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A Review on the Capability and Smart Contract Potential of Block chain Technology 3

A Review on the Capability and Smart Contract Potential of B...

引用

3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Singh, Devansh Malhotra, Mrs Vimmi Depatment of Computer Science and Engineering Dronachary College of Engineering Gurgaon India

ISBN: (纸本)9781665464871

There has been a lot of buzz about blockchain technology lately, thanks to the success of digital currencies like Bitcoin and Ethereum. All of these applications of blockchain technology have received a lot of attention. Bitcoin and Ethereum were able to process 7 and 15 TPS, respectively, when they were launched, whereas VISA and PayPal were able to process 1700 and 193 TPS. Scalability, which may be described as the capability to modify the block size to accommodate the rising traffic, is the most significant barrier to the widespread adoption of blockchain technology. This article aims to examine blockchain applications, including their applications, smart contracts, and the solutions to those problems. It is vital to scale blockchains while maintaining their fundamentally decentralised nature to appreciate these technologies' promise fully. The successful implementation of scalability solutions is necessary for several reasons, including the provision of services whose performance is on par with that of prevalent technologies, the support of innovative applications, and the maintenance of the workload produced by an expanding user base. In the future, this study will be extended by considering the options that contributors provide as an initiative towards resolving these issues. © 2023 IEEE.

关键词： Smart contract

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Novel Capsule Network Algorithm Based Image Classification with Efficiency

Novel Capsule Network Algorithm Based Image Classification w...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Adhitya Kannan, S.G. Uma Devi, S. IFET College of Engineering Depatment of Computer Science and Engineering Tamil Nadu Villupuram India

ISBN: (纸本)9798331540685

Despite the significant impact of convolutional neural networks (CNNs) across various domains, one of their limitations is the lack of consideration for spatial orientations. Capsule networks have the potential to address these issues by incorporating the storage and routing of pose information for the features they extract throughout their architectures. This involves seeking agreement between the lower-level feature extraction and the higher-level poses at each layer. Through the storage and routing of posture information across their architectures, capsule networks aim to resolve these issues. The fundamental components of the trainable segmentation engine include an encoder network, a corresponding decoder network, and a pixel-wise classification layer. The quantitative evaluations conducted demonstrate that Capsule Networks exhibit robust performance, competitive inference times, and superior memory efficiency compared to alternative architectures. The training and testing process utilizes datasets such as MNIST and CIAFR-10 datasets. The obtained results indicate that the proposed approach successfully outperforms CNNs in addressing image classification problems. © 2024 IEEE.

关键词： Image segmentation

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A Review on the Capability and Smart Contract Potential of Block chain Technology

A Review on the Capability and Smart Contract Potential of B...

引用

Smart Data Intelligence (ICSMDI), International Conference on

作者： Devansh Singh Mrs Vimmi Malhotra Depatment of Computer Science and Engineering Dronachary a College of Engineering Gurgaon India

关键词： Online banking Scalability Smart contracts Bitcoin Maintenance engineering Blockchains

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Intelligent Movie Recommender based on Emotions 3

Intelligent Movie Recommender based on Emotions

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Naik, Archana Ushashree, P. Bharadwaj, Isha Bhatnagar, Archita Potula, Divya Fouziya Nitte Meenskshi Institue of Technology Department of Computer Science and Engnieering Bangalore India Geethanjali College of Engineering and Technology Depatment of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350343632

Recommender systems are designed to help users navigate through a vast space of possible options and find the best ones that meet their needs and preferences. User state of mind and emotions are crucial for making an appropriate decision for personalization of products like music, news, and movies. In these cases, user emotions can strongly influence their overall experience and satisfaction, often more so than objective factors like plot, genre, or ratings. Recognizing the importance of user emotions, modern recommender systems have begun incorporating emotional analysis techniques into their algorithms. These techniques can help the system better understand and capture the emotional states of users, which in turn allows for more accurate and personalized recommendations. © 2023 IEEE.

关键词： Artificial neural network Convolution neural network Live Imaging Web scraping

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Wideband Tunable Transmission Type Polarization Converter Metasurface using Graphene for Terahertz Frequency Regime 4

Wideband Tunable Transmission Type Polarization Converter Me...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mistri, Hiranmay Ghosh, Anumoy Sardar, Abdur Rahaman Ramkrishna Mahato Government Engineering College Department of Electronics and Communication Engineering Purulia India National Institute of Technology Mizoram Department of Electronics and Communication Engineering Aizwal India Ramkrishna Mahato Government Engineering College Depatment of Computer Science and Engineering Purulia India

ISBN: (纸本)9798350352931

This article represents a broadband transmission type linear-to-circular polarization converter (LTCPC) realized using a graphene metasurface for the terahertz (THz) frequency regime. The composition of the unit cell includes a lossy-SiO2 substrate with a graphene-based sub-wavelength mono-layer structure on the top and a similar but very thin gold sub-wavelength structure at the back side to cover it partially. This metasurface can be used to transform waves with linear polarization into circularly polarized waves from 9.15 THz to 13.0 THz, i.e., about 3.85 THz bandwidth and about 35 percent of fractional bandwidth (FBW). The chemical potential (CP) and relaxation time (RT) can be varied to tune the graphene metasurface over a vast range of frequencies. All physical measurements of the unit cell geometry are optimized for performance optimization. The proposed lumped parameter circuit for the unit cell is also incorporated. The proposed unit cell can perform invariably up to 40° incident angle. © 2024 IEEE.

关键词： Terahertz waves

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Epidermis Bruise Analysis Using Deep Learning 6

Epidermis Bruise Analysis Using Deep Learning

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Redefining Disease Assessment in Raspberries: A Hybrid GAN-SVM Model for Detailed Intensity Measurement 4

Redefining Disease Assessment in Raspberries: A Hybrid GAN-S...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Arora, Dinesh Sharma, Deepika Sharma, Avinash Goyal, Parul Chandigarh Enigineering College Department of Electronics & Communications Engineering Landran Punjab Mohali India Chandigarh College of Technology Depatment of Biotechnolgy Landran Punjab Mohali India Chandigarh Enigineering College Department of Computer Science & Engineering Jhanjeri Punjab Mohali India M. M Engineering College Maharishi Markandeshwar Deemed to be University Computer Science & Engineering Department Mullana Haryana Ambala India

ISBN: (纸本)9798331529635

This study presents a hybrid Generative Adversarial Network (GAN) and Support Vector Machine (SVM) model for accurately categorizing Raspberry Cane Disease (RCD) into four severity levels. Leveraging a dataset of 7,000 high-resolution images, the model achieves 98.8% accuracy. GANs generate synthetic images to address data scarcity, while SVMs ensure precise multi-class classification. The results demonstrate the model's effectiveness in disease severity assessment, highlighting its potential for broader agricultural applications. © 2024 IEEE.

关键词： Generative adversarial networks

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Novel Capsule Network Algorithm Based Image Classification with Efficiency

Novel Capsule Network Algorithm Based Image Classification w...

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Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： Adhitya Kannan S.G S. Uma Devi Depatment of Computer Science and Engineering IFET College of Engineering Villupuram Tamil Nadu India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

关键词： Training Neural networks Memory management Signal processing algorithms Signal processing Feature extraction Routing

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Machine Learning for Cardiac Disease Detection and Family History Association 3

Machine Learning for Cardiac Disease Detection and Family Hi...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Srinivas, Inumarthi V. Saritha, G. Yadav, Renu Subburam, S. Divya, K. Kj Somaiya Institute of Management Data Science and Technology Depatment Mumbai India Sri Sairam Institute of Technology Computer Science India Ies College of Technology Department of Electronics & Communication Engineering Bhopal India New Prince Shri Bhavani College of Engineering and Technology India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350342338

As cardiac diseases continue to rise, early detection is crucial for effective treatment. Machine learning (ML) offers promising avenues for accurate diagnosis. This study explores the integration of family history, a vital risk factor, into ML-based cardiac disease detection models. By leveraging diverse patient datasets, ML algorithms are trained and validated. Incorporating family history enhances predictive accuracy. The literature review highlights the significance of genetics in disease predisposition. Model performance metrics demonstrate improved precision, recall, and overall accuracy. Interpretation of results reveals the interplay between genetics and ML. While data limitations and biases are acknowledged, the study presents implications for both clinical practice and research. This research contributes to the growing field of ML-driven medical diagnostics and underscores the importance of family history in cardiac disease prediction. © 2023 IEEE.

关键词： Clinical decision support disease prediction and machine learning genetic predisposition healthcare analytics healthcare applications patient datasets

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Potato Plant Leaf Disease Detection and Recognition Using R-CNN Model 3

Potato Plant Leaf Disease Detection and Recognition Using R-...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Balakrishnan, Minu Raja, M. Kumar, P.Vinoth Subramaniam, Suvitha Latha, R. Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Chennai Institute of Technology Depatment of Mechatronics Chennai India Kpr Institute of Engineering and Technology Department of English Coimbatore India Chennai India

ISBN: (纸本)9798350322842

A smart farming system that makes use of the appropriate infrastructure is an example of a new technology that aids in the improvement of the quality and quantity of the country's agricultural goods, such as potatoes. Diseases cannot be prevented since developing potato plants requires consideration of factors such as the environment, soil, and quantity of sunlight. Deep learning has resulted in recent advances in computer vision. These advancements have enabled Potato to utilize a camera to diagnose disease. This study developed an effective new method for detecting infections in potato plants. Yukon Gold, a kind of potato, served as the trial subject. Specifically, Alternaria, Blackleg, and Target Spot were targeted for detection using this method. Using 5,615 images of diseased and healthy Potato plant leaves taken in controlled environments, deep CNN is trained to identify between the presence and absence of three illnesses. To identify the Potato illnesses affecting the tracked plants, the system used a CNN. Anomaly detection using the F-RCNN-trained model achieved a confidence score of 80%, while illness identification using the Transfer Learning model achieved an accuracy of 97.85%. The effectiveness of the automated image-capturing system for diagnosing diseases in Potato plant leaves was determined to be 93.33%. © 2023 IEEE.

关键词： Convolutional neural networks

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