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Automated Multimodal Brain Tumor Classification Using a YOLOv7 Approach 9

Automated Multimodal Brain Tumor Classification Using a YOLO...

引用

9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Berlin Shaheema, S. Muppalaneni, Naresh Babu National Institute of Technology Department of Computer Science and Engineering Assam Silchar India

ISBN: (纸本)9798350311402

Automated brain tumor classification is one among the most complicated and popularly used applications of medical imaging. Manual diagnosis of brain tumors is complex and inefficient. Therefore, identifying the appropriate type of tumor at an early stage plays a significant role in choosing the exact treatment plan. Therefore, a new method based on the YOLOv7 architecture was suggested to extract features, select features, identify tumors, and classify them from multimodal brain images. The brain tumor is categorised using the classifier as glioma, meningioma, non-tumor and pituitary tumors to increase confidence in tumor detection problems. YOLOv7 method is assessed utilizing the brain MRI dataset which includes Br35H dataset, Figshare dataset, and SARTAJ dataset, consisting of 7023 MRI images. Overall effectiveness of the suggested technique is assessed in terms of precision, specificity, sensitivity, F1-score, and accuracy as 98.73%, 99.69%, 99.81 %, 98.56% and 98.73 % respectively. The classification outcomes demonstrates that described method can be useful to help clinicians make quick and accurate decisions related to the diagnosis of brain tumors. © 2023 IEEE.

关键词： Tumors

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Analyzing the Prediction Accuracy of Corn Leaf Diseases Using A Pre-Trained Network Model

Analyzing the Prediction Accuracy of Corn Leaf Diseases Usin...

引用

2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Ashwini, C. Sellam, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396485

During corn's exploration and manufacturing phases, farmers have a complicated issue in accurately diagnosing corn crop infections. To solve this issue, this work provides a method for specific position three prevalent diseases of corn leaves: grey spot, leaf blight, and rusty depending on grouping and an upgraded deep learning method. Deep learning advancements paved the path for enhancing prediction accuracy as its significance is adopted over various research fields. Disease prediction and classification is time-consuming due to the lesser foreground and background intensity information. To identify three illnesses, first cluster reference images using the clustering technique, then input them into the enhanced deep network. The influences of various 'k' values on corn diagnostic techniques are investigated in this research. The trial findings show that the approach has the most significant identification impact on observations with the analytical prediction of corn disease, rust, and grey spot disease. Here, VGG-16 and ResNet18 likewise produce the best diagnostic findings, with an average diagnostic accuracy, respectively. The technique presented in this study has a diagnostic performance of 95% for the three corn diseases. It has a more substantial diagnostic impact than another four techniques and can be used to safeguard crops in the agricultural area. The simulation is done in the MATLAB 2020a environment, and various performance metrics like accuracy, precision, F1-score, recall and some other statistical measures are evaluated and compared with existing approaches. © 2023 IEEE.

关键词： Forecasting

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An Optimized Hybrid Soil Texture Classification Model using Stacked Sparse Autoencoder 1

An Optimized Hybrid Soil Texture Classification Model using ...

引用

1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Prabavathi, R. Chelliah, Balika J Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

With effective soil management, which lowers the costs associated with soil erosion, improves water utilisation, and other factors, soil data analysis can offer vital information to raise agricultural yield and nutrient utilisation efficiency. An unsupervised method of data analysis called clustering finds similar groups of objects by analysing attribute values. The dynamic subject of soil classification starts with the model and moves on to explain the classes and, at the end, apply the knowledge gained from the field. In light of this, the study presents a novel clustering and classification-based hybrid soil texture classification model (HSTC). Soil data will be clustered and categorised into multiple classes by the proposed (HSTC) model. Hydrological soil groupings (HSG) of various kinds are used as classes to accomplish this. Based on characteristics like the proportion of clay, silt, and sand, the HGS can be categorised. The density-based clustering (DBSCAN) algorithm is used to group the soil texture data. The stacked sparse auto encoder (SSAE) model is used to categorise the clustered data. In addition, the parameter values of the SSAE technique are determined using a new barnacle mating optimizer (BMO). The HSTC model has achieved the best results, with accuracy of 95.66%. The experimental validation of the proposed HSTC model on soil texture dataset revealed that the suggested model outperformed previous techniques. © 2023 IEEE.

关键词： Deep learning

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Implementing a land registration system using non-fungible tokens to represent land in the system and side-chain for data storage 5

Implementing a land registration system using non-fungible t...

引用

5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Kunjumon, Cyril Mutharia, Aditya Shareef, Salemula Suchithra, M.S. Indian Institute of Information Technology Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350334128

Blockchain technology has been increasing in popularity in recent times due to its decentralized nature, immutability, transparency, high security and transparency. Several private and public organisations have been using it to develop new and innovative projects like cryptocurrency, NFTs, Metaverse, etc., and also to upgrade and improve old technologies like supply chains in agriculture and government voting systems. Land Registration is one such sector that can be upgraded using blockchain because in the current system there are a lot of middlemen involved and data is centralized and not secure, these things make the current system vulnerable to frauds and corruption. In this paper we have proposed a new implementation for the existing land registration system in the country that uses Non-Fungible tokens in the blockchain to represent a property and a sidechain like IPFS for storing its data in a decentralized manner. With our implementation we can overcome the flaws in the old system and make land registration secure, transparent and trustless. Here by "trustless"we mean that users do not have to trust any centralized authority or intermediary to verify transactions or maintain the integrity of the system. Instead, trust is placed in the system's consensus mechanism and cryptographic protocols. © 2023 IEEE.

关键词： Smart contract

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Queueing delay-based congestion control technique for seamless routing in WRSN 9

Queueing delay-based congestion control technique for seamle...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Siron Anita Susan, T. Nithya, B. Reji, Sneha Ann Avisha Rai, M. National Institute of Technology Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India

ISBN: (纸本)9798350314090

Congestion in Wireless Sensor Network (WSN) and Wireless Rechargeable Sensor Network(WRSN) can significantly impact network performance. It is essential to find a congestion control policy that minimizes congestion while maximizing throughput and bandwidth utilization. Existing solutions use either a loss-based approach which depends on timeout or duplicate acknowledgements, or a delay-based approach which depends on RTT. These solutions detect congestion only after it has occurred but does not prevent it. Thus, it is important to have a congestion policy that can detect or forecast an upcoming congestion scenario and try to prevent it. In this paper, an Queueing Delay-based Congestion Control Technique(QDCCT) is proposed that prevents congestion before it occurs and thus maximizes throughput and minimize overhead. The proposed technique takes into account both its own data flow and other background flows to identify congestion level. With the aid of run time metrics such as RTT, queueing delay variation and congestion window growth factor, the proposed algorithm prevents /resolves the congestion on the network thus allowing more packets to be transmitted with minimal delays and losses. The proposed TCP variant has been simulated and the solution has been evaluated and analyzed with the existing algorithms in terms of throughput, goodput, Packet Loss Ratio(PLR) and Packet Loss Ratio(PDR). © 2023 IEEE.

关键词： Wireless sensor networks

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Decentralized Data Storage Solutions using Hyperledger Fabric

Decentralized Data Storage Solutions using Hyperledger Fabri...

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2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Jadhav, Swati V. Patil, Sahil P. Patil, Siddhesh B. Patodia, Dirgh D. Pokharkar, Aryan Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798350312126

As geospatial data grows exponentially with the various advanced photographic capture technologies and satellite imagery being readily available and accessible, the need for secure and scalable storage systems grows larger. Blockchain-based distributed data storage and access systems can help provide an apt solution to this ever-looming issue. This paper talks about how Private Blockchain using IPFS (Interplanetary file System) and Linux Hyperledger technologies can be utilized for the Storage & Retrieval of Geospatial data. This paper explains the flow of the data from the point of generation to the point of retrieval from the Blockchain. The image is stored into IPFS Decentralized servers where we are provided with a CID (Content Identifier) for every unique entity. The CID is stored into the Hyperledger for the additional layer of blockchain-based security. For data retrieval, we simply enter the specific CID into the Hyperledger server through the authorized nodes with Keys to the private blockchain. This study aims to make the technology for the safe storage and reading of such data available. © 2023 IEEE.

关键词： Blockchain

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Credit Risk Prediction using Genetic Algorithm and Stacking Ensemble

Credit Risk Prediction using Genetic Algorithm and Stacking ...

引用

2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Barthe, Shashank Biswas, Saroj Kumar Sanyal, Saptarsi Purkayastha, Biswajit National Institute of Technology Department of Computer Science and Engineering Assam Silchar India

ISBN: (纸本)9798350338003

It is not an easy task to estimate the likelihood that a borrower will fulfill his or her duty to the lender (bank). There are several factors to take into account before deciding whether to extend any kind of loan. This decision-making process calls for sufficient quantitative data to compute, which takes time and is susceptible to error by humans. To finish this difficult and busy work, An Machine learning model for Credit Risk Prediction using Stacking Ensemble (ESCRPGSE) has been proposed as a solution to this time-consuming and difficult task. It combines oversampling technique, 'Gaussian Smote' to solve the imbalance problem, Genetic Algorithm to select useful features that are useful for model training, and a Stacking ensemble bagging technique for classification. By implementing 10-fold cross-validation, the proposed model's performance is evaluated in terms of accuracy and f1-score, and it is then contrasted with standard single-classifier classifier models, ensemble-based models, and various models that have been published in the literature. © 2023 IEEE.

关键词： Genetic algorithms

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Integrating AI and computer Vision for Effective Foreign Object Detection on Airport Runways 3

Integrating AI and Computer Vision for Effective Foreign Obj...

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

作者： Annie Silviya, S.H. Kingsley, S. Agitha, W. Sriman, B. Priyadarshan, V.J. Mohana, V. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India P.B. College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350322842

The FOD-R Dataset is a collection of images that depict common types of foreign object debris (FOD) that can be found on runways or taxiways. The dataset has primarily been annotated using bounding boxes to facilitate object detection. However, the FOD-xR Dataset includes an additional feature - color codes - that provide information on the ease of removing the debris. Lightweight objects are marked in green, while heavy objects are marked in red. This centralized system simplifies coordination efforts, making it easier for organizations to manage FOD removal effectively. This, in turn, can have a positive impact on an organization's bottom line by minimizing delays and damage caused by FOD incidents. Overall, the FOD-R and FOD-xR Datasets are valuable resources for organizations looking to improve their FOD detection and removal processes. © 2023 IEEE.

关键词： Object detection

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Summarization of Video Clips using Subtitles 4

Summarization of Video Clips using Subtitles

引用

4th International Conference on Communication Systems, Computing and IT Applications, CSCITA 2023

作者： Anil, Eleesa Sebastian, Sherine Johnson, Janice Rane, Janhavi Priya Karunakaran, K. Computer Engineering Department St. Francis Institute of Technology Mumbai India

ISBN: (纸本)9781665459877

Due to the ever growing world of high speed internet, videos have become a common medium for information on the web. When we want to gain information about anything from educational topics to entertainment we prefer watching videos instead of reading long paragraphs. With the vast diversity of videos available on the internet today on every single topic possible it gets confusing to find the right content for our needs. People end up wasting time on trying to find a good video instead of on the actual work the video is needed for. Video content being such a big part of our information source today it is necessary to have a system that will enable users to understand a gist of the video instead of having to sit through hours of content just to find nothing useful. The primary objective of this given paper is to propose a method to create a video summary in a way that it contains only the necessary and important information in a concise format by using various NLP algorithms such as Textrank, LexRank and LSA(Latent Semantic Analysis). © 2023 IEEE.

关键词： Natural language processing systems

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A Review of Machine Learning Techniques for Tuberculosis Meningitis Diagnosis 4th

A Review of Machine Learning Techniques for Tuberculosis Men...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Ramteke, Monali Raut, Shital Kadam, Tejal Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (纸本)9789819920990

TB has been considered to be a major global health hazard. With regard to early meningitis tuberculosis identification, numerous studies and research have been conducted in recent years. The most serious form of tuberculosis is tuberculous meningitis (TBM), although it can be challenging to distinguish TBM from other meningitis. Therefore, we reviewed papers related to machine learning models for identification and diagnosis of different forms of meningitis. In this review, the most recent research and methods for integrating medical data with machine learning models for diagnosis of meningitis and its respective outcomes are summarized. To achieve the best and most accurate results for detection and classification, authors have used a variety of strategies, beginning with preprocessing the data and partitioning the dataset into distinct training, validation, and test sets with varied ratios. This brings to attention that various machine learning tools and algorithms are useful in the analysis of medical data to diagnose meningitis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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