13 research outputs found
Facilitating enhanced user access through Palm-leaf manuscript digitization — Challenges and solutions
No full textObject Sub-Categorization and Common Framework Method using Iterative AdaBoost for Rapid Detection of Multiple Objects
No full text185-191Object detection and tracking in real time has numerous applications and benefits in various fields like survey, crime detection etc. The idea of gaining useful information from real time scenes on the roads is called as Traffic Scene Perception (TSP). TSP actually consists of three subtasks namely, detecting things of interest, recognizing the discovered objects and tracking of the moving objects. Normally the results obtained could be of value in object recognition and tracking, however the detection of a particular object of interest is of higher value in any real time scenario. The prevalent systems focus on developing unique detectors for each of the above-mentioned subtasks and they work upon utilizing different features. This obviously is time consuming and involves multiple redundant operations. Hence in this paper a common framework using the enhanced AdaBoost algorithm is proposed which will examine all dense characteristics only once thereby increasing the detection speed substantially. An object sub-categorization strategy is proposed to capture the intra-class variance of objects in order to boost generalisation performance even more. We use three detection applications to demonstrate the efficiency of the proposed framework: traffic sign detection, car detection, and bike detection. On numerous benchmark data sets, the proposed framework delivers competitive performance using state-of-the-art techniques
Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction
No full textCompanies in a wide variety of industries use the customer churn prediction (CCP) process to keep their current clientele happy. Insurance companies need to be able to forecast churn to enhance the potency and functionality of deep learning methods. Deep learning techniques have a significant impact on improving and forecasting customer retention. Numerous studies employ standard machine learning and Deep Learning strategies to enhance customer retention, despite the fact that these strategies have a number of accuracy issues. In light of this need, this piece is dedicated to the development of a stacked bidirectional long short-term memory (SBLSTM) and RNN model for Arithmetic Optimisation Algorithm (AOA) in CCP. The proposed AOA-SBLSTM-RNN model intends to proficiently forecast the occurrence of Customer Churn in the Insurance industry. Initially, the AOA model performs pre-processing to transform the original data into a useful format. In addition, the SBLSTM-RNN model is used to distinguish between churning and non-churning customers. To improve the CCP outcomes of the SBLSTM-RNN model, an optimal Hyperparameters tuning process using Improved Gravitational Search Optimization Algorithm (IGSA) is used in this study. In this work, Three Health Insurance datasets were used to evaluate performance, and four sets of experiments were conducted. The Measures of true churn, false churn, specificity, precision, and accuracy are employed to assess the efficacy of the proposed approach. Experimental result shows that the Ensemble Deep Learning model AOA-SBLSTM-RNN with IGSA produces accuracy value of 97.89 and 97.67 on dataset 2 and dataset 1. which is better and had higher predictability levels in compared with all other models
Product Recommendation Systems Based on Customer Reviews Using Machine Learning Techniques
No full textDigital twin and IOT technology for secure manufacturing systems
No full textThe Digital Twin (DT) method offers new concepts for choosing the context of information technologies and smart production systems. This research focuses on low-price, extremely effective defect diagnosis methods, low-efficiency, high-cost devices to obtain timely feedback and accurate fault detection results, and secure manufacturing systems. The data structure, control plane, and output units are the three components of the manufacturing system that creates a data link between the virtual model using Micro-Electro-Mechanical (MEM) devices and the Zigbee wireless transmission system in the database layer. This study acquired DT information from the control plane using the Internet of Things (IoT) through sensors for secure manufacturing information. It separates and calls the pertinent data by the attribute processor and transfers it to the outcome units. To produce the classification and outcomes of work build features information, the evaluation method analysis the output nodes that split the test set and learning group using a dynamic database. The hybrid IoT with DT technology to examine the consequences of defects detected efficiently predicted and secures the manufacturing system
Gesture Recognition for Enhancing Human Computer Interaction
No full text438-443Gesture recognition is critical in human-computer communication. As observed, a plethora of current technological
developments are in the works, including biometric authentication, which we see all the time in our smartphones. Hand
gesture focus, a frequent human-computer interface in which we manage our devices by presenting our hands in front of a
webcam, can benefit people of different backgrounds. Some of the efforts in human-computer interface include voice
assistance and virtual mouse implementation with voice commands, fingertip recognition and hand motion tracking based on
an image in a live video. Human Computer Interaction (HCI), particularly vision-based gesture and object recognition, is
becoming increasingly important. Hence, we focused to design and develop a system for monitoring fingers using extreme
learning-based hand gesture recognition techniques. Extreme learning helps in quickly interpreting the hand gestures with
improved accuracy which would be a highly useful in the domains like healthcare, financial transactions and global busines
Effective Predictive Maintenance to Overcome System Failures—A Machine Learning Approach
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