2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)最新文献

{"title":"The Role of 5G Networks in Energy-Efficient Smart Cities Development using IoT","authors":"Chinnaraj Govindasamy, T. Dayana, Vasu V, A. Tamilarasi, R. Prakash, R. Kalaivani","doi":"10.1109/ICAAIC56838.2023.10140765","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140765","url":null,"abstract":"In theory, residents in “smart cities” would have access to a wide range of amenities designed to enhance their daily lives by making better use of public resources and providing a more satisfying experience overall. The efficiency of these services depends on the information sharing across disparate systems serving similar functions. Large amounts of data with a variety of sophisticated, application-specific needs will likely be sent throughout the information exchange process. By better management of public resources and a focus on resident comfort, infrastructure upkeep, and environmental sustainability, “smart cities,” which rely on ICT, aim to raise the bar on service quality. Fifth-generation (5G) wireless mobile communication paves the way for a new kind of communication network that can connect everyone and everything. Impacting economies and communities, 5G will offer the connectivity infrastructure required by many smart city applications. A glimpse into the future of urban areas may be seen in the rise of “smart cities” and the IoT. The integration of many systems aimed to monitor varied components of the smart city may be utilized to generate a more sustainable and securer city. This research study presents a construction that mixes the data from diverse systems. In order to lessen the quantity of produced traffic in the 5G network and subsequently the energy consumption, suggest the usage of data aggregation in each antenna. A summary of 5G communication networks and different 5G technologies utilized in smart cities to promote sustainability is provided. The sustainability metrics for 5G networks are then examined across the environmental, social policy, and economic aspects, as well as sub-dimensions such as energy efficiency, energy usage, carbon footprint, contamination, cost, health, safety, and security. The findings indicate that while in an effort to address sustainability in 5G technology and intelligent buildings, the bulk of research publications concentrate on the environmental components of sustainability.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127442760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Plant Leaves having Anti-Diabetic Property using Machine Learning","authors":"Amrita Verma Pargaien, Devendra Singh, M. Chauhan, Hansi Negi, Bhawana Chilwal, N. Pargaien","doi":"10.1109/ICAAIC56838.2023.10140394","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140394","url":null,"abstract":"About 1200 medicinal plants have been used in Ayurveda, Unani and Chinese medicines for the management of diabetes. Diabetes is an endocrine disorder where glucose levels rise in blood due to lack of insulin production by pancreas. Identification and detection of these plants manually can be extremely tedious and time consuming thus; using machine learning is more beneficial and promising. Due to improved capacity of machine learning to acquire, manage, and store extremely vast volumes of data, machine learning is being trained to be applied to identify the plants, their phenotype using images of plants and their disease. This research study has proposed the application of machine learning for the identification of leaves of plants possessing anti-diabetic property. Here, the machine learning algorithms are applied for the detection of leaves of some anti-diabetic plants namely Basella alba, Moringa oleifera, Fenugreek, Psidium guajava, Hibiscus rosa sinesis. In the proposed experiment, the highest accuracy of about 99.4% was achieved by using a combination of Neural Network and Logistic Regression. The proposed model effectively classifies the plant images with high accuracy.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127449036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Stroke Disease using Convolutional Neural Network and Multimodal Biosignals","authors":"Mallikharjuna Rao, Pasumarthi Sowmya Sree, Jaya Rama Krishna Prasad Narravula, Renuka Devi Rompicharla, Harsha Vardhan Nukala","doi":"10.1109/ICAAIC56838.2023.10140205","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140205","url":null,"abstract":"The high mortality and disability rates associated with strokes highlight the need of early diagnosis and preventative measures. Both ischemic and hemorrhagic forms of these illnesses need urgent care, although the two types differ in their specifics. In order to get expert medical help within the optimal treatment window, it is essential to recognize precursor symptoms as soon as possible. Nevertheless, prior research has mostly concerned itself with post-onset therapy rather than the identification of predictive signs. In this approach, a method for real-time stroke illness prediction using Convolutional Neural Network and multi-modal bio-signals are used The CNN-LSTM model demonstrates a satisfying accuracy of 99.15% utilizing raw data, and the system takes into account the convenience of senior patients to obtain high prediction accuracy. The suggested approach has the ability to identify and prevent strokes earlier than current imaging methods allow.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126626626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Analysis of Activation Function, Evaluating their Accuracy and Efficiency when Applied to Miscellaneous Datasets","authors":"A. Tomar, Animesh Sharma, Aditya Shrivastava, Anurag Rana, Pradeep Yadav","doi":"10.1109/ICAAIC56838.2023.10140823","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140823","url":null,"abstract":"Numerous deep learning architectures have been developed as a result of activation functions (AFs), which are crucial for allowing deep neural networks to deal with intricate real-world problems. In order to achieve cutting-edge performance, AFs play a crucial role by facilitating diverse computations between the hidden and output layers. This paper presents a comparison between various activation function like sigmoid, tanh, ReLU, Softmax on thedatasetMNIST, CIFAR-10 and IRIS and their accuracy on these datasets with minimum errors. These observations offer valuable insights into determining the most suitable activation function for diverse scenarios and datasets, thereby providing a comprehensive understanding of the optimal activation function for distinct situations.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122007281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of the ART Tomographic Reconstruction Algorithm - Monte Carlo Simulation","authors":"S. Raviraja, K. Seethalakshmi, Sumit Kushwaha, Vishnu Priya P M, K. R. Kumar, Bijesh Dhyani, V. Prabhu","doi":"10.1109/ICAAIC56838.2023.10141047","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141047","url":null,"abstract":"This research study discusses about the algebraic reconstruction technique (ART) for performing 3D object reconstruction and two other variants: the use of positivity constraints for reconstructed values and the extra use of transversal projections. This study has used Monte Carlo techniques for 3D objects to mimic illusions and projections using Poisson noise. For a variety of practical signal-to-noise ratios, the modulation parameter was optimized. In contrast to the reuse of transverse projection lines, this study has determined the usage of positivity restriction in the ART technique. If computing time is considered as a significant factor, these enhancements can be compromised in terms of processing speed.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126693924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chatease: A Blockchain based Chat Application","authors":"Akhil Kuchimanchi, Maddineni Vagdevi, M. Reddy, Ganesh Avugaddi, Sanjay Kumar","doi":"10.1109/ICAAIC56838.2023.10140347","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140347","url":null,"abstract":"Security problems have traditionally arisen when messages are exchanged through unsecured channels. Despite the fact that there are various strategies for encrypting messages, attempts on gathering Information continues to be sent. Furthermore, the fact that the vast majority of applications store their data in centralized databases is grounds for worry. Blockchain technology offers new ways to ensure data privacy, censorship resistance, immutability, and decentralization. Each node in the network receives a copy of the data sent directly into the Blockchain by users. Using their private key, only approved persons may access the data on the blockchain. It eliminates the need for trustworthy intermediates. Because the system is totally decentralized, users may send messages with confidence. This software shows how to hash messages and mine them in the blockchain. To provide decentralized storage and quick lookup, we'll use a distributed hash table (DHT) and blockchain. This paper describes how to build a decentralized chat program.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124281012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nutrient Deficiency Detection in Mobile Captured Guava Plants using Light Weight Deep Convolutional Neural Networks","authors":"Sona Haris, K. S. sai, N. Rani, P. B. R.","doi":"10.1109/ICAAIC56838.2023.10141055","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141055","url":null,"abstract":"Nutrition deficiency in plants is a major problem that affects their growth, yield, and nutritional value. Over the past few years, there has been a significant growth in the application of machine learning and computer vision techniques, in early detection and classification of plant disorders. This study, proposes a deep learning-based approach for detecting nutritional deficiencies in guava leaf images. A dataset of guava leaf images captured using mobile devices, containing various nutritional deficiencies including magnesium and phosphorous, was acquired for training the model. A pre-trained deep CNN model is employed to extract convolved features and detect the affected regions, categorizing them as nutritional deficient or non-nutritional deficient Experimental results show that the proposed method achieved an accuracy of 87% in detecting nutritional deficiencies in guava leaf images. These outcomes demonstrate that the proposed approach provides a reliable and accurate method for early detection of nutritional deficiencies in guava leaves. This approach has the potential to be deployed in the agricultural domain for the effective diagnosis of plant nutrient deficiencies, ultimately increasing crop productivity and nutritional quality.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"22 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124450291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Handwriting Text Recognition using CNN and RNN","authors":"R. Sumathy, S. Swami, T. P. Kumar, V. L. Narasimha, B. Premalatha","doi":"10.1109/ICAAIC56838.2023.10140449","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140449","url":null,"abstract":"In the field of optical character recognition, there are still unans wered research questions regarding the recognition of handwritten text. In this paper, an effective method for developing handwritten handbook recognition systems is proposed This article uses a 3-subcaste Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in conjunction with a supervised literacy technique. Although bit chart descriptions of the input samples boost the delicateness of any textbook recognition system, they are used as point vectors in the s ys tern. The objective vectors are pre-processed before the resulting goal variables according to input samples are applied to the CNN. Using samples of each digit in the number 123, the CNN&RNN training procedure is carried out to verify the system's general connection to new inputs. Two different algorithms for literacy are utilized in this study. Cumulative image processing techniques have also been developed to deal with the several characters that are provided in a single image, cocked image, and rotated image. The trained systern provides a better delicacy.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123623082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Traffic Management System using YOLOv4 and MobileNetV2 Convolutional Neural Network Architecture","authors":"Varun Chava, Sri Siddhardha Nalluri, Sri Harsha Vinay Kommuri, Arvind Vishnubhatla","doi":"10.1109/ICAAIC56838.2023.10141268","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141268","url":null,"abstract":"Congestion owing to traffic is one of the crucial complications in urban cities, which is need to be addressed to improve traffic control and operation. The present traffic system is a timer-based system that operates irrespective of the amount of traffic and the existence of emergency vehicles like ambulances and fire engines. Vehicle flow discovery appears to be an important part of modern world traffic control and operation system. This design proposes a novel smart traffic system that utilizes real-time Average Vehicle Area and Emergency vehicle detection to optimize traffic flow and improve emergency response times. This system employs YOLOv4 and MobileNet V2 Convolutional neural network pre-trained model to accurately detect the number of vehicles present on the road, Average Vehicle Area and identify emergency vehicles in real-time. Using this information, this system can dynamically adjust traffic signals and reroute vehicles to minimize congestion and ensure priority access for emergency vehicles. Experimental results show that this system significantly reduces average travel times and emergency response times, making it a promising solution for modern traffic management and emergency services.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121912773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Damaged Image Repair using Masks with Computer Vision Inpaint Method","authors":"D. Ramakrishna, G. S. M. Emmanuel, Mercy Paul Selvan","doi":"10.1109/ICAAIC56838.2023.10141229","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141229","url":null,"abstract":"Image inpainting is the technique used to automatically fix damaged areas using data from sections that have been saved. With the development of deep learning in recent years, image drawing performance has substantially increased. This research study reviews the main methods used for automating image inpainting. This research study provides a brief overview of traditional techniques while concentrating on deep learning-based inpainting techniques, covering model categorization, strengths and drawbacks, scope of application, and performance comparison. Finally, the challenges and trends surrounding automated image inpainting are examined and foreseen. A tool called image inpainting uses the data from the remaining components to repair damaged areas. With the advancement of society, image inpainting has become a vital research area in the field of computer vision. It is extensively used in culture, daily life, and security, including object removal and the preservation of digital cultural assets. Conventional methods build geometric models based on geometric consistency and image content similarity, or they use texture generation to patch up small sections of damaged images. It partially solves the problem of loose coupling between high-level semantics and low-level image properties, enabling deep learning to gradually overtake traditional methods in computer vision.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122792741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}