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A Secure IoT-Cloud Based Healthcare System for disease Classification Using Neural Network

computer Systems Science & engineering 2022年第4期41卷 95-108页

作者： M.Vedaraj P.Ezhumalai Department of Computer Science and Engineering R.M.D.Engineering CollegeKavaraipettai601206TamilnaduIndia

The integration of the Internet of Things(IoT)and cloud computing is the most popular growing technology in the IT *** integrated cloud com-puting technology can be used in smart cities,health care,smart homes,environ-mental monitoring,*** recent days,IoT integrated cloud can be used in the health care system for remote patient care,emergency care,disease prediction,pharmacy management,***,still,security of patient data and disease predic-tion accuracy is a major *** machine learning approaches were used for effective early disease ***,machine learning takes more time and less performance while classiﬁ*** this research work,the Attribute based Searchable Honey Encryption with Functional Neural Network(ABSHE-FNN)framework is proposed to analyze the disease and provide stronger security in IoT-cloud healthcare *** this work,the Cardiovascular disease and Pima Indians diabetes dataset are used for heart and diabetic disease classiﬁ***-ally,means-mode normalization removes the noise and normalizes the IoT data,which helps to enhance the quality of ***ﬁed linear Unit(RlU)was applied to adjust the feature weight to reduce the training cost and error classiﬁ-*** proposed ABSHE-FNN technique provides better security and achieves 92.79%disease classiﬁcation accuracy compared to existing techniques.

关键词： Honey encryption functional neural network rectiﬁed linear unit feature selection classiﬁcation

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Adaptive Marine Predator Optimization Algorithm(AOMA)-deep Supervised learning Classification(dSlC)based IdS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion detection System(IdS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IdS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the deep Supervise learning Classification(dSlC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-dSlC based IdS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion detection System(IdS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) deep Supervise learning Classification(dSlC)

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A Comparative Analysis of denoising PCG Signals using Adaptive Wiener-Kalman Filtering 8

A Comparative Analysis of Denoising PCG Signals using Adapti...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Pandiaraj, A. Ramshankar, N. Gnanasekar, A. Arunchandran, R. Azariah John, B.S. Febin, C. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India R.M D. Engineering College Department of Computer Science and Engineering India R.M D. Engineering College Department of Computer Science and Engineering kavaraipettai India R.M.D Engineering College Chennai India

ISBN: (纸本)9798331505462

Cardiovascular disease (CVd) is the group of disorder occurs on heart and blood vessel, and they are coronary heart disease, cerebrovascular disease, rheumatic heart disease, etc. Particularly people under the age of 70 are mostly affected by this disease. Earlier cardiologist analyse phonocardiography (PCG) signals to know the condition of heart. Because of the undesirable noise present in PCG signal makes complication in diagnosis by experts. This study analyse and denoising heart sound using five different techniques namely Short-Time Fourier Transform (STFT), Empirical Mode decomposition (EMd), Recurrent Neural Network (RNN), weiner filter (WF), adaptive Wiener-Kalman filtering (AWKF). There are lot of noise removal techniques available as discussed in the literature, failed to achieve higher performance in denoising audio signal. The developed denoising techniques were applied on various pathological heart sounds and on heart sound recorded in a noisy environment. The AWKF filtering has great impact on removing distortion and interference present on the signal. This method reserves the major characteristics of heart beat sound. The experimental results showed that AWKF acquired higher SNR, PSNR, lower RMSE and great denoising effect. © 2024 IEEE.

关键词： Wiener filtering

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Effect of Sustainable Column Materials on Compressibility Characteristics of decomposed MSW Through Model Study 9th

Effect of Sustainable Column Materials on Compressibility Ch...

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9th Indian Young Geotechnical Engineers conference, 9IYGEC 2023

作者： Shah, Manish V. Shah, Alpa J. Jha, Abhishek Gajjar, Nirali Department of Applied Mechanics L. D. College of Engineering Ahmedabad India

ISBN: (纸本)9789819769872

Over a period of time, municipal solid waste decomposes and converts into material having 70–80% soil content. MSW has a sandy gradation, but it has a water affinity, which makes it more compressible. The composition of this waste varies from place to place, as do its geotechnical properties. This waste material can be used as fill material for construction sites, or old landfills themselves can be used as construction sites. However, because of its organic content and fibrous composition, it is compressible in nature and requires long-term geotechnical techniques to reduce compressibility and increase load carrying capacity. As per the Central Pollution Control Board (CPCB), only 28% of collected waste is treated, and the rest is dumped in landfills, which need to be sustainably utilized. The composition of MSW in India is approximately 40–60% compostable, 30–50% inert, and 10–30% recyclable. This will be a complete dual task of waste utilisation and fill material independence from borrow pits. In India, the rate of generation of C&d waste annually increases by 5%, and approximately 40% of fly ash remains unutilized every year, which needs to be utilized. The objective of the present research work is to determine the load-settling characteristics of old MSW by infusing sand, fly ash, lime and C&d waste columns, respectively, from the top. The present work is mainly focused on the improvement in load settlement characteristics like axial load carrying capacity, load resistance, settlement reduction factor, and stress concentration ratio obtained due to the infusion of various column materials. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Fly ash

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Smart Contracts in Real Estate: Enhancing Efficiency, Security, and Fraud Prevention Through Blockchain Technology - Modified Proof of Authorization (M-POA) 2

Smart Contracts in Real Estate: Enhancing Efficiency, Securi...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Subbarayudu Hemalatha, M. Prathyusha Engineering College Department of Information Technology Chennai India R.M.D Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

The real estate market is gradually changing with the pace of technology enhancement. While modernization with processes such as online platforms, virtual tours, and eSignatures has made some progress in this industry, a lot of challenges lie ahead, especially in security and fraud. This article addresses all these issues through the introduction of a new real estate ecosystem using blockchain technology but mainly with the Proof of Authorization (PoA) consensus mechanism. Blockchain technology offers an immutable ledger system whereby all property data and transactions are permanently recorded and verified. However, our research goes a step further in utilizing PoA. Unlike blockchain open models, PoA participation is restricted to only authorized entities within the network. They go through rigorous verification procedures, thus making fraudulent activities close to impossible under public and permission less models. By employing PoA, create a trust framework where only authenticated participants can transact on the system. This very much minimizes the realm potential to be governed by fraud. In short, the study used and empowered by blockchain, and security backed by PoA, is developing much more secure, transparent, and efficient setup of real estate transactions, developing trust and confidence within the stakes toward a future where real estates' transaction would be fast, secure, and fraud-free. With this innovation, the business of real estate is moving in a direction that is more effective, secure, and trustful. © 2024 IEEE.

关键词： Smart contract

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Model Study of Reinforced Earth Wall Using dune Sand as a Backfill Material 9th

Model Study of Reinforced Earth Wall Using Dune Sand as a Ba...

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9th Indian Young Geotechnical Engineers conference, 9IYGEC 2023

作者： Shah, Manish V. Shah, Alpa J. Kalsariya, Vimal Shukal, Sparsh Department of Applied Mechanics L. D. College of Engineering Ahmedabad India

ISBN: (纸本)9789819766420

As reinforced earth walls are more durable and aesthetically better finished when compared to conventional retaining walls, the flexible nature of reinforced earth mass enables a reinforced earth wall to withstand large differential settlements without distress. Reinforced earth walls are consequently economical when the height of the structure is large or ground conditions are unfavorable and suitable backfill materials are locally available. The main objective of the research is to evaluate the feasibility of dune sand as a sustainable fill material for reinforced earth walls through the infusion of synthetic reinforcement at suitable positions. dune sand from Jaisalmer district, Rajasthan, is used as a backfill material for reinforced earth walls. For this purpose, load settlement and load–displacement tests were carried out on dune sand and River Sand, with a mix proportion of 1(dS):1(RS), 1.5(dS):1(RS), and 2(dS):1(RS) using a large-scale model test setup. To measure the displacement and settlement, dial gauges were set up in the RE wall model to measure the vertical settlement of the backfill material and the lateral displacement of the RE wall. The vertical pressure distribution at the base of the RE wall using three different methods: Uniform pressure distribution, Trapezoidal pressure distribution, and Meyerhof’s pressure distribution, Vertical stress distribution on horizontal and vertical planes, active earth pressure were calculated for the justification of the experimental results. The results depicted a mixture of 1(dune Sand):1(River Sand) to be satisfactory. With the increase in the percentage of dune sand in the backfill, the composition shows an increase in lateral displacement. The vertical settlement of 2(dune Sand):1(River Sand) is much less compared to other proportions. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Sand

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SAdCCNet: self-attention-based dense cascaded capsule network for bone cancer detection using deep learning approach

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Iran Journal of computer Science 2025年 1-21页

作者： Kotwal, Jameer Gulab Sharma, Nand Kishore Somkunwar, Rachna Bavkar, dnyaneshwar deshmukh, Swapnil Khan, Vajid Korade, Nilesh Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pimpri Pune 411018 India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur 209305 India Department of Computer Engineering MGM College of Engineering & Technology Navi-Mumbai 410209 India Department of Computer Engineering G.S. Moze College of Engineering Balewadi Pune 411045 India Department of Computer Science and Engineering (Artificial Intelligence) Vishwakarma Institute of Technology Pune India

Bone cancer is a cancerous tumour that originates in bone tissue and causes inflammation, pain, and fractures. It could spread from other body areas or start in the bones. After a diagnosis, it is essential to begin appropriate treatment procedures at once, especially for malignant bone tumours. Several techniques were developed for bone cancer detection at an earlier stage. However, those techniques failed to provide efficient results due to their overfitting, higher false positive rate, etc. To overcome these kinds of issues, the proposed methodology is introduced to provide efficient performance. Initially, the data were gathered from Chonnam National University Hospital (CNUH) dataset. The pre-processing was done using the uprated non-local mean filtering (Up-NonMF) and contrast-limited AdAPTIVE histogram equalisation (ClAHE) for noise reduction and contrast enhancement. Then, the multi-stage UNet (Multi-SUnet) is used for segmentation. Finally, the proposed self-attention-based dense cascaded capsule network (SAdCCNet) model is used to classify bone cancer. Here, the SAdCCNet model is optimised using the binary zebra optimisation algorithm (BZOA) to improve its efficiency. This model is executed using the Python tool. The performance of the proposed technique is determined by comparing it with various existing models such as Res_UNet, MobiNet_V2, and VGG-16 models. The proposed model achieves 98.93% accuracy, 97.05% precision, 97.09% recall, 97% F1 score, and 97.35% Specificity. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adaptive histogram equalisation dense capsule networks Self-attention UNet

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detection of Vehicle lights and Classification of Its State 3

Detection of Vehicle Lights and Classification of Its State

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Kumar Jha, Raman D y Patil College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350348347

The lights of a vehicle are an essential part of it. It not only helps to drive but also helps in controlling the vehicle. In this paper, a novel method is presented for the detection of vehicle headlights and taillights. This paper also classifies the state of the vehicle lights. Vehicles that are near to the camera are considered for the experiment and the size of the vehicle light had a significant impact on the detection accuracy. Indian driving dataset (Idd) was used for the training and testing of the model. Idd consists of the rural and urban roads of Hyderabad and Bengaluru City. It covers various light conditions like day and night and scenarios like a smaller number of vehicles and a greater number of vehicles for detection. Some data preprocessing was also performed before using the dataset for the experiment. The model first detects the vehicle light and then it also classifies the state of the vehicle light. For vehicle light detection, some of the widely used object detection models such as YOlO, Faster RCNN, and MobileNet are used. For the classification of the state of the vehicle light, a color thresholding approach was used to develop the system. The system achieved a mean average precision(mAP) of 91.1%. It classifies it as Headlight on, Taillight on, or light off. The model performance was also evaluated and tested on Idd videos and it was very promising. © 2023 IEEE.

关键词： Image segmentation

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Classification of diseases in Citrus Fruits and leaves using data Science Technique with Pesticides Spraying System using Arduino

Classification of Diseases in Citrus Fruits and Leaves using...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Sharmila, V. Ezhumalai, P. Selvi, d.M. Kalai Nisshanth, J. Singh, ManwinYouds A. Rubesh, H. Department of Computer Science and Engineering R.M.D Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

Agriculture is popularly called to be "backbone of global economy". The genus Citrus, which contains a range of fruits distinguished by its unique perfume, high vitamin C content, and refreshing taste, is home to the group of fruits known as citrus fruits. They are widely cultivated and consumed around the world. Citrus fruits, beloved for their refreshing taste and high nutritional value, can face significant challenges from various diseases that impact both the trees and the fruit they bear. Among these diseases, citrus canker stands out as a bacterial affliction causing raised lesions on leaves, stems, and fruit. Though various technological advancements have replaced various industries, the agriculture sector cannot be replaced. lack of proper planning and forecasting techniques leads to poor yield and wastage of the crops produced. deep learning is widely employed for such classification. In this paper, we have used pre-trained Convolution Neural Network (CNN) models such as AlexNet and leNet Architecture. It gave feasible result when it was retrained with a dataset of 2029 images. This model classifies the fruits and leaves into 7 categories. After classifying the fruits, the output to send to the hardware implementation and pesticide and water spraying is triggered based on logical combinations of Arduino as well as taking into consideration the output of the soil moisture sensor. This project aims to improve the crop yield by early detection of diseases and provides an automated pesticide spraying system. © 2024 IEEE.

关键词： Soil moisture

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Impact of UAV-Based Transmitter Mobility on Physical layer Security

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5604-5619页

作者： Ruby, Rukhsana Elhalawany, Basem M. Pham, Quoc-Viet Wu, Kaishun Wang, lu Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Kuwait College of Science and Technology Electronics and Communication Engineering department Block 4 Doha District93004 Kuwait Trinity College School of Computer Science and Statistics Dublin 2 DublinD02 PN40 Ireland

Owing to flexible management and low overhead, wireless physical layer security (PlS) has been applied to support many critical applications (e.g., data dissemination) of unmanned aerial vehicles (UAVs)-based mobile communication networks in emergency scenarios. Although the impact of static network scenarios or receiver mobility on PlS has been well studied, there is no much work that studies the impact of UAV-based transmitter mobility on PlS. To fill this gap, in this paper, we investigate PlS of a scenario, where a random mobile UAV-based transmitter transmits information to a static ground entity under Rayleigh fading channel. More specifically, we consider a communication system, in which a mobile UAV hovers over a region to collect information and then disseminates this information to a static ground network entity in a confidential manner under the presence of an eavesdropper. Because of popularity and practicality, the UAV is assumed to hover following the random way point (RWP) mobility model. We investigate the secrecy characteristics of the UAV under steady running state in terms of ergodic secrecy capacity (ESC), positive secrecy capacity probability (PSCP) and secrecy outage probability (SOP) for the communication between the UAV and the receiver. We then investigate the secrecy performance of the proposed system while considering the pause time of the RWP mobility model adopted by the UAV. We further extend our proposed theoretical model to other realistic scenarios, including the presence of multiple cooperative and non-cooperative eavesdroppers, and study the PSCP and SOP metrics of the corresponding system. Furthermore, we propose three types of secrecy improvement strategies for the considered communication model. We strike a good trade-off between the secrecy improvement and transmit outage probability. Extensive simulations have been conducted to validate our theoretical analysis as well as the effectiveness of the proposed secrecy improvement s

关键词： drones

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