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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Bose, Ankita Reddy, C. Karthik Balaji, O. Nithin Sai Dushyanth, B.V. GITAM Deemed to be University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505776

Epilepsy, a condition affecting millions worldwide is characterized by recurring seizures. EEG (Electroencephalography) offers a direct assessment of the brain's electrical activity. This non-invasive measurement technique, with its high temporal resolution, enables the detection of specific seizure patterns. Early and precise prediction of seizures is crucial in preventing consequences like status epilepticus and sudden unexpected death in epilepsy (SUDEP). The objective of this study is to develop an advanced seizure detection algorithm with high accuracy. In our proposed model band analysis and feature extraction are done using Short Time Fourier Transform (STFT) and these features are passed to a hybrid model of CNN2D (network with 2D input) and Bidirectional Long Short Term Memory (BiLSTM) model. This model is validated across various patient profiles. Results indicate that the CNN2D+BiLSTM model outperforms other existing models in terms of prediction accuracy and speed. Furthermore, adaptive learning mechanisms enhance detection capabilities over time. This study makes use of the CHB MIT Scalp EEG Database and the Siena Scalp EEG Database, which contain EEG recordings from 500 individuals. Key performance measures include: OVLP: Overlap ratio signifying intersection between true and predicted events. EPOCH: Analyzing the alignment of labels and predictions in a time frame, for each EEG channel. ROC Curve: Illustrates the balance between sensitivity and specificity. © 2024 IEEE.

关键词： Long short-term memory

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Authentication and Access Control in Software Defined Network-Internet of Things: A Security Perspective

Authentication and Access Control in Software Defined Networ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sahana, D.S. Gopala Krishnan, C. Gitam School of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

The study's primary goal is to prevent unauthorized people and systems from accessing protected resources in a way that goes beyond their permissions. It aims to provide an overview of authentication and access control methods while showcasing current research developments in the SDN-IoT space. The review discusses current Authentication and Access Control approaches and technologies, including security difficulties, risks, and numerous technological innovations. Finally, it also covers the various authentication and access control existing mechanisms and how technology might be incorporated into a network's architecture plan. © 2024 IEEE.

关键词： Authentication Protocol

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Advanced UAV - based leaf disease detection: Deep Radial Basis Function Networks with multidimensional mixed attention

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Multimedia Tools and Applications 2024年 1-29页

作者： Das, Bappaditya Raghuvanshi, C.S. Research Scholar Department of Computer Science and Engineering FET Rama University Kanpur India Professor Department of Computer Science and Engineering FET Rama University Kanpur India

Plant disease causes a highly significant impact on production due to its destructive characteristics. A variety of chemical techniques should be applied to agriculture at different stages of disease infestation to enable precise plant protection. The integration of unmanned aerial vehicles (UAVs) for obtaining high-resolution images has become necessary due to the growing demand for precision agriculture, making crop health monitoring more efficient. In this study, we developed an innovative approach that combines the Deep Radial Basis Function Network (DRBFN) with the Multidimensional Mixed Attention (MMA) method. Since DRBFNs use radial basis functions as effective feature extractors, the model can detect even the smallest changes in the composition, color, and texture of leaves. This capability is particularly helpful when dealing with a diversity of plant species and pathogens exhibiting different symptoms. By deliberately considering pertinent spatial and spectral attributes, MMA is applied to enhance the discriminatory abilities of the network. By integrating spatial and spectral attention, the network can efficiently acquire information from both local and global sources, thereby enhancing the overall resilience and precision of the detection system. Our proposed model leverages UAV-captured images to achieve a classification accuracy of 96.95% in identifying leaf diseases across two different plant species. The integration of mixed attention mechanisms improved feature representation, leading to a 47.63% reduction in Root Mean Square Error (RMSE) compared to traditional methods. Furthermore, our model demonstrates significant efficiency by processing 2000 images, making it suitable for real-time applications. These results underscore the potential of UAV-based systems combined with advanced deep learning techniques for precision agriculture, offering scalable solutions for early disease detection and management. © The Author(s), under exclusive licence to S

关键词： Deep learning

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Emotion estimation from nose feature using pyramid structure

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Multimedia Tools and Applications 2023年第27期82卷 42569-42591页

作者： Shaila, S.G. Vadivel, A. Avani, Suma Department of CSE Dayananda Sagar University Bangalore India Department of Computer Science and Engineering GITAM School of Technology GITAM University Bengaluru Campus Bengaluru India

The facial expression and human emotions are considered as important components for building various real-time applications such as human expression and emotion recognition systems. Various parts of the human face contribute to recognizing expression. The contribution of action units on the nose is considered important. In this paper, input images are converted into HSV color space for better representation. The nose area is localized and the boundary is drawn by segmentation process using Fuzzy C-means Clustering (FCM). The segmented nose on the human face is modelled using a pyramid/tetrahedron structure and it is superimposed on the reference face. The feature points are identified on the pyramid model, where the Action Units (AUs) falling on the tetrahedron are identified. These points are validated with the theoretical properties of the tetrahedron so that the constructed feature vector is robust. The degree of deformation at various points is constructed as the feature vector. The feature vector is extracted for all the database images, say JAFE and CK++ datasets, and the feature database is created and stored separately. The feature data sets are used for training and thus, they are n-fold cross-validated to avoid over and under fitting. Given an input image for estimating the expression and emotion, the feature vector of the input image is compared with the feature vector of deformed images stored in the database. We have used Support Vector Machine (SVM), and Multilayer Perceptron (MLP) and Random Forest classifier to classify the expression and derive emotion. The JAFE and CK++ datasets are used for experimental analysis. It is found that the Nose feature using pyramid/tetrahedron structure is giving good results. Most of the time the classification accuracy is more than 95%. The experimental results are compared with some of the well-known approaches and the proposed tetrahedron model has performed well with classification accuracy more than 95%. © 2023,

关键词： Support vector machines

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A novel composite machine learning model for the prediction of compressive strength of blended concrete

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Journal of Building Pathology and Rehabilitation 2025年第1期10卷 1-18页

作者： Prasad, E.V. Rama Krishna, S. Singha, S. Department of Civil Engineering School of Technology GITAM (Deemed to be University) Telangana Hyderabad India Department of Computer Science and Engineering School of Technology GITAM (Deemed to be University) Telangana Hyderabad India

Blended cement, comprising clinker and supplementary cementitious materials (SCMs), such as fly ash, slag, and silica fume, forms blended concrete when combined with aggregates. This study introduced a novel ensemble approach, the LightGBM and ANN Fusion (LAF) model, to check the prediction of compressive strength in blended concrete using eight key input features, including cement content, slag, fly ash, water, and curing age, derived from 1030 samples. The LAF model synergistically combines predictions from LightGBM and ANN with the fusion weight (alpha) dynamically optimized using Support Vector Machine (SVM) analysis. This integration leverages the strengths of both models while mitigating their individual limitations. The LAF model exhibited superior performance, with an R² of 0.9514 during training and 0.8863 during testing, significantly surpassing standalone models such as LightGBM (R² = 0.9385) and ANN (R² = 0.9136). It achieved the lowest error rates, including an MSE of 15.7384 and RMSE of 3.9672 during training, underscoring its robust generalization capabilities. The novel utilization of SVM-tuned alpha for dynamic model adaptation reduces overfitting and enhances predictive accuracy compared to traditional ensemble and single models. Comparative analysis with existing ensemble techniques, including stacking and gradient-boosted ANN, further validated the superior accuracy and adaptability of the LAF model. This approach represents a significant advancement in predictive modeling, offering a scientifically sound and versatile framework for concrete strength prediction. Future research will refine the SVM tuning, expand the model’s application to other material properties, and explore real-time deployment, positioning the LAF model as a pioneering tool for smart construction materials. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： ANN Compressive strength LAF LGBM Performance indicators Sensitivity analysis SVM

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Improved Set Algebra-Based Heuristic Technique for Training Multiplicative Functional Link Artificial Neural Networks for Financial Time Series Forecasting

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SN computer science 2024年第5期5卷 567页

作者： Behera, Sudersan Kumar, AVS Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering & amp Technology GIET University Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

The current study is defined by two main aims. An effective strategy for improving local search is to combine the Set Algebra-Based Heuristic Algorithm (SAHA) algorithm with the Nelder-Mead simplex method. The approach outlined above, referred to as the Improved SAHA (ISAHA), and has the ability to produce superior outcomes. The multiplicative functional link artificial neural network (MFLANN) is an improved version of the functional link artificial neural network that promotes exploration by replacing the summing unit in the output layer with a multiplication unit. Moreover, the combination of ISAHA and MFLANN results in the development of ISAHA-MFLANN, a sophisticated hybrid forecasting model. The main assessment of the hybrid model rests on its ability to forecast complex and dynamic financial time series. It's possible to get around the problems that come with traditional learning-based MFLANN techniques by using MFLANN's advanced approximation features and ISAHA's resilient global search features together. Experimental verification using two stock market datasets and three currency exchange rates demonstrates the validity of the idea. The results show that the suggested improved SAHA hybrid learning works well at improving six standard benchmark functions. The ISAHA-MFLANN model is also statistically significant at accurately capturing the volatility that is inherent in the financial time series. In addition, it surpasses other models such as SAHA-MFLANN, Monarch Butterfly Optimization-MFLANN, Particle Swarm Optimization-MFLANN, Genetic Algorithm-MFLANN, Gradient Descent-MFLANN, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Auto Regressive Integrated Moving Average (ARIMA). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Exchange rate forecasting Improved Set Algebra-Based Heuristic Algorithm Multiplicative functional link artificial neural network Stock market forecasting

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An Extensive Examination of Methods for Detecting and Classifying Malignant and Benign Skin Cancers Using Deep Learning Techniques 3

An Extensive Examination of Methods for Detecting and Classi...

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

作者： Pasumarthy, Rambabu Gondi, Lakshmeeswari Gitam School Of Technology Gitam Deemed To Be University Department Of Computer Science And Engineering Andhra Pradesh Vishakhapatnam India

ISBN: (纸本)9798350318609

Skin cancer is a pressing health concern responsible for numerous fatalities in today's world, often diagnosed in its advanced stages, making treatment planning and patient survival challenging. Medical image interpretation, such as MRI as well as CT scans, is crucial for skin cancer diagnosis, but the manual analysis is costly, time-consuming, and can be influenced by biases. DL (Deep learning), a cutting-edge technology, is gaining attention as it allows for the automatic evaluation and interpretation of medical images, reducing the need for human intervention. Deep learning approaches have demonstrated remarkable outcomes in various domains by automatically extracting intrinsic image features, excluding the need for manual feature extraction. This review delves into the application of DL approaches in the diagnosis & detection of cancer, with a particular focus on skin cancer. Detecting melanoma from samples of dermoscopic skin is a formidable task, but employing a machine vision tool, like a deep learning approach, can address some of these challenges. The study introduces an automated melanoma classifier on the basis of DCNN (Deep Convolutional Neural Network) to accurately differentiate malignant from benign melanoma. It also outlines the various deep learning models and steps involved in utilizing these models for skin cancer detection. Recent advancements in the skin cancer detection Deep Learning (DL) methods are explored and summarized, shedding light on the critical challenges that have been faced in accurately detecting the cancer of skin in its early stages. For this research, dermoscopic images from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC 2017, and ISIC 2020), containing diverse cancer samples, were obtained and evaluated based on metrics like precision, accuracy, specificity, recall, and F1-score. © 2024 IEEE.

关键词： Convolutional neural networks

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Analyzing the performance of geometric mean optimization-based artificial neural networks for cryptocurrency forecasting

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International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Behera, Sudersan Kumar, A V S Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering and Technology GIET University Odisha Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

In this study, we utilize a recently proposed non-parametric metaheuristic algorithm known as geometric mean optimization (GMO) to adjust the hidden layer input weights and bias of six ANN variants, namely PSNN, SPNN, JPSNN, FLANN, RBFN, and MLP, thereby creating six hybrid models. Later on, we engage all these hybrid models to predict the closing price of four widely used cryptocurrencies. For comparison purposes, we also engage the widely popular traditional GD algorithm to fix the hidden layer input weights and biases of the same set of ANNs, creating six alternate models and employing those models for the same prediction task. In order to assess the effectiveness of each model, we employ the mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) statistics. The experimental result shows the superiority of GMO-based PSNN over others. We then compare the GMO-based PSNN's outcomes with some of the existing hybrid models in the literature. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ANN Cryptocurrency forecasting Financial time series forecasting GMO Higher order neural networks

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Enhancing User Experience and Mitigating BIAS in Electronic Device Recommendations on e-Commerce Platforms: Leveraging NLP and GPT Technologies 4

Enhancing User Experience and Mitigating BIAS in Electronic ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Gunda, Pavan Komati, Thirupathi Rao Gitam School of Technology GITAM Deemed to be University Department of Computer Science & Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798331540364

Electronic devicerecommendations on e-commerce sites enhance the user experience by assisting users in finding products that suit their requirements, interests, and preferences. By providing them with pertinent options, it helps users who are looking for specific gadgets save time and effort. Electronic devices commonly saw shorter product lifecycles due to the regular introduction of updated and new models. Users may feel under pressure to update to newer versions in order to access the newest features or upgrades, which could cause obsolescence problems. Previously NLP techniques are utilized for recommending electronic gadgets. Biased recommendations may result from NLP models inheriting biases found in the training set of data. As a result, there may be unfair or discriminatory consequences, such as more product recommendations for particular demographic groups or the reinforcement of preexistingpreconceptions. GPT may be used to construct chatbots or AIs that engage with customers using natural language. These assistance systems driven by AI are able to help customers make purchases, answer their questions, and make personalized product suggestions. Companies can improve the customer experience by responding quickly and giving useful information. One can use GPT to simplify customer service tasks like answering common questions and fixing problems right away. This can help companies help customers around the clock, speed up reaction times, and give human support workers less work to do. Support systems driven by GPT can also learn from exchanges with customers over time, making them better at what they do. High-quality material may be produced by GPT for use in blog entries, social media postings, marketingand copy, including product descriptions. One can use this material to get people interested in and buying from your e-commerce sites, show off product attributes and advantages, and attract new customers. We can also make GPT-generated content search engine

关键词： Sales

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A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

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Multimedia Tools and Applications 2024年第17期84卷 1-27页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

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