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Real hypersurfaces of complex space forms satisfying FischerMarsden equation
Let M be a real hypersurface of a complex space form of constant curvature c. In this paper, we study the hypersurface M which admits a nontrivial solution to FischerMarsden equation, that is, the induce metric g of M satisfies Hessg(?) = (?g?) g+ ?Sg, where ? is a nontrivial function. We prove that there does not exist a complete Hopf real hypersurface in a non-flat complex space form satisfying FischerMarsden equation. Finally, we show that a complete real hypersurface with A?= ??, ?? 0 , of a complex Euclidean space Cn satisfying FischerMarsden equation is locally congruent to a sphere or S1R2n-2. 2021, Universitdegli Studi di Ferrara. -
Real time conversion of sign language to speech and prediction of gestures using artificial neural network
Sign language is generally used by the people who are unable to speak, for communication. Most people will not be able to understand the Universal Sign Language (unless they have learnt it) and due to this lack of knowledge about the language, it is very difficult for them to communicate with mute people. A device that helps to bridge a gap between mute persons and other people forms the crux of this paper. This device makes use of an Arduino Uno board, a few flex sensors and an Android application to enable effective communication amongst the users. Using the flex sensors, gestures made by the wearer is detected and then according to various pre-defined conditions for the numerous values generated by the flex sensors, corresponding messages are sent using a Global System for Mobile(GSM) module to the wearer?s android device, which houses the application that has been designed to convert text messages into speech. The GSM module is also used to send the sensor inputs to a cloud server and these values are taken as input parameters into the neural network for a time series based prediction of gestures. The system is designed to be a continually learning device and improve reliability by monitoring every individual?s behaviour at all times. 2018 The Authors. Published by Elsevier B.V. -
Real- coded genetic algorithm for optimal ordering and pricing in segmented market with freshness and price- dependent demand, advance payment, and trade credit
We study the inventory model of a product having demand affected by its freshness and selling price in the context of supply chains, freshness, and price-dependent demand, where the supplier is dominated, as is usually the case with producers of agri-based products. The product when received exhibits heterogeneous quality. The retailer subdivides the product into quality-dependent segments, which he sells simultaneously during the selling season at prices commensurate with the quality. The sizes of the segments are random variables. The supplier can get a partial advance payment from the dominant retailer by providing a discount on the partial advance with the proportion of partial payment as well as the epoch of partial payment chosen by the supplier. The retailer can, at times, choose the advance proportion to be paid, and the discounted price which we call the endogenous case but takes a loan for the advance payment from a financer, whom he repays with interest when a delayed payment period permitted by the supplier gets over. The retailer in turn gets some time before he can pay his remaining dues and pays the supplier a fraction of the cost price commensurate with the quality of the product. Lost sales shortages are considered for fresh items. The model is aimed at obtaining optimal values of ordering amount, selling price, and discounted selling prices for the various segments. It is also aimed to obtain advance proportion and the discount on advance payment for the endogenous case. Real-coded genetic algorithm (RCGA) and Hybrid RCGA have been used to obtain the optimal solutions for numerical examples and the results are compared. Finally, sensitivity analysis to evaluate the effects of changes in some parameter values has also been presented. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Real-Time Application of Document Classification Based on Machine Learning
This research has been performed, keeping a real-time application of document (multi-page, varying length, scanned image-based) classification in mind. History of property title is captured in various documents, recorded against the said property in all the countries across the world. Information of the property, starting from ownership to the conveyance, mortgage, refinance etc. are buried under these documents. This is by far a human driven process to manage these digitized documents. Categorization of the documents is the primary step to automate the management of these documents and intelligent retrieval of information without or minimal human intervention. In this research, we have examined a popular, supervised machine learning technique called, SVM (support vector machine) with a heterogeneous data set of six categories of documents related to property. The model obtained an accuracy of 88.06% in classifying over 988 test documents. 2020, Springer Nature Switzerland AG. -
Real-Time Application with Data Mining and Machine Learning
Data mining and machine learning are the most expressive research and application domain. All real-time application directly or indirectly depends on data mining and machine learning. There are manyrelevantfields, like data analysis in finance,retail, telecommunications sector, analyzing biological data, otherscientific uses, and intrusiondetection.The most expressive research and application domain is data mining and machine learning. Data mining and machine learning are used in all real-time applications, whether directly or indirectly. Data analysis in finance, retail, telecommunications, biological data analysis, extra scientific applications, and intrusion detection are just a few exampleswhere it can be used. Because it captures a lot of data from sales, client purchase histories, product transportation, consumption, and services, DM has a lot of applications in the retail industry. It's only logical that the amount of data collected will continue to climb as the Internet's accessibility, cost, and popularity increase. In the retail industry, DM assists in the detection of customer buying behaviors and trends, resulting in improved customer service and increased customer retention and satisfaction. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Real-time architectural efforts in building a social network using NOSQL databases
Relational database management systems (RDBMS) today are the predominant technology for storing structured data in web and business applications. Along with the increasing size of the datasets, the number of accesses and operations performed increases. This growth, enhanced by the proliferation of social networks, led to a depletion of traditional relational databases that were commonly used to solve a wide range of problems. -
Real-Time Cyber-Physical Risk Management Leveraging Advanced Security Technologies
Conducting an in-depth study on algorithms addressing the interaction problem in the fields of machine learning and IoT security involves a meticulous evaluation of performance measures to ensure global reliability. The study examines key metrics such as accuracy, precision, recall, and F1 scores across ten scenarios. The highly competitive algorithms showcase accuracy rates ranging from 95.5 to 98.2%, demonstrating their ability to perform accurately in various situations. Precision and recall measurements yield similar information about the model's capabilities. The achieved balance between accuracy and recovery, as determined by the F1 tests ranging from 95.2 to 98.0%, emphasizes the practical importance of data transfer in the proposed method. Numerical evaluation, in addition to an analysis of overall performance metrics, provides a comprehensive understanding of the algorithm's performance and identifies potential areas for improvement. This research leads to advancements in the theoretical vision of machine learning for IoT protection. It offers real-world insights into the practical use of robust models in dynamically changing situations. As the Internet of Things environment continues to evolve, the study's results serve as crucial guides, laying the foundation for developing strong and effective security systems in the realm of interaction between virtual and material reality. The Author(s) 2024. -
Real-Time Data Fusion Algorithm for Multi-Modal Environmental Sensor Networks Using Kalman Filtering and IoT Integration
Fusion of heterogeneous, noisy, and asynchronous multimodal data streams is essential to environmental sensor networks, given the computational, memory, and energy constraints of IoT devices. This paper introduces a real-time data fusion framework integrating hybrid adaptive Kalman filtering, distributed edge computing, and seamless IoT connectivity. The proposed framework incorporates three key innovations. First, a hybrid adaptive Kalman filtering mechanism employs the Unscented Kalman Filter (UKF) sigma-point technique, augmented with Long Short-Term Memory (LSTM) neural networks and fuzzy logic, for dynamic noise correction and robust nonlinear state estimation. Second, a three-tier distributed fusion architecture employs edge computing for local data processing, reducing network latency, communication overhead, and energy consumption. Third, a modular Service-Oriented Architecture enables seamless IoT integration, remote data access, and adaptive system reconfiguration. The framework also incorporates multi-criteria fault detection that combines chi-square tests, sequential probability ratio tests, and LSTM-based predictive compensation during sensor failures. Experimental validation employed 150 sensors for urban air-quality monitoring, industrial facility surveillance, and water-quality measurement. Sensor nodes utilized ESP32-S3 microcontrollers with LoRa communication, while Raspberry Pi 4 devices served as edge gateways connected to AWS IoT infrastructure. Compared to standard Kalman filtering, the proposed method achieved: (i) 25.2% reduction in root mean square estimation error, (ii) 41% energy reduction driven by 70% communication savings through predictive transmission and edge compression, (iii) sub-100 ms end-to-end latency representing 54% improvement, and (iv) robust performance maintaining below 10% degradation at 15% sensor failure rates. 2026 Taylor & Francis Group, LLC. -
Real-time detection and response: How AI is shaping the future of hate speech
This chapter traverse how artificial intelligence is transforming hate speech detection by facilitating real-time detection and response. It focuses on the technical aspects of using machine learning. Natural language processing and deep learning models to identify and mark spiteful content. This chapter also discusses the advantages, obstacles, and ethical considerations associated with using AI to moderate online speech. The ultimate objective is to provide insights into how AI is changing the landscape of content moderation on platforms around the world. 2025, IGI Global Scientific Publishing. All rights reserved. -
Real-Time Fabric Defect Detection Using a Lightweight YOLOv8 Model on Edge Devices
The detection of defects in fabric is a critical process for maintaining quality standards and reducing economic losses in the textile industry. Traditional inspection methods, which rely on human operators, are often slow, inconsistent, and susceptible to error. This research introduces an innovative solution that harnesses Edge AI and deep learning to facilitate real-time, on-site defect detection. We developed a highly efficient and lightweight model based on the YOLOv8 architecture, specifically tailored for deployment on resource-constrained edge devices like NVIDIA Jetson Nano or Raspberry Pi. Through a process of comprehensive literature analysis and domain expertise, a compact, high-precision model was trained on diverse fabric defect datasets. To ensure optimal performance on edge hardware, we employed advanced optimization techniques like quantization and pruning. The primary offering of the work are threefold: the making of a streamlined YOLOv8-based model for fabric defect detection, a comparative analysis of various edge inference strategies, and a proposed system architecture for real-time embedded deployment. This study effectively demonstrates the practical application of advanced AI to solve longstanding challenges in textile quality control. Future efforts will be directed towards extensive real-world operational testing and exploring localized Model Training with Federated Learning enhancement. 2025 IEEE. -
Real-Time Fire Detection Through the Analysis of Surveillance Videos
The Forest Fire Detection System is an intelligent system that can detect forest fires and alert authorities in real-time. It uses a YOLOv5 deep learning algorithm to process live video feeds captured by a web camera which is trained with the sizable dataset of inputs to locate the fire accurately, making it an ideal choice for real-time fire detection in the forest. Upon detecting a fire, the system sends an email alert to a designated email address, containing a picture of the fire and location information. The email alert system is built using the standard SMTP protocol, which ensures that the message is delivered to the recipient in a timely and reliable manner. The system is also equipped with a speaker that triggers an alarm upon detecting a fire. The alarm is designed to alert people in the vicinity of the fire so that they can take the necessary action. It is activated using the Pygame library, a collection of Python modules specifically crafted for game development across multiple platforms. Overall, the Forest Fire Detection System is a fast, efficient, and accurate system that can help prevent the spread of forest fires. It is an intelligent system that can detect fires quickly and send alerts to authorities, giving them the information they need to take the necessary action to control the fire. The system is built using a web camera, a computer, and a speaker, making it easy to install and maintain. 2024 IEEE. -
Real-Time Football Match Analysis with Region-Independent Player Tracking Using Deep Learning
This project explores the application of AI in analyzing football games by tracking players across the entire video frame. Unlike traditional methods that focus on limited areas, the system here uses YOLO for detecting players everywhere in the frame and ByteTrack to follow them throughout the match. The goal is to get a clearer picture of each player's movement, particularly their speed and distance covered. Manual methods or GPS-based tools often fall short in providing quick, reliable data, especially in real-time scenarios. This study compensates for camera motion and adjusts for different viewpoints to get more accurate tracking results. As a way to test player identity consistency, the system randomly assigns popular player names to different tracking IDs. Experiments on public match videos show that the system can keep track of players even during zoom-ins, crowding, or partial visibility. Code snippets show how the model works in practice. Our results show that using full-frame AI tracking gives coaches more detailed tactical insights and helps them develop more effective strategies. 2025 IEEE. -
Real-time human action prediction using pose estimation with attention-based LSTM network
Human action prediction in a live-streaming videos is a popular task in computer vision and pattern recognition. This attempts to identify activities in an image or video performed by a human. Artificial intelligence(AI)-based technologies are now required for the security and human behaviour analysis. Intricate motion patterns are involved in these actions. For the visual representation of video frames, conventional action identification approaches mostly rely on pre-trained weights of various AI architectures. This paper proposes a deep neural network called Attention-based long short-term memory (LSTM) network for skeletal based activity prediction from a video. The proposed model has been evaluated on the BerkeleyMHAD dataset having 11 action classes. Our experimental results are compared against the performance of the LSTM and Attention-based LSTM network for 6 action classes such as Jumping, Clapping, Stand-up, Sit-down, Waving one hand (Right) and Waving two hands. Also, the proposed method has been tested in a real-time environment unaffected by the pose, camera facing, and apparel. The proposed system has attained an accuracy of 95.94% on BerkeleyMHAD dataset. Hence, the proposed method is useful in an intelligent vision computing system for automatically identifying human activity in unpremeditated behaviour. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
Real-Time Implementation of Deep Learning Model for Polyp Classification and Segmentation in Medical Imaging
Real-time deep learning models for polyp identification and segmentation in medical imaging. Recognising the limits of current database systems for real-time applications, the research focusses on creating a deep learning model capable of recognising crucial picture components to aid in precise polyp categorisation. The suggested methodology is intended for realtime, practical healthcare and diagnostic applications that need quick polyp detection via preliminary colonoscopy testing. Performance investigation demonstrates that ResNet50 and EfficientNet B2 outperform other models, implying that they are suitable for real-world application and optimal outcomes. 2025 Bharati Vidyapeeth, New Delhi. -
Real-time Litter Recognition Using Improved YOLOv4 Tiny Algorithm
Littered roads have become a familiar sight in India. The main reason is the increasing population and inefficient waste disposal system. Since garbage collectors cannot pick litter in all the places, there is a need for an efficient way to detect it. Hence, a machine learning-based object detection model is used. In this, we have applied an improved YOLOv4-Tiny algorithm to detect the garbage, classify it and make the detection process easier on custom datasets. We have improved the algorithm in terms of the object prediction time, this is done by replacing a max pooling layer with one of two layers present in a fully connected layer. When an input is given, the algorithm detects the litter in the image with a bounding box around it along with the label and confidence score. The proposed model reduces the prediction time by 0.517 milliseconds less than the original algorithm employed which concludes that the object is predicted faster. 2022 IEEE. -
Real-Time Monitoring and Anomaly Detection in Cloud-Based IoT Networks
IoT devices have seen explosive growth, causing a data explosion, which makes it almost impossible to manage and monitor these networks. This has led to a demand for solutions providing real-time monitoring and detection of anomalies in cloud-based Internet of Things (IoT) networks. In cybersecurity, the term real-time monitoring denotes the ongoing analysis of data and network performance to detect potential problems. It allows for the identification of anomalies and possible weaknesses in the system. In contrast, anomaly detection is the process of finding deviations from what is considered normal for a system or data. Within cloud-based IoT networks, this might involve identifying abnormal traffic patterns or unusual activity from devices. To solve these challenges, we propose a cloud computing and machine learning-based solution. IoT devices generate a massive amount of data, which is processed in real-time and stored on the cloudbased infrastructure. Many machine learning algorithms analyze these data algorithms to identify anomalies or threats to security. This solution provides an active early warning detection of security breaches from the network management perspective, along with timely response in the case of abnormal behaviour. It will ultimately result in improved cloud-based networks for IoT devices with regard to reliability, security, and performance. This solution can be a key factor in driving the mass implementation of IoT in various sectors. 2025 IEEE. -
Real-Time Network Monitoring: Integrating Machine Learning and Custom Packet Sniffer Using Python
The growth in network traffic and the increasing complexity of cyber threats necessitate robust systems for detecting anomalies that indicate security breaches. This research presents a methodology for finding anomalies in packets sent when the connection is established. It uses a machine learning model and a packet sniffer. It captures Transmission Control Protocol (TCP), User Datagram Protocol (UDP), IPv4, and Internet Control Message Protocol (ICMP) segments to predict if any anomalies are present (Sanders in Practical packet analysis: using wireshark to solve real-world network problems, No Starch Press, San Francisco, 2017). An unsupervised learning model is utilized. The presence of unlabeled data to enhance the real-time prediction using isolation forest model. The data collected by packet sniffer undergoes avoiding null values and encoding addresses, and thus an isolation forest is used so that it predicts if anomalies are present using binary trees. The performance is evaluated on the basis of metrics like accuracy, precision, and F1-score (Goutte and Gaussier in European conference on information retrieval, Springer, New York, 2005). The result illustrates the model is accurate in predicting whether anomalies are present. Future work is focused on enhancing the models capabilities with more protocols and an active defense mechanism. The study addresses real-world deployment challenges especially in heterogeneous environments like IoT-based networks. While isolation forest is getting high accuracy, future research could explore hybrid approaches combining traditional statistical methods with deep learning techniques for enhanced industry applications (Ahmed et al. in J Netw Comput Appl 60:1931, 2016). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Real-Time Safety Monitoring for Construction Sites Using RFID and Visual Recognition Technologies
The integrated automated safety monitoring system for construction sites utilizes RFID, Wi-Fi, and vision-based recognition systems to enhance worker safety and ensure adherence to safety regulations. This system combines sophisticated components such as RFID tags and Raspberry Pi, solenoid locks, servo motors, and PIR sensors to provide an exhaustive solution. RFID technology is applied to assign unique tags to each worker, facilitating accurate tracking and identification The Wi-Fi and visual recognition components improve the system's functionalities, enabling wireless connectivity instantaneous data transmission, and verification of appropriate safety gear application. Solenoid locks and servo motors ensure regulated access to hazardous areas, responding to authenticated safety compliance records. PIR sensors sense motion, differentiating between authentic presence and mere nearness. The methodology outlines the necessary hardware and software criteria, procedures for system initialization, evaluation phases, server connectivity setup, access control enactment, and session closure protocols. It details the seamless integration and verification of hardware components, backend connectivity, identity and safety adherence verification, data encoding, and session termination processes. This research aims to upgrade safety surveillance in construction environments, boosting productivity, accuracy, and security. It also underscores the need for further adaptability to various construction settings to advance greater uptake and continuous improvement in workplace safety protocols. 2025 IEEE.