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2024 International Conference on computer, Electrical and Communication engineering, ICCECE 2024

作者： Mondal, Arnab Dey, Ranita Roy, Anubhav Dutta, Sagnik Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350386479

In the era of pollution water being a resource we cannot waste. India is a country of agriculture and the need of water is high yet surface water is polluted thus cannot be used. Many water resources like ponds and small lakes in the village or urban area cannot be used just because the resources are not clean enough. Cleanliness is much needed;this is where the proposed model 'Suddh Salil' comes into play its role. The proposed model will take out any kind of floating waste product from the surface of stagnant water. Cleaning of surface water by traditional way was to make people get in the water and retrieve the waste products. This procedure is time consuming as well as not very precise. This proposed model will clean the surface of any stagnant water resource with the help of machine learning algorithm. Thus, making the cleaning process more precise and with automation. The model can precisely clean upper surface of the stagnant water resource using panoramic vision algorithm, YOLO V7(You Only Look Once) and Support Vector Machine (SVM). © 2024 IEEE.

关键词： Waste management

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Machine learning algorithms for the diagnosis of Alzheimer and Parkinson disease

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Journal of Medical engineering and Technology 2023年第1期47卷 35-43页

作者： Nancy Noella, R.S. Priyadarshini, J. School of Computer Science and Engineering VIT University Chennai India

Dementia is a general term used to indicate any disorder related to human memory. The various memory-related problems severely affect the human brain and so the individual feels difficulty in doing their normal physical as well as mental activities. There are different types of dementia that exist, but the commonly seen and fatal types of dementia are Alzheimer’s disease (AD) and Parkinson’s disease (PD). In this paper different efficient Machine Learning Techniques are selected analysed their behaviours in the diagnosis of AD and PD using Positron Emission Tomography (PET). The PET image dataset used in this work consists of 1050 images with AD, PD and Healthy Brain images. The total number of images is split into two different categories in the ratio of 7:3 for training and testing respectively. The different machine learning classifiers used are Bagged Ensemble, ID3, Naive Bayes and Multiclass Support Vector Machine. The classification of the AD and PD with the reference of a healthy brain is done by comparing the input image with the trained samples in the PET image database. In the comparison of trained samples with the input image for the PET images, the bagged ensemble learning classifier worked better than the other classification algorithms and yielded an accuracy of 90.3%. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Support vector machines

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Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

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computers, Materials & Continua 2023年第4期75卷 891-909页

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this ***-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.

关键词： Pneumonia prediction distributed deep learning data parallel model ensemble deep learning class imbalance skewed data

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Recent progress of artificial intelligence for liquid-vapor phase change heat transfer

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npj Computational Materials 2024年第1期10卷 2552-2565页

作者： Youngjoon Suh Aparna Chandramowlishwaran Yoonjin Won Department of Mechanical and Aerospace Engineering University of CaliforniaIrvineCAUSA Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCAUSA

Artificial intelligence(AI)is shifting the paradigm of two-phase heat transfer *** innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past,for making their insights available to other domains,and for solving for physical quantities based on first principles for phasechange thermofluidic *** review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation *** technologies for meta-analysis,data extraction,and data stream analysis are described with their potential challenges,opportunities,and alternative ***,we offer outlooks and perspectives regarding physics-centered machine learning,sustainable cyberinfrastructures,and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer.

关键词： transfer meaningful shifting

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BruSSH: Early Detection of Distributed Brute Force SSH Attacks Using LSTM 18

BruSSH: Early Detection of Distributed Brute Force SSH Attac...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sharma, Neha Swarnkar, Mayank Zuhair, Md Computer Science & Engineering Varanasi India

ISBN: (纸本)9798350391725

Secure Socket Shell, also known as Secure Shell, refers to the cryptographic network protocol and suite of implementation utilities that helps users connect a computer over an unsecured network. Although SSH provides a strong authentication and encryption mechanism to protect the data shared between an SSH client and an SSH server, cybercriminals relentlessly exploit SSH servers through brute force attacks using the common username and password combinations. These attacks are still prevalent because many servers are misconfigured;some servers might be using default usernames and passwords, and some can be using predictable usernames and easily guessed passwords. Moreover, these attacks pose significant challenges to the existing detection methods because attackers use distributed brute-forcing in which multiple sources are involved to repeatedly attempt to log into an SSH server by guessing the possible username and password combinations. Distributed brute-forcing attacks are harder to detect because IP addresses and port numbers change every time. Additionally, the majority of traditional detection systems rely on post-event analysis, which leads to delayed response times and increased vulnerability. To overcome the aforementioned problems, we present BruSSH, which utilizes a Long Short-Term Memory (LSTM) neural network for the early and accurate detection of distributed brute-force SSH attacks. Leveraging the capabilities of LSTM, we train our model using cumulative login failure counts per event, allowing for forecasting future login failure counts. Furthermore, BruSSH identifies the suspicious IP addresses associated with malicious activity. To verify the validity of our proposed method, we tested BruSSH with two publicly available datasets. The experimental results show that our method achieves favorable results in the detection of distributed brute-force SSH attacks. © 2024 IEEE.

关键词： Long short-term memory

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Blockchain-based Efficient and Secure Peer-to-Peer Distributed IoT Network for Non-Trusting Device-to-Device Communication

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Informatica (Slovenia) 2023年第4期47卷 515-522页

作者： Sharma, Rajesh Kumar Pippal, Ravi Singh Department of Computer Science & Engineering RKDF University Bhopal India

The security and privacy issues in the Internet of Things (IoT) are a mandatory process and also a challenging task for researchers. Blockchain technology enhanced and motivated the recent security parameters, and it has been validating various technical sectors since its inception. In this paper, a peer-to-peer distributed IoT network is presented where non-trusting devices can interact with other devices without a trustworthy intermediary using the blockchain technique in automated verifiable mode. Major implementation issues for deploying blockchain in the IoT network are pointed out in this paper. The model presents a modern blockchain technique to surpass the traditional security system for efficient and secure IoT deployment under various conditions. Finally, to validate the signification of blockchain in the IoT network, the avalanche effect is calculated and compared with Triple-DES, AES, and Blowfish cryptographic algorithms for non-trusting device-to-device (D2D) communications and transactions. The result presents significant output changes in hash for the blockchain-IoT integrated model as compared to other cryptographic algorithms. Conclusively, blockchain in the IoT network can make a remarkable impact across various industrial and business applications. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Internet of things

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Identification of Downy Mildew and Leaf Miner Diseases in Hybrid Cucumber

Identification of Downy Mildew and Leaf Miner Diseases in Hy...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Supreetha, S. Manjula, S.H. Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350357905

Agriculture plays a crucial role worldwide. For those with little experience in agriculture, an automated system that can recognize diseases in plants from visual indications would be quite helpful. In countries like India, where the economy heavily relies on agriculture, plant diseases significantly impact the quality and quantity of agricultural products. To ensure global food security and maximize profits for stakeholders, accurate detection and classification of plant diseases are of utmost importance. Previous research has shown that the rising incidence of plant leaf diseases accounts for about 42% of losses in agricultural productivity. Several approaches have been made possible in this area with the introduction of deep learning-based image classification. However, the practicality of implementing these solutions on low-end devices necessitates the development of fast, precise, and computationally efficient systems. To address this, our work proposes a lightweight transfer learning-based approach for detection of two diseases namely Downy Mildew and Leafy Miner in plant leaves of Hybrid Cucumber. This approach incorporates an effective preprocessing method that enhances leaf images resulting in improved classification performance. Our proposed system employs a combined model comprising a pretrained MobileNetV2 architecture and a classifier network to extract features and make accurate predictions. © 2024 IEEE.

关键词： Image enhancement

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Deep Learning Techniques to Detect Nutrient Deficiency in Rice Plants 7

Deep Learning Techniques to Detect Nutrient Deficiency in Ri...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Supreetha, S. Premalathamma, R. Manjula, S.H. Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350359299

Rice is one of the most widely grown crop in China, Bangladesh, and India. Crop productivity is eventually impacted by nutritional imbalances in plants, which affect their responses and defense mechanisms against abiotic stress, pests, and diseases. A majority of farmers use soil analysis, plant tissue analysis, and visual symptoms to identify nutrient deficiencies. However nutrient deficiency detection using soil analysis and plant tissue analysis requires experts. To reduce the workload of experts and farmers, modern technologies like Artificial Intelligence (AI), Machine Learning, Internet of Things (IoT) systems, and Deep Learning techniques are used. The main objective is to improve the accuracy in identifying and classifying nitrogen, phosphorous and potassium deficiency in rice plants compared to earlier approaches. In the proposed study, five of the most popular pre-trained CNN models namely InceptionV3, VGG16, VGG19, ResNet50, and ResNet152 is used along with SVM to predict the nutrition deficiencies in the Kaggle rice NPK deficiencies dataset. Additionally, the number of images is increased from 1156 to 4963 by performing augmentation on the Kaggle rice NPK deficiency dataset. The proposed system applies pre-trained CNN models (InceptionV3, VGG16, VGG19, ResNet50, and ResNet152) on datasets (with and without augmentation) to extract deep features from the images. These features are then saved in the feature vector. The feature vectors are then fed to the SVM in order to identify any nutrient NPK deficits in the images. Each classifier's performance is evaluated and contrasted based on its accuracy. The accuracy of all pre-trained CNN models ranges from 91% to 99%. The best result on the dataset without augmentation is 97.40%, and on the dataset with augmentation is 99.05%, with the pre-trained CNN model Resnet50+SVM. © 2024 IEEE.

关键词： Nutrients

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Phishing Website Detection Using Machine Learning 2

Phishing Website Detection Using Machine Learning

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Pathmanaban, J. James, Prabahar Godwin Ashok, P. Ragesh, B. Aakash, S. Kaushik, N. Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365269

In the digital age, the emergence of online risks, especially phishing websites, has become a major worry. Phishing websites are created with the intention of tricking users into divulging personal information, which frequently results in losses of money and security violations. This study uses web browser extensions and machine learning algorithms to provide a new method for phishing website detection to fight this threat. This research suggests creating a web browser add-on that watches over people in real time while they explore the internet. An extensive dataset of well-known phishing websites, trustworthy websites, and other dubious web pages was used to train the machine learning model that is included into the plugin. Through an examination of a website's composition, content, and user behavior, an additional line of defense against phishing attempts is offered by the extension's ability to recognize and notify users of potentially dangerous websites. © 2024 IEEE.

关键词： Websites

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Detection of Images for Video Based Spectroscopy by Using the Combination of Saliency Methods 4

Detection of Images for Video Based Spectroscopy by Using th...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar, Kaushal Bhushan Agarwal, Bharat Computer Science & Engineering Department India

ISBN: (纸本)9798350364729

Depth perception affects 2D image brightness, color, texture, and motion. In addition to stereoscopic vision, depth range, displaying size, 3D visualization, naturalness, and visual comfort can reconstruct 3D depth information. These features do not affect 2D visual attention. Replicating human visual attention in 3D situations requires saliency prediction methods designed for 3D environments. Stereoscopic videos' visual contrasts and object movements make saliency detection difficult. Depth map data is essential for saliency detection. For practical use, stereoscopic data is better than 2D. As visual scenes get more complex, 2D data alone cannot identify significant items. Depth maps help distinguish related items in complex situations. We use spatiotemporal correlation and depth information refinement to overcome these problems in this saliency detection investigation. Comparative analytical analysis of a baseline spatiotemporal depth correction system for multimodal saliency detection frameworks supports the basic premise. We chose two more saliency detection models: a cutting-edge multimodal filter transformer-based approach and a two-stream hierarchical model. We then compared these models to the baseline. We found that the spatiotemporal saliency model is effective due to its higher mean absolute error (MAE) values. Saliency models show that this method reduces visual region estimation and object detection mistakes. The spatiotemporal depth correction strategy is proven necessary and successful by this method. © 2024 IEEE.

关键词： Stereo image processing

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