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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Dev, R.B. Shanthi, S. Brindha, V. Arunkarthi, T. Ashwinth, K. Kuppuchamy, R. Kongu Engineering College Department of Computer Science and Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India

ISBN: (纸本)9798350349221

The process of identifying and categorizing lung cancer in its early stages is difficult, yet doing so will improve patient survival rates. There is a wealth of research that segments and categorizes lung nodules using convolution neural networks (CNN). Numerous factors, including batch size, epochs, learning rate, optimizers, activation function, and weight initialization, affect the model's performance. To categorize CT scan images as benign, malignant, or normal, a deep CNN model is developed. Optimizers are crucial to the training process of the model because they adjust the weight and learning rate of the network in each epoch, which lowers loss and raises classifier accuracy. Using ReLU and selu as activation functions, this work offers a thorough analysis of the performance for a number of optimizers, including Nesterov Adaptive Momentum (Nadam), Adaptive Delta (Adadelta), Adaptive Max Pooling (Adamax), Stochastic Gradient Descent (SGD), Adaptive gradient (Adagrad), Adaptive Momentum (Adam), Adaptive Gradient Descent (Adagrad), and Root Mean Square Propagation (RMSProp)[1][1][1][1][1][1]. The best training accuracy for the ReLu activation function is 92.67%, with a validation accuracy of 97.92, and for the Selu activation function, it is 95.42%, with a validation accuracy of 96.88, according to the Adam optimizer. The study aims to assess various optimization techniques in deep CNNs for lung cancer classification using CT scan images. It analyzes different optimizers to understand their impact on model convergence, accuracy, and generalization. This insight is crucial for selecting the most effective optimization methods in lung cancer diagnosis. © 2024 IEEE.

关键词： Biological organs

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Suicide risk assessment using word-level model with dictionary-based risky posts selection

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Multimedia Tools and applications 2024年第7期83卷 21435-21454页

作者： Tsai, Yun Sheng Chen, Arbee L. P. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

Suicide is a serious issue around the world and is a leading cause of death in US. In the past 20 years, the suicide rate has seen a significant increase of 35%. With the rapid development of information technology, more and more people begin to use social media to share their inner feelings. It enables social media data to be widely used for research on suicide risk assessment. However, not all social media posts are suicide related. Previous research addressed this problem with post-level attention mechanism. However, post-level attention mechanism may not find relevant suicide posts. This problem becomes more serious in the feature-based post embeddings since each post is converted into a single vector to serve as the input of the model, resulting in the loss of word-level information during training. In this paper, we addressed this problem by introducing a novel word-level model including a post-selectin layer as a solution. Firstly, we utilize a suicide keyword dictionary to identify risky posts that may be missed by the post-level attention mechanism. We then convert the words in the risky posts into word embeddings and use self-attention to generate the post embeddings for the risky posts. Finally, we pass the post embeddings to a multilayer perceptron to classify the suicide risk. We also demonstrate that the FScore used in previous studies can be reduced to a function of accuracy, which does not reflect the model performance in predicting imbalanced datasets. Therefore, we additionally adopt macro F1 score as the evaluation function. Experiment results show that our model not only outperforms previous studies in FScore performance, but also achieves macro F1 Score a nearly 4% improvement compared to previous studies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Embeddings

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Sustainable Energy Generation from Speed Breakers for Smart Street Lighting Application 2

Sustainable Energy Generation from Speed Breakers for Smart ...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vijayakumar, P. Pyingkodi, M. Karuppusamy, S. Prasaath, J.M. Dhyaneshvar Jeysaravanan, S. Manojkumar, V. Kongu Engineering College Department of Computer Applications Perundurai Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

This research explores the potential of integrating rack and pinion mechanisms with electromagnetic induction to generate electricity for street light control systems. By converting mechanical energy into electrical energy, this approach offers a sustainable and environmentally friendly solution. The proposed system utilizes Light Dependent Resistors (LDRs) and a Raspberry Pi microcontroller to dynamically control street lights based on ambient light conditions. This intelligent control strategy optimizes energy consumption and reduces operational costs. This research study analyzes the theoretical foundations, design considerations, and implementation methodologies of the proposed system. Experimental validation demonstrates the feasibility and effectiveness of the approach. By promoting the use of renewable energy sources and reducing reliance on conventional power grids, this innovative solution contributes to the development of sustainable and resilient urban infrastructure. © 2024 IEEE.

关键词： Proportional control systems

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IoT Based Agricultural Monitoring and Alert System 4

IoT Based Agricultural Monitoring and Alert System

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priya, V Vishnu Nisanth, G. Dhanushree, D. Sindhu, M. Varshini, T. Preteka, A.T. Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Applications Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (纸本)9798331523923

The goal of this project is to implement an Internet of Things (IoT)-based Agricultural Monitoring & Alert System (AMAS) that will integrate multiple sensors to continuously monitor agricultural parameters, such as soil moisture levels, soil temperature, water levels, irrigation pump operation status, and water quality, to improve precision farming practices. Enabling farmers to make well-informed decisions, maximize resources, and enhance crop output in real time is the primary objective of the AMAS system. The challenges of using IoT for agricultural monitoring, include issues with adoption among farmers, dependable technology, efficient data administration, and connectivity. Small-scale farmers' financial hardship and rural areas' restricted access to technology. Many obstacles stand in the way of the widespread adoption of IoT-based agricultural monitoring systems, such as smallscale farmers' limited financial resources, rural communities' restricted access to technology, and problems with connectivity, dependable data handling, and system scalability. Machine learning-based prediction models and IoT-integrated smart irrigation are two recent approaches that have showed promise in tackling these issues, but they still face obstacles such high implementation costs, technical complexity, and infrastructure dependence. By improving yields, increasing output, and facilitating remote monitoring, the AMAS system seeks to overcome these limits while taking into account the unique requirements and limitations of farmers. This study plan to enhance productivity, optimize yield, and implement remote monitoring. © 2025 IEEE.

关键词： Soil moisture

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ML computational inference techniques and indicator metrics for analyzing uncertainties in stock market data

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Multimedia Tools and applications 2025年 1-21页

作者： Henge, Santosh Kumar Mandal, Sanjeev Kumar Sharma, Amit Upadhyay, Aditya Kundu, Shakti Iyer, Srinivasan Saini, Ashok Kumar Yadav, Manoj Kumar Singh, Bhupinder Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Bangalore India Department of Computer Applications Lovely Professional University Punjab Jalandhar India Center for Distance and Online Education Manipal University Jaipur Rajasthan Jaipur India Haryana Gurugram122413 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science & Engineering Lovely Professional University Punjab Jalandhar India

In a growing demand of accurately predicting the stock market and inefficient complex markets the rising accurate relationship prediction is not adequately addressed by the conventional methods. The dynamic and complex natures of data sources become the issue and we need to propose an effective method for adaptive algorithms to accurately forecasting and make decision efficiently. The system framed with two stages of execution: The first stage has covered time series-based predictions for different stock values involving the LSTM Model;the second stage integrated the experimental scenarios based on trend, value, indicators, and it's supporting impacted data metrics, which experimented on various large-cap stock companies. The dataset is sourced online by yahoo finance API with consecutive days data and dependable, non-dependable influenced indicators, which helped to intend trend-based evaluation performances. The proposed model experimented on distinct stock values and test cases executed based on indicator-based prediction with a 76.92% accurate performance rate using a Random Forest Classifier and 76.92% of Logistic Regression. The value-based prediction test case has achieved a performance rate of 97.5% using SVR Regressor and 97.2% using Linear Regressor. It computed value of root mean square error for the Ten large cap companies for performance evaluation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive algorithms

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Real-Time Face Detection on Security Surveillance Footage

Real-Time Face Detection on Security Surveillance Footage

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Mohd, Noor Dham, Shradhay Bisht, Jai Singh Mehta, Aryan Joshi, Akshita Martha, Sheshikala Department of Computer Science and Engineering Dehradun India Department of Computer Applications Dehradun India Sr University Department of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350379716

Recent years have witnessed notable progressions in facial recognition technology which have been led by the inception of deep learning models-primarily Siamese Neural Networks. This article delves into the use of Siamese Neural Networks for one-shot face classification tasks, giving priority to extracting discriminative features and training deep learning models. Among the pillars of our methodology are: data collection;preprocessing;model building and real-time verification using deep learning abilities, especially Convolutional Neural Networks (CNNs). We hope that through this approach we can significantly improve the accuracy of facial recognition in real-time surveillance applications with what CNNs have to offer. © 2024 IEEE.

关键词： Convolutional neural networks

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Dual image based secured reversible data hiding scheme exploiting huffman compression tree combining bit-reversal permutation technique

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Multimedia Tools and applications 2025年第9期84卷 5653-5698页

作者： Datta, Kankana Jana, Biswapati Chakraborty, Mamata Dalui Department of Computer Applications Haldia Institute of Technology HIT Campus Hatiberia Haldia Purba Medinipur West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal Midnapore India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

In the area of data hiding and information security, the greater need is to ensure high embedding capacity of the stego media without hampering the visual quality while ensuring the tightest possible security. Now, it has become one of the biggest challenge to meet all these requirements. Embedding secret messages in dual images is one of the latest techniques that aims to cater such needs. However, there are very few data hiding schemes that are currently using dual images, but are suffering from several shortcomings, such as security loophole and poor visual quality with limited hiding capacity. In this paper, a dual image-based reversible data hiding scheme has been proposed where multiple bits can be embedded within a pair of pixels dynamically by Huffman compression where different pseudo codes are generated for different chunk of secret bits which are embedded by Bit-Reversal Permutation (BRP) and X-OR operations. Finally, pixels are interchanged between the dual images based on odd-even properties of the pixel values. The functional efficiency of the proposed scheme is evaluated by comparing the relevant parameters of different state-of-the-art schemes. It is established that the proposed scheme provides more than 64 dB PSNR value on average after embedding a maximum of 3, 27, 680 bits of secret data which ensures good visual quality and high embedding capacity as compared to other state-of-the-art schemes thereby benefitting innumerable applications across public and private sectors. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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Prostate Cancer Prediction Using Convolutional Neural Networks 3

Prostate Cancer Prediction Using Convolutional Neural Networ...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sethi, Bijaya Kumar Singh, Debabrata Rout, Saroja Kumar Department of Computer Science and Engineering Bhubaneswar India Department of Computer Applications Bhubaneswar India Department of Information Technology Hyderabad India

ISBN: (纸本)9798350364569

Cancer-related mortality in men is highest among men who suffer from prostate cancer. The lack of clarity and consistency of early symptoms often makes diagnosis a challenge in the later stages (stages III and IV). Existing diagnostic techniques face challenges such as subjectivity, variability between observers, and lengthy testing processes involving biomarkers, biopsies, and imaging tests. This paper introduces a novel convolutional neural network (CNN) algorithm for prostate cancer diagnosis and prediction to overcome these drawbacks. An additional dataset of histopathology images was used to train and validate the system before it was put through its learning phase. In the study, 95.12% of cancerously derived cells were identified correctly and 93.02% were identified correctly, a remarkable accuracy of 98.07%. As a result of this study, Various challenges associated with expert evaluations by humans were successfully addressed, including higher misclassification rates, interdependencies between observers, and lengthy analysis periods. Prostate cancer diagnosis and prognosis have been made much simpler and faster by this research. Moving forward, to optimize the effectiveness of our proposed method, future investigations should explore the latest developments and innovations in this field. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting Risky Environment for Child Inside House using Deep Learning 5

Predicting Risky Environment for Child Inside House using De...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Ahmad, Naeem Arya, Sumedha Singh, Deepak Department of Computer Applications Raipur India Department of Computer Science and Engineering Raipur India

ISBN: (纸本)9781665475242

We place a high priority on making sure kids are safe. Yet in the modern world, there are a lot of threats to children's safety and challenges to achieving other safety goals. Parent can't always supervise their kids and leave them alone inside the house. So this is a big problem of our time as the small kids is unable to assess the risk due to any harmful activity or objects. The purpose of this study is to provide safety to the child by alerting the parent about the child's position and surroundings. This proposed model can be used to monitor child's movements. We proposed a children's video data set. The video data set contains every moment of the child in the form of frames, and 30 frames are created every second. Additionally, we train a baseline computer vision module that determines the level of dangerousness for a child using deep learning. The baseline consists of well-established methods like conditional and logistic regression for child's pose estimation, as well as cutting-edge deep learning image classifiers. We then integrate the stumbled-condition baseline into various learning platforms and evaluate performance in terms of accuracy and loss. © 2023 IEEE.

关键词： computer vision

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