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Non-Invasive Oral Cancer Detection Using Hyperspectral Imaging and Advanced Spectral Unmixing Models

Non-Invasive Oral Cancer Detection Using Hyperspectral Imagi...

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Intelligent Computing and Emerging Communication Technologies (ICEC), International Conference on

作者： Valluri Ayyapa Kesava Sriram Kothamasu Priyusha Killaru Saadhivik Muddana Suresh Aala Vamsi Gutha Mukkara Prasana Kumar Sunil Chinnadurai Dept.of Electronics and Communication Engineering SRM University AP India Dept. of Computer Science and Engineering SRM University AP India Dept. of Computer Science SRM University AP India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

Oral cancer is a significant global health concern, often leading to high mortality rates due to late-stage diagnosis and the lack of effective early detection methods. Despite advances in medical science, the absence of reliable early diagnostic tools remains a critical challenge. Hyperspectral imaging (HSI) has emerged as a powerful noninvasive technology, capturing detailed spectral information across a wide range of wavelengths. This allows for accurate differentiation between cancerous and healthy tissues, improving early detection and enhancing treatment outcomes. In this study, we propose the use of HSI for early oral cancer diagnosis. To address the scarcity of labeled data, we developed a synthetic hyperspectral dataset that includes spectral signatures of both normal and cancerous tissues. The dataset was generated using a bilinear mixing model, with key spectral features extracted through Vertex Component Analysis (VCA) and abundances computed using Non-Negative Least Squares (NNLS). The model's performance was evaluated using Spectral angle distance (SAD) and Root mean square error (RMSE) metrics. Our findings demonstrate that HSI significantly improves the accuracy of early oral cancer detection, outperforming traditional methods. This work highlights the potential of advanced imaging technologies in revolutionizing cancer diagnosis, offering a robust framework for non-invasive detection and showcasing the effectiveness of synthetic datasets in medical imaging research.

关键词： Measurement Accuracy Computational modeling Mortality Feature extraction Reliability Root mean square Cancer Hyperspectral imaging Synthetic data

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Bidirectional Laser Based OCDMA-VLC System for Smart Home Applications 6

Bidirectional Laser Based OCDMA-VLC System for Smart Home Ap...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arya, Vivek Kumari, Meet Chauhan, Rahul Sharma, Neha Dept. of ECE FET Haridwar India Chandigarh University Dept.of ECE Mohali Gharuan India Graphic Era Hill University Computer Science and Engineering Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350304480

A bidirectional symmetric 3 x35Gbps optical code division multiple access (OCDMA) with visible light communication (VLC) system is presented. The performance is measured using multi-diagonal (MD) code for different users with channel impairments. The simulation results indicate that faithful VLC range of 45m is achieved successfully. Also, the system employing MD code can supports 80 numbers of end users in both downlink as well as upstream transmission. Also, the system shows that best performance over other existing ones for smart home applications. © 2023 IEEE.

关键词： multi-diagonal (MD) optical code division multiple access (OCDMA) smart home visible light communication (VLC)

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A Computational Analysis of Optimization Techniques to Solve Assignment Problem

A Computational Analysis of Optimization Techniques to Solve...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Yash Kuma Ramesh Ch. Sahoo Prashant Dixit Dept.of Computer Science & Engineering Manav Rachna International Institute of Research and Studies Faridabad Haryana India Dept. of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

In the field of combinatorial optimization, numerous methods have been developed to address complex problems, including assignment, scheduling, and resource allocation. This paper presents a comparative analysis of seven prominent optimization techniques: Hungarian Algorithm, Greedy Algorithm, Auction Algorithm, Linear Programming, Genetic Algorithm, Simulated Annealing, and Constraint Programming. We evaluate these methods on computational efficiency, scalability, and practical applicability, focusing on assignment problems. The analysis reveals that Hungarian Algorithm, Linear Programming, and Constraint Programming consistently deliver high accuracy across all matrix sizes, making them ideal for scenarios where solution precision is paramount. Conversely, the Greedy Algorithm excels in speed on smaller matrices but suffers from significant time and accuracy issues as matrix size increases. Additionally, while both Constraint Programming and Genetic Algorithm achieve high accuracy, their execution times rise considerably with matrix size, especially for Genetic Algorithm, which becomes impractical for large-scale problems. Thus, Hungarian Algorithm, Linear Programming, and Constraint Programming are optimal for accuracy-focused tasks, while Greedy Algorithm is preferable for smaller, speed-critical cases.

关键词： Constraint handling Greedy algorithms Accuracy Processor scheduling Scalability Simulated annealing Linear programming Resource management Optimization Genetic algorithms

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Preprocessing, Feature Extraction, and Classification Methodologies on Diabetic Retinopathy Using Fundus Images

Preprocessing, Feature Extraction, and Classification Method...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Veerendra Basreddy Sachinkumar Veerashetty Dept.of Computer Science and Engineering Faculty of Engineering and Technology (Coed) Sharnbasva University Kalaburagi India

Significant progress has been made throughout the detection of Diabetic Retinopathy (DR) over the past few decades because of the utilization of Deep-Learning (DL) techniques. Accurate and economical identification of DR using Fundus Images (FI) is made possible by combining state-of-the-art methods for image-processing, feature-extraction, and classification algorithms. This survey aims to explore and analyze the latest developments in preprocessing, feature extraction, and classification approaches, highlighting their key ideas and contributions. Novel approaches to feature extraction, involving traditional handcrafted features and DL-based methods, are investigated to capture relevant characteristics of DR lesions effectively. Furthermore, the survey explores diverse classification algorithms, including Support-Vector-Machines (SVM), Convolutional-Neural-Networks (CNNs), and ensemble methods, which enable accurate categorization of DR severity levels. This survey provides valuable insights into the state-of-the-art methodologies for DR detection, facilitating a comprehensive understanding of the key approaches and innovations in preprocessing, feature extraction, and classification techniques.

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Comparison of Novel Machine Learning Algorithms for Predicting Chronic Renal Disease

Comparison of Novel Machine Learning Algorithms for Predicti...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Hima Bindu, G.B. Valluru, Dinesh Vachhani, Harshita Sunil Kumar, M. Ganesh, D. Neelima, P. The Apollo University School of Technology Department of of CSE Andhra Pradesh Chittoor India MLRITM Engineering College Department of IT Telangana Hyderabad India Pratibha College of Commerce and Computer Studies Chinchwad Dept.of Computer Science Maharshtra Pune India School of Computing Andhra Pradesh Tirupati India SPMVV School of Engineering & Technology Department of CSE Tiruapti India

ISBN: (纸本)9798350381887

Chronic Renal Disease (CRD) is a serious sickness that affects the entire world and is the primary cause of mortality for many other illnesses. Due to the lack of visible symptoms at the beginning of CRD, people usually neglect the illness. Early CRD diagnosis enables individuals to receive prompt therapy to slow the course of their illness. Because it quickly and accurately identifies potentials, model-based machine learning can help doctors accomplish this aim. In this paper, an ML strategy for CRD forecasting. The UCI machine learning repository provided the CRD dataset, which has a high number of uncertain data values. To address unknown values or unknown data, KNN estimation was employed, which picks multiple instances that contain the most comparable measures to manage the undetermined data value for every insufficient sample. Missing data is prevalent in real therapy situations, therefore individuals may skip particular measurements for a variety of reasons. The technique is generated using five machine learning algorithms (SVM, Logistic Regression, k-nearest neighbor, Random Forest, and Neural Network). Random forest outscored the other models of machine learning regarding accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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APOA based Multi-scale Parallel Convolution Blocks with Hybrid Deep Learning for Gastric Cancer Prediction from Endoscopic Images 1

APOA based Multi-scale Parallel Convolution Blocks with Hybr...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Padthe, Adithya Mathapati, Mahantesh Priyanka, M.S. Nandihal, Priya University of the Cumberlands Department of Information Technology United States Dept.of Cse India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350316469

In terms of mortality rates, gastric cancer is second only to lung cancer. Manual gastric slice pathology examination is labor-intensive and prone to observer bias. Endoscopy of the upper digestive tract is commonly used for the screening of gastric cancer. An object identification model, a kind of deep learning, was presented as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. However, difficulties were encountered while attempting to reduce the sum of false positives in the identified findings. Tumour segmentation from the preprocessed pictures was carried out in this study, which is often more challenging and crucial. The research suggests a productive approach that makes use of multi-scale parallel convolution blocks (MPCs). Multi-scale parallel convolutions (MPCs) use filters of variable sizes to extract characteristics that are relevant across a range of tumour sizes. To further aid feature extraction with fewer parameters, residual connections and residual blocks can be used. In addition, the suggested study uses an Artificial plant optimisation algorithm (APOA) to fine-tune the segmentation model's parameters without resorting to post-processing approaches. Finally, gastric cancer detection from endoscopic pictures is accomplished using a hybrid classification strategy that incorporates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Experiments employing 1208 photos from healthy people and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross-validation approach. These findings show promise for the suggested method's application in automated early stomach cancer diagnosis using endoscopic images. © 2023 IEEE.

关键词： Artificial plant optimization algorithm Convolutional Neural Network Endoscopic images Gastric cancer Multi-scale parallel convolution blocks Recurrent Neural Network

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Bi-Directional Gated Recurrent Unit Approach for Detecting and Classifying High Impedance Fault in Power Distribution 3

Bi-Directional Gated Recurrent Unit Approach for Detecting a...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Irfan, Md. Mujahid Supriya, R. Reddy, P. Malleswara Alabdeli, Haider Gurumoorthy, Sasikumar Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Dept.of Ise Bengaluru India Department Of Eee Ananthapuramu Gooty India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Engineering And Technology Dhanalakshmi Srinivasan University Computer Science And Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350318609

In the recent days, detecting the High Impedance Faults (HIF) is an extremely difficult task because it is unpredictable, asymmetric, and nonlinear. Due to the size of the fault current is typically much lower than the normal load current, these faults are typically undetectable and impossible to isolate using traditional over-current approaches. Therefore, this research introduced a novel technique name called Bi-directional Gated Recurrent Unit (Bi-GRU) for power distribution-related HIF detection and isolation. The devised technique makes use of voltage as well as data from current sensors. In order to clarify the procedure, many types of algorithms were utilized during the detecting phase. For classification purposes, the algorithm Bi-GRU is applied on IEEE 13- distribution of node networks to test this method's ability to recognize and detect HIF with both damaged and unbroken wires. The suggested Bi-GRU provides fast and flawless output when troubleshooting high impedance issues. Bi-GRU considerably increases input processing and enhances classification accuracy. From the overall analysis, suggested Bi-GRU combines the forward and reverse GRUs in reverse order, which allows it to complete the operation more quickly and accurately than existing Morphological Fault Detector by achieving 95.63 % accuracy rate. © 2024 IEEE.

关键词： Principal component analysis

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Fog Computing based Privacy Preservation via Distributed Access Control System in Blockchain Technology 1

Fog Computing based Privacy Preservation via Distributed Acc...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Thatikonda, Ramya Nandihal, Priya Priyanka, M.S. Mathapati, Mahantesh PhD in Information Technology University of the Cumberlands Williamsburg United States Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dept.of Cse Bengaluru India

ISBN: (纸本)9798350316469

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices. © 2023 IEEE.

关键词： Blockchain Data Privacy Fog computing Homomorphic encryption Internet of Things Malicious Tampering

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A Multistep Ahead Predictive Analysis of Crude Oil 2

A Multistep Ahead Predictive Analysis of Crude Oil

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Mohapatra, Srikanta Kumar Singh, Kamakhya Narayan Sarangi, Prakash Kumar Sahu, Premananda Mohanty, Jayashree Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School Of Computer Application KIIT Deemed University Odisha India Hyderabad India School of Computer science and Engineering Lovely professional University Punjab India Chandigarh University Dept.of AIT-CSE Punjab Chandigarh Mohali India

ISBN: (纸本)9798350333763

The population is increasing in a rapid manner and the number of vehicles is increasing in the same ratio. Also, crude oils are very useful towards the energy needs in the industries. As a result, a day will come when we face the issue of feeling the vehicles with the petrol to run the vehicle and to run the industries. So, the solution is to find out the period up to which we can use the crude oil which is currently present in the world. To get a conclusion the most populated countries India and China is taken as reference. For simulation purpose time series based Artificial Neural Network application along with Facebook Prophet is used. Facebook Prophet is used to predict future situations in time series data related to oil production and consumption in two top countries by population, China, and India, over a period of 32 years. NeuralProphet is used for modelling time-series based on neural networks forecasting the near-term oil crisis. The dataset used in this study contains three characteristics, namely population, oil production, and consumption, and is obtained from different government sources. The best part of this model is that it can predict multi step ahead of given current data as required. The method based on NeuralProphet is best suit for the time series prediction for short period or long period. It can be used in multistep ahead prediction methods. It is predicted that by 2050 the scarcity of the petroleum is most. So, the solution is either to reduce the uses of vehicles, control the population or simply go for an alternate fuel like hydrogen could be considered a suitable substitute for oil. This prediction highlights the need for sustainable energy sources to meet the energy demand in the future. In conclusion, this paper presents NeuralProphet approach for forecasting future oil consumption and production trends combining the Facebook Prophet and NeuralProphet method. © 2023 IEEE.

关键词： Time series

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FPGA based Smart and Sustainable Agriculture

FPGA based Smart and Sustainable Agriculture

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International Conference on VLSI Design

作者： Shyam Peraka SK Irfan Ali Durga Vasu Mogili Ashok Kumar Palivela Sudheer Reddy Jyotsna Bavisetti Dhanush Reddy Y Dept. of Electronics and Communication Engineering RGUKT-Nuzvid India Dept.of Computer Science and Engineering RGUKT-Nuzvid India

The agriculture sector is one of the key areas of concern interlinked with most of the Sustainable Development Goals (SDGs), as stated by most organizations. However, technological advancements and agricultural intervention, particularly in developing and underdeveloped countries, are still in their early stages. IoT, cloud computing, data analysis, and other technologies are being widely used in most areas, where they are bringing significant changes in outcomes compared to previous conventional practices. It is also observed that there have been some initiations in bringing such technologies into agriculture to assist farmers, but they are not comprehensive and real-time working prototypes. Hence, we have come up with a novel, comprehensive solution to address different issues. The focus of the proposed paper is to develop and design a real-time prototype model implementing the following: 1. Control of the irrigation system based on crop stage requirements and other factors such as soil nature, humidity levels, rainfall levels, and so on. 2. Detection of weeds and disease using image processing and machine learning algorithms 3. Identification and recommendation of suitable crops based upon the nature of the soil, fertility levels, climatic conditions, water availability, etc. 4. Voice-based responsive system-mobile application or web-based application. The above tasks were successfully implemented and tested on the prototype developed with the help of the DE10 Nano Cyclone FPGA board. The sensors used in the project include the NPK sensor, moisture sensor, pH sensor, electric conductivity sensor, and water level sensor. Azure cloud services are used for the deployment of the website. The farmer is able to get the voice-based response through a mobile application developed for this purpose.

关键词： Cloud computing Irrigation Crops Prototypes Soil Real-time systems Cyclones

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