since cameras are so widely available, taking pictures has become more and more common. In order to gain more information, it is frequently necessary to enhance photographs, which are crucial in our daily lives as memories or sources of wealth of information. There are many tools accessible to enhance the quality of photos;however, some of them are also widely used to alter images, leading to the dissemination of false information. This makes picture forgeries more severe and frequent, which is now a major cause of concern. To identify fake images, many conventional methods have been developed over time. Convolutional neural networks (CNNs) have drawn a lot of interest recently, and they have had an impact on the area of picture fraud detection as well. However, the majority of CNN-based picture forgery detection methods currently used in the literature are restricted to identifying a certain kind of fraud (either image splicing or copymove). As a result, a method that can quickly and precisely identify any hidden forgeries in a picture is needed. We present a powerful deep learning-based approach for detecting picture forgeries in this study. The RGB format images converted to error level analysis (ELA) are used to train our model. The suggested model is compact, and its effectiveness shows that it outperforms cutting-edge methods in terms of speed. The experiment's findings are promising, with a 91.37% total validation accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Amalgamation of Transfer Learning and Explainable AI for Internet of Medical Things

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Recent Advances in computer Science and Communications 2024年第4期17卷 40-53页

作者： Murugan, Ramalingam Paliwal, Manish Patibandla, Rama Seetha Maha Lakshmi Shah, Pooja Balaga, Tarakeswara Rao Gurrammagari, Deepti Raj Singaravelu, Parvathavarthini Yenduri, Gokul Jhaveri, Rutvij School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India AP India Kallam Haranadhareddy Institute of Technology Andra Pradesh Guntur India Department of Computer Applications Kongu Engineering College Erode Perundurai India

The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized medicine, supports clinical decision making, and confirms the responsible handling of sensitive patient data. Therefore, this integration promises to revolutionize healthcare by merging the strengths of AI driven insights with the requirement for understandable, trustworthy, and adaptable systems in the IoMT ecosystem. © 2024 Bentham Science Publishers.

关键词： Knowledge management

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