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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.L. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Customized CNN Model for Alzheimer's Disease Stages Classification and Comparison with Transfer Learning 1

Customized CNN Model for Alzheimer's Disease Stages Classifi...

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1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Sindhu, B. Prasad, Rishitha Kodipalli, Ashwini Kumar Swamy, S. Rao, Trupthi Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306637

Alzheimer's disease, the predominant form of dementia, progressively worsens over time, initially causing mild memory loss and eventually severely hampering daily tasks and communication abilities. This degenerative condition affects regions of the brain integral to cognition, memory, and language. Due to its intricate nature, symptom experiences and progression can vary among individuals. This study proposes a predictive model to discern Alzheimer's stages. Leveraging 'deep learning' techniques through convolutional neural networks (CNNs), the algorithm is trained on an extensive dataset of brain MRI scans. It learns to recognize pivotal features and correlate them with diverse disease phases. The Customized CNN model's accuracy of 94.37% surpasses alternative machine learning methods, including transfer learning. This precision empowers early prediction, significantly benefiting patient care and therapeutic strategies, thereby enhancing Alzheimer's disease management. © 2023 IEEE.

关键词： Convolutional neural networks

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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A novel Approach for optimizing the crop yield by considering climatic and soil features through fusion of Machine Learning and Deep Learning algorithms 15

A novel Approach for optimizing the crop yield by considerin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suganthavalli, K. Meenakshi, C. Parvathy, K. Department of Computer Science Chennai Tamil Nadu Pallavaram India Department of Computer Application Chennai Tamil Nadu Pallavaram India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350370249

The agricultural sector is the scientific and artistic cultivation of plants and animals. Agriculture has an important role in India's economy, which covers a significant 60.45% of the country's land and stands as the world's second-largest agricultural sector, driving economic growth through its diverse produce and associated industries. Soil composition really matters for growing stuff. Elements like Nitrogen, Phosphorous, & Potassium play a big role. The way you rotate crops, soil moisture, and even temperature all affect how well things grow. Rainfall is important too. Research has shown that using machine learning can help figure out the best crops to grow. They utilize methods that include Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) for this. The study also looks into deep learning methods to make the model even better. In addition to predicting crop yields, it also provides detailed information on the required amounts of soil elements and their associated costs. The new model offers superior precision compared to the current one. The system examines the provided data and assists farmers in forecasting a crop, which subsequently aids in achieving financial gains. The prediction of a suitable yield takes into account the meteorological and soil parameters of the area. This Python-based system utilizes advanced algorithms to optimize decision-making, forecasting the most lucrative outcomes in various situations while streamlining costs and boosting productivity. The study employs Support Vector Machines (SVM) as an example of Machine Learning (ML) method, while Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used as Deep Learning (DL) techniques. © 2024 IEEE.

关键词： Agriculture crop Prediction DL LSTM and RNN ML SVM

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A benchmark for visual analysis of insider threat detection

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science China(Information sciences) 2022年第9期65卷 294-296页

作者： Ying ZHAO Kui YANG Siming CHEN Zhuo ZHANG Xin HUANG Qiusheng LI Qi MA Xinyue LUAN Xiaoping FAN School of Computer Science and Engineering Central South University School of Data Science Fudan University Qi An Xin Group School of Information Technology and Management Hunan University of Finance and Economics

Dear editor,Malicious insiders are trusted entities that are given the power to violate rules in a given security policy, and insider threats occur when trusted entities abuse such a power. In modern networked work environments, malicious insiders and their malicious activities pose serious threats to organizations and businesses. Trusted employees travel the intranet of an organization with a high degree of freedom.

关键词： visual analytics visualization evaluation benchmark intellligence analysis

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Performance Investigation of Hybrid Bio-Inspired Zone Routing Protocol Over Proactive and Reactive Routing Protocols

Performance Investigation of Hybrid Bio-Inspired Zone Routin...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Ramamoorthy, Raghu Naidu, R.Ch.A. Saravana Kumar, E. Ashwin, M. The Oxford College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798350313987

Vehicular ad hoc networks (VANETs) require optimal routing mechanisms for routing in highly dynamic environments. The most dynamic environment is subject to dynamic changes and a lack of central coordination. The combination of bio-inspired algorithms with reactive approaches is a suitable choice to deal with the dynamic characteristics of VANETs. In this regard, hybrid bio-inspired zone routing protocol (HyBIZRP) proposes to find the optimal shortest path with a limited number of vehicles for routing messages. In HyBIZRP, phase 1 follows a modified proactive mode and phase 2 follows an enhanced reactive mode for routing. Using modified Ant Colony Optimization (ACO) in HyBIZRP provides optimal routing with low overhead and delay. The proposed HyBIZRP and existing works are simulated in Network Simulator (NS 2). Important metrics such as throughput, packet delivery ratio (PDR), overhead (OH), and end-to-end delay (EED) were used to compare the proposed HyBIZRP with proactive enhanced bio-inspired routing algorithm (EBIRA), reactive improved distance-based Ant Colony Optimization Routing (IDBACOR), and Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP). The simulation outputs show that the proposed HyBIZRP is superior in all aspects compared to EBIRA, IDBACOR, and EHACORP. © 2023 IEEE.

关键词： Ant Colony Optimization Hybrid Network Simulator 2 Proactive Reactive Vehicular ad hoc networks

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Joint Communication and Synchronization Performance Optimization in Digital Twin Enabled Networks

Joint Communication and Synchronization Performance Optimiza...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Hanzhi Liu, Yuchen Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States

ISBN: (纸本)9798350351255

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accurac

关键词： Resource allocation

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Comparative Study of Customized CNN Model and Transfer Learning for Brain Tumor Classification

Comparative Study of Customized CNN Model and Transfer Learn...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Vaishnavi, N. Sinchana, K. Kodipalli, Ashwini Rao, Trupthi Kumarswamy, S. Global Academy of Technology Department of Computer Science and Engineering Karnataka Banglore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

Brain tumors are abnormal cell growths in the brain, which can be cancerous or non-cancerous. They have diverse effects, including cognitive and neurological impairments, as well as personality and behavioral changes. This study proposes a multiclass classification framework utilizing a plain CNN architecture and Transfer learning to accurately identify brain tumor types: meningioma, glioma, pituitary tumor, and no tumor. We have explored transfer learning models such as VGG16, AlexNet, and ResNet50, these are initialized with pre-trained weights and fine-tuned on a comprehensive brain tumor dataset which has an equal distribution among classes. Optimizers such as Adam, AdaDelta, and SGD are modified to enhance performance. AdaDelta was used as the VGG16 optimizer, and it helped us achieve the highest accuracy of 99.83% with a loss of 0.74%. ResNet50 on the other hand, had the lowest accuracy 70% and highest loss 66.68%. While AlexNet without dropouts and AdaDelta as optimizer gives highest loss of 92.83%. The custom CNN model's loss was 0.04%, which is the lowest of any of them. The findings contribute to robust tools for accurate brain tumor diagnosis and treatment planning. © 2023 IEEE.

关键词： Tumors

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INFOCON: CONCEPT DISCOVERY WITH GENERATIVE AND DISCRIMINATIVE INFORMATIVENESS 12

INFOCON: CONCEPT DISCOVERY WITH GENERATIVE AND DISCRIMINATIV...

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12th International Conference on Learning Representations, ICLR 2024

作者： Liu, Ruizhe Luo, Qian Yang, Yanchao HKU Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong School of Electronics Engineering and Computer Science Peking University China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

We focus on the self-supervised discovery of manipulation concepts that can be adapted and reassembled to address various robotic tasks. We propose that the decision to conceptualize a physical procedure should not depend on how we name it (semantics) but rather on the significance of the informativeness in its representation regarding the low-level physical state and state changes. We model manipulation concepts - discrete symbols - as generative and discriminative goals and derive metrics that can autonomously link them to meaningful sub-trajectories from noisy, unlabeled demonstrations. Specifically, we employ a trainable codebook containing encodings (concepts) capable of synthesizing the end-state of a sub-trajectory given the current state - generative informativeness. Moreover, the encoding corresponding to a particular sub-trajectory should differentiate the state within and outside it and confidently predict the subsequent action based on the gradient of its discriminative score - discriminative informativeness. These metrics, which do not rely on human annotation, can be seamlessly integrated into a VQ-VAE framework, enabling the partitioning of demonstrations into semantically consistent sub-trajectories, fulfilling the purpose of discovering manipulation concepts and the corresponding sub-goal (key) states. We evaluate the effectiveness of the learned concepts by training policies that utilize them as guidance, demonstrating superior performance compared to other baselines. Additionally, our discovered manipulation concepts compare favorably to human-annotated ones while saving much manual effort. Our code is available at: https://***/InfoCon/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Trajectories

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Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases 6

Hybrid Convolutional Neural Networks for Enhanced Detection ...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Porna, Shweta Bhattacharjee Kabir, Md. Firoz Rana, Md. Imran Chowdhury Sajol, Md. Saiful Islam Roy, Tonmoy Khan, Mohammad Aman Ullah Adnan, Md. Al Bhavani, Girigula Durga Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Master's in Information Technology University of the Cumberlands United States Bachelor of Business Administration International American university Los Angeles CA United States Louisiana State University Department of Computer Science and Engineering LA United States Data Analytics & Information Systems Utah State University Logan United States Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka1212 Bangladesh Noakhali Science And Technology University Bsc in software engineering Bangladesh Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9798331505790

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model's performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of 100 % on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture. © 2024 IEEE.

关键词： Fruits

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