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A cyber-physical systems and the smart city vision: A comprehensive guide
The process of urban areas' transformation into smart cities with the help of Smart Cyber-Physical Systems (SCPS) is one of the most defining trends of modern urbanism. It requires a multifaceted perspective of smart cities, thereby evaluating the facets of SCPS intently concerning the complexities of their integration in urban structures while exploring their influence that transcends the domains of social sciences and economics, which has become crucial. In this context, smart cities are constructed as integrated systems at the crossroads of the digital and the physical: they sustain, facilitate, and improve the performance of the city's functions and living environment. The importance of technological environments in orientation and close consideration of SCPS reveals the functions in gathering data, immediate analysis, and decision-making processes of urban management. The interconnection of the Internet of Things (IoT), artificial intelligence (AI), and big data analytics considering their impact and the creation of sustainable enhancing the quality of public services. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A cyber-physical systems and the smart city vision: A comprehensive guide
The process of urban areas' transformation into smart cities with the help of smart cyber-physical systems (SCPS) is one of the most defining trends of modern urbanism. It requires a multifaceted perspective of smart cities, thereby evaluating the facets of SCPS intently concerning the complexities of their integration in urban structures while exploring their influence that transcends the domains of social sciences and economics, which has become crucial. In this context, smart cities are constructed as integrated systems at the crossroads of the digital and the physical: they sustain, facilitate, and improve the performance of the city's functions and living environment. The importance of technological environments in orientation and close consideration of SCPS reveals the functions in gathering data, immediate analysis, and decision-making processes of urban management. 2025, IGI Global Scientific Publishing. -
A Data Mining approach on the Performance of Machine Learning Methods for Share Price Forecasting using the Weka Environment
It is widely agreed that the share price is too volatile to be reliably predicted. Several experts have worked to improve the likelihood of generating a profit from share investing using various approaches and methods. When used in reality, these methods and algorithms often have too low of a success rate to be helpful. The extreme volatility of the marketplace is a significant contributor. This article demonstrates the use of data mining methods like WEKA to study share prices. For this research's sake, we have selected a HCL Tech share. Multilayer perceptron's, Gaussian Process and Sequential minimal optimization have been employed as the three prediction methods. These algorithms that develop optimal rules for share market analysis have been incorporated into Weka. We have transformed the attributes of open, high, low, close and adj-close prices forecasted share for the next 30 days. Compare actual and predicted values of three models' side by side. We have visualized 1step ahead and the future forecast of three models. The Evaluation metrics of RMSE, MAPE, MSE, and MAE are calculated. The outcomes achieved by the three methods have been contrasted. Our experimental findings show that Sequential minimal optimization provided more precise results than the other method on this dataset. 2023 IEEE. -
A data-driven approach to predicting breast cancer recurrence with hybrid machine learning models
Breast cancer recurrence is one of the most significant medical concern, and accurate recurrence models can assist in early intervention and treatment planning. Breast cancer recurrent remains as one of the most critical concern for patients prognosis and treatment planning. Accuracy Predicting individual recurrence risk is crucial for the development of precise therapy, specialy for those patients with high-risk profiles. In the study proposes a hybrid machine learning approach that uses the computational modeling and the medical information to predict the recurrence of breast cancer in a patient. The dataset contains the medical and patient information like the age, tumor size, lymph node involvement, malignancy degree, location, irradiation status and recurrence class. This proposed approach begins with the process of data processing, handling the missing data values, features normalization and encoding of categorical variable into numerical format. The dataset is divided into two parts the training set and the testing set and the two selected models random forest and logistic regression models are trained independently. The predictions form both the model is stacked and a logistic regression meta-model is trained on these combined predictions. The evaluation of the model was conducted using the metrics such as accuracy, precision, recall, and F1 score. The designed hybrid model was able to achieve the accuracy of 97.66% with the precision, recall and F1 score all reaching around 98.15%. This study highlights the potential of hybrid machine learning techniques, improving the accuracy and reliability of machine learning models for breast cancer recurrence prediction. This development model can serve as a valuable tool for the medical industry to support decision making and assist in personalized treatment decisions, offering early detection of recurrence. This can enhance the treatment of a patient by supporting early detection and patients outcomes through targeted therapy. Copyright 2026 Techno-Press -
A decade of climate change concern in India: Determinants of personal and societal climate concern
Scientists have called for a culturally relevant investigation of factors impacting public climate concern to devise relevant behavioural and policy interventions. Although India will be adversely affected by climate change, there is a shortage of models that track changes in Indian climate concern across time. The study tracked the growth of climate concern from 2006 to 2020 and identifies determinants of personal and societal climate concern. Secondary analyses of survey data from the International Science Survey and World Values Survey (2006-2020, N = 9254), were conducted to predict climate concern across the year, environmental protection versus economic growth preferences, and socio-demographic variables. Within responses from 2020 (N = 3176), the predictive role of anthropogenic climate change beliefs, trust in scientists, adequate government action, collective efficacy, environmental protection preferences, and sociodemographic variables were evaluated to understand personal and societal climate concern. Binary logistic regression found that climate concern increased significantly from 2006 (2.6%) to 2020 (89.5%) and was predicted by education and preferences for environmental protection. Multiple regression results identified personal climate concern as predicted by education, anthropogenic climate change beliefs, trust in scientists, and environmental protection preferences; while government action beliefs and favouring left-wing affiliation predicted societal climate concern. There was mixed support for the political polarization of climate concern. The study shows an increase in Indian climate change concern over the past decade, with personal and societal climate concern being influenced by different psychological characteristics. Important implications for future climate communication research and social policy development are discussed. 2024 by author(s). -
A decade survey on internet of things in agriculture
The Internet of Things (IoT) is a united system comprising of physical devices, mechanical and digital machines, and different hardware components like sensors, actuators, cameras etc., monitored and operated by the software. The combination of devices and systems connected over the internet opens the pathway for development of various applications beneficial in terms of economic growth of a nation. IoT has evolved as a potentially emerging computer technology solving various real-life problems and issues. IoT covers vast group of applications, from warfare to surveillance, from habitat monitoring to energy harnessing, predictive analytics and personalized health care, and so on. Among various fields, agriculture is one important field having maximum scope of implementation and investment. The main aim of this book chapter is to furnish all the details related to applications of IoT in the field of agriculture. This includes the details related to data collection, types of sensors used, deployment details, data access through cloud. It also covers details related to various communication technologies used in IoT such as Bluetooth, LoRaWAN, LTE, 6LowPAN, NFC, RFID etc. And above all, the chapter focuses on the significance of IoT on agronomics, agricultural engineering, crop production and livestock production. This chapter is a decade survey conducted to study the contribution of IoT in the field of agriculture. Around 40 research papers for the duration 2008-2018 are collected from peer reviewed journals and conferences. The collected articles are analyzed to provide relevant information required for the various end users. Springer Nature Switzerland AG 2020. -
A Deep Assessment of ML Based Procedure used as a Classifiers in the Clinical Field
In the unexpectedly evolving panorama of healthcare technology, the mixing of data mining and machine mastering gives exceptional possibilities for the advancement of sickness prediction fashions. This research paper introduces a unique Machine Learning Smart Health Procedure designed to harness the predictive energy of those era for forecasting illnesses. By meticulously reading ancient healthcare facts, which includes affected individual signs and symptoms and effects, this system leverages cutting-edge algorithms which includes Nae Bayes, Support Vector Machines (SVM), and neural networks to expect capacity health problems with accelerated accuracy. This method now not best pursuits to facilitate early and specific evaluation but also strives to noticeably enhance affected individual care and treatment consequences. Through the strategic utility of statistics mining and prediction analysis in the healthcare area, our proposed machine demonstrates the capacity to revolutionize conventional diagnostic techniques, developing a proactive and predictive healthcare model more plausible and effective than ever earlier than. 2024 IEEE. -
A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data
Several economists and social scientists have held a longstanding fascination with the practice of stock market prediction. As the stock market is essentially uncontrollable chaos, many experts believe that trying to predict it is futile. Due to the complexity of the numerous factors, accurate stock price predictions are notoriously difficult to achieve. While the market behaves more like a scale than a voting machine over the long run, its behavior may be predicted with some certainty. Information from Twitter is used into the algorithm. In this proposed method, a convolutional extreme learning machine model with kernel support was introduced (CKELM). To improve feature extraction and data classification, the CKELM model builds on the KELM's hidden layer by adding convolutional and subsampling layers. The convolutional layer and the subsampling layer do not employ the gradient technique to fine-tune their parameters because some designs worked well with random weights. When compared to popular models like CNN and KELM, The proposed model fares quite well, with an accuracy of around 98.3 percent. 2023 IEEE. -
A Deep Ensemble Framework for DDoS Attack Recognition and Mitigation in Cloud SDN Environment
Much research has been done in the recent past on the absolute shift of Internet infrastructure in order to make it more significantly programmable, configurable and make it more conveniently feasible. Software Defined Networking (SDN) forms the basis for this absolute shift in Internet infrastructure. When you look at the benefits of an SDN-based cloud environment they are monumental. Namely, network traffic control and elastic resource management. The SDN-based cloud environment becomes susceptible to cyber threats, especially like that of Distributed Denial of Service (DDoS) attacks and other cyber-attacks that perturb the SDN-based cloud environment. Hence, automated Machine Learning (ML) models are an efficient way to protect against these cyber-attacks. This research will develop a deep learning-based ensemble model for DDoS attack detection and classification (DLEM-DDoS) in a cloud environment. Long Short-Term Memory (LSTM), 1-D Convolutional Neural Networks (1D-CNN) and Gated Recurrent Unit (GRU) are the three DL models integrated into an ensemble model that classifies the incoming packet by majority voting classifiers. Network traffic data including source and destination IP addresses, packet and byte counts, packet and byte rates, flow duration, protocol types and port numbers are fed into the DLEM-DDoS model. This model preprocesses this data by converting categorical values (like protocol types) into numerical values and removing any missing values. Once collected and preprocessed, the data is fed into deep learning models (LSTM, 1D-CNN, GRU) within the framework for analysis. Finally, in this research using the DLEM-DDoS technique an efficient DDoS attack mitigation scheme in an SDN-based cloud environment is demonstrated. The report shows comprehensive stimulations as well as a superiority into the current approaches in terms of several measures. 2024 S. Annie Christila and R. Sivakumar. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
A deep learning approach in early prediction of lungs cancer from the 2d image scan with gini index
Digital Imaging and Communication in Medicine (DiCoM) is one of the key protocols for medical imaging and related data. It is implemented in various healthcare facilities. Lung cancer is one of the leading causes of death because of air pollution. Early detection of lung cancer can save many lives. In the last 5years, the overall survival rate of lung cancer patients has increased, due to early detection. In this paper, we have proposed Zero-phase Component Analysis (ZCA) whitening and Local Binary Pattern (LBP) to enhance the quality of lung images which will be easy to detect cancer cells. Local Energy based Shape Histogram (LESH) technique is used to detect lung cancer. LESH feature extracts a suitable diagnosis of cancer from the CT scans. The Gini coefficient is used for characterizing lung nodules which will be helpful in Computed Tomography (CT) scan. We propose a Convolutional Neural Network (CNN) algorithm to integrate multilayer perceptron for image segmentation. In this process, we combined both traditional feature extraction and high-level feature extraction to classify lung images. The convolutional neural network for feature extraction will identify lung cancer cells with traditional feature extraction and high-level feature extraction to classify lung images. The experiment showed a final accuracy of about 93.27%. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A Deep Learning Approach to Clinical Decision Support in Heart Disease Diagnosis
Heart disease is the dominant cause of extinction worldwide, emphasizing the importance of early diagnosis and treatment planning. In this article, the authors developed a Clinical Decision Support System (CDSS) for heart disease prediction using deep learning techniques. This system will suggest a neural network architecture with Leaky ReLU as the activation function in the hidden layers and Sigmoid as the activation function in the output layer for binary classification. The configuration neural network is enhanced across three to nine hidden layers. The proposed approach is evaluated using accuracy as the measurable value on five multivariate datasets. By integrating advanced deep learning with clinical expertise, this study aims to enhance predictive accuracy, contributing to reduced heart disease mortality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Deep Learning Approach to Phishing Detection Using BiLSTM with an Attention Mechanism
Phishing sites are a serious cyber threat as they trick users into revealing sensitive personal data. Conventional detection techniques, including rule-based systems and blocklists, cannot cope with changing phishing tactics. In this paper, a new approach to phishing detection is introduced using a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism. The suggested model learns and examines URL-based features and identifies forward and backward relationships in data, enhancing classification accuracy. A 30,000 URL-tagged dataset is utilized to train the model, which is then optimized with the help of methods like sequence tokenization, embedding layers, dropout regularization, and class weight balancing to counter data imbalance issues. The BiLSTM layer processes sequential information about URLs in a bidirectional manner, whereas the attention mechanism applies weights to important features differently to ensure the model pays attention to the most critical elements of phishing URLs. The model was tested based on standard performance metrics and has attained an astounding accuracy of 99.22%, precision of 99.1%, recall of 99.3%, and an F1-score of 99.2%, surpassing the traditional approach like Logistic Regression. The model indicates good generalization ability and is possible to be applied in real-time in web security systems. In the future, the use of dynamic data analysis and large datasets will be applied to improve further the detection efficiency and responsiveness against the new emerging phishing attacks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Deep Learning Method for Autism Spectrum Disorder
The present study uses deep learning methods to detect autism spectrum disorder (ASD) in patients from global multi-site database Autism Brain Imaging Data Exchange (ABIDE) based on brain activity patterns. ASD is a neurological condition marked by repetitive behaviours and social difficulties. A deep learning-based approach using transfer learning for automatic detection of ASD is proposed in this study, which uses characteristics retrieved from the intracranial brain volume and corpus callosum from the ABIDE data set. T1-weighted MRI scans provide information on the intracranial brain volume and corpus callosum. ASD is detected using VGG-16 based on transfer learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Deep Learning Method for Classification in Brain-Computer Interface
Neural activity is the controlling signal used in enabling BCI to have direct communication with a computer. An array of EEG signals aid in the selection of the neural signal. The feature extractors and classifiers have a specific pattern of EEG control for a given BCI protocol, which is tailor-made and limited to that specific signal. Although a single protocol is applied in the deep neural networks used in EEG-based brain-computer interfaces, which are being used in the feature extraction and classification of speech recognition and computer vision, it is unclear how these architectures find themselves generalized in other area and prototypes. The deep learning approach used in transferring knowledge acquired from the source tasks to the target tasks is called transfer learning. Conventional machine learning algorithms have been surpassed by deep neural networks while solving problems concerning the real world. However, the best deep neural networks were identified by considering the knowledge of the problem domain. A significant amount of time and computational resources have to be spent to validate this approach. This work presents a deep learning neural network architecture based on Visual Geometry Group Network (VGGNet), Residual Network (ResNet), and inception network methods. Experimental results show that the proposed method achieves better performance than other methods. 2023 IEEE. -
A Deep Learning Methodology CNN-ADAM for the Prediction of PCOS from Text Report
Text categorization is a popular piece of work in natural language processing (NLP) and machine learning, and Convolutional Neural Networks (CNNs) can be used effectively for this purpose. Although CNNs are traditionally associated with computer vision tasks, they have been adapted and applied successfully to text classification problems. In the proposed study Convolutional Neural Networks (CNNs) with adam optimization algorithm plays a crucial role in detecting PCOS words from sonographic text reports. 2023 IEEE. -
A Deep Learning Model for Information Loss Prevention from Multi-Page Digital Documents
World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers' sensitive information of a reputed TI company securing it's one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization's information security monitoring team. 2013 IEEE. -
A Deep Learning-Based BCI System for Emotion Classification Using EEG Signals
Electroencephalography-based Brain-Computer Interfacing (EEG-BCI) technologies allow for effortless interaction between external hardware and the human brain through monitoring its electric signals. These systems rely on EEG recordings, which provide non-invasive and real-time neural information through electrodes placed on the scalp. To advance emotion-recognizing efficiency and accuracy, this study proposes a deep learning-based method that can extract valuable temporal and spatial information from EEG signals. The proposed model includes the use of a Graph Convolution Network (GCN) for learning spatial relationships between different EEG channels to model the data in graph form and gain features through that modelling. A Convolutional Autoencoder (CAE) is then used to compress data to low dimensions and to reconstruct it so that major features are not ignored. Furthermore, the model uses an Attention-based Bidirectional Gated Recurrent Unit (ABiGRU) for temporal classification, which can emphasize the most important time steps in both backwards and forward directions. Two standard datasets are employed to test the developed approach. The DEAP dataset is used for emotion recognition with a binary response, and SEED is used with multi-class classification. The model attains great results of 98.12% accuracy on DEAP and 97.58% on SEED datasets. The very high performances show the efficacy of the model for decoding emotional states from EEG signals and very strong potential for real-time emotion recognition in affective computing and BCI. 2026 Seventh Sense Research Group. -
A Deep Learning-Enhanced Adaptive Intrusion Detection Framework for Real-Time Cyber Security Threat Analysis
The rapidly evolving nature of cyber-attacks significantly reduces the effectiveness of conventional intrusion detection systems (IDS) that rely on static rules and signatures. This work presents an adaptive deep learning- based intrusion detection framework designed to maintain reliable performance in real-time environments affected by concept drift. The proposed approach integrates one-dimensional convolutional neural networks (1D-CNN) for local feature interaction learning with a bidirectional long short-term memory (BiLSTM) network to model sequential network traffic behavior. To address evolving attack patterns, a sliding-window-based incremental learning mechanism is employed, enabling continuous model adaptation to recent traffic characteristics. The model is trained using cross-entropy loss optimized with the Adam optimizer, while dropout regularization is applied to reduce overfitting and ensure fast convergence. To enhance transparency and analyst trust, explainable artificial intelligence techniques are incorporated, including SHAP-based feature attribution and an attention mechanism for interpreting temporal dependencies. Experimental evaluation on labeled network traffic data demonstrates stable convergence, consistent detection accuracy under changing traffic conditions, and improved robustness compared to non-adaptive baseline models. These results confirm the effectiveness and practical applicability of the proposed framework for real-time and interpretable cybersecurity intrusion detection. 2026 IEEE. -
A deep understanding of virtual reality's role in human resource management, with a specific focus on recruitment
In a rapidly evolving recruitment landscape, virtual reality (VR) emerges as a transformative tool for human resource management (HRM). This chapter delves into the profound impact VR can have on attracting, assessing and onboarding talent. Furthermore, VR services have been already included in other areas where it has been shown its essence to be useful for those industries. The current study explores how VR simulations can provide immersive experiences, allowing candidates to virtually step into the workplace and interact with potential colleagues and tasks. This fosters a deeper understanding of the role and company culture, leading to better-matched hires and improved candidate experience. This chapter also examines the potential of VR for skills assessments, offering a more realistic and engaging evaluation process compared to traditional methods. Finally, this study discusses the challenges and considerations for integrating VR into HRM practices, ensuring a successful implementation of this innovative technology in the realm of recruitment. 2025 The Authors. All rights reserved. -
A Design of Agricultural Robotics for the use of Sowing and Planting
Agricultural robots is always getting better to deal with problems like population growth, fast urbanization, fierce competition for high-quality goods, worries about protecting the environment, and a lack of skilled workers. This in-depth study looks at the main uses of farming robotic systems, covering jobs like preparing the land, sowing, planting, treating plants, gathering, estimating yields, and phenotyping. Each robot is judged on how it moves, what it will be used for, whether it has sensors, a robotic arm, or a computer vision program, as well as its development stage and where it came from. The study finds trends, possible problems, and things that stop business growth by looking at these shared traits. It also shows which countries are putting money into studying and developing (R&D) for these products. The study points out four important areas - movement systems as a whole sensor, computer vision computer programs, and communication technologies - that need more research to make smart agriculture better. The results make it clear that spending money on farming robotic systems can pay off in the long run by helping with things like accurate yield estimates and short-term benefits like keeping an eye on the harvest. 2024 IEEE.