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  • Semantic Information Extraction from Multi-Corpora Using Deep Learning
  • Abstract Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity…
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  • Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms
  • Abstract This study aims to empirically analyze teaching-learning-based optimization (TLBO) and machine learning algorithms using k-means and fuzzy c-means (FCM) algorithms for their individual performance evaluation in terms of clustering and classification. In the first phase, the clustering (k-means and FCM) algorithms were employed independently and the clustering accuracy was evaluated using different computational measures. During the second phase, the non-clustered data obtained from the first phase were preprocessed with TLBO. TLBO was performed using k-means (TLBO-KM) and FCM (TLBO-FCM) (TLBO-KM/FCM) algorithms. The objective function was determined by considering both minimization and maximization criteria. Non-clustered data obtained from the first phase…
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  • Fusion of Infrared and Visible Images Using Fuzzy Based Siamese Convolutional Network
  • Abstract Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared (IR)/visible (VS) images. Dissimilarities in various kind of features in these images are vital to preserve in the single fused image. Hence, simultaneous preservation of both the aspects at the same time is a challenging task. However, most of the existing methods utilize the manual extraction of features; and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image. Therefore, this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images. Firstly, fuzzification of two IR/VS…
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  • A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data
  • Abstract Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the “curse of dimensionality” that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a…
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  • Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction
  • Abstract Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction systems. However, previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye images. This makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics: 1) users’ continuous movements, 2) various lighting conditions, and 3) a limited amount of available data. To address these issues, we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze direction. The proposed system captures the user using multiple cameras with…
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  • Personality Detection Using Context Based Emotions in Cognitive Agents
  • Abstract Detection of personality using emotions is a research domain in artificial intelligence. At present, some agents can keep the human’s profile for interaction and adapts themselves according to their preferences. However, the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject. The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior. In our daily life, humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input. This paper presents a conceptual personality model…
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  • Convolutional Neural Network Based Intelligent Handwritten Document Recognition
  • Abstract This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system…
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  • Intelligent Deep Learning Based Automated Fish Detection Model for UWSN
  • Abstract An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces. It has paved the way for new opportunities that can address questions relevant to diversity, uniqueness, and difficulty of marine life. Underwater Wireless Sensor Networks (UWSNs) are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions. In this scenario, it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies. Several models have been…
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  • MLA: A New Mutated Leader Algorithm for Solving Optimization Problems
  • Abstract Optimization plays an effective role in various disciplines of science and engineering. Optimization problems should either be optimized using the appropriate method (i.e., minimization or maximization). Optimization algorithms are one of the efficient and effective methods in providing quasi-optimal solutions for these type of problems. In this study, a new algorithm called the Mutated Leader Algorithm (MLA) is presented. The main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated leader. In addition to information about the best member of the population, the mutated…
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  •   Views:47       Downloads:41        Download PDF
  • Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles
  • Abstract With recent advances made in Internet of Vehicles (IoV) and Cloud Computing (CC), the Intelligent Transportation Systems (ITS) find it advantageous in terms of improvement in quality and interactivity of urban transportation service, mitigation of costs incurred, reduction in resource utilization, and improvement in traffic management capabilities. Many traffic-related problems in future smart cities can be sorted out with the incorporation of IoV in transportation. IoV communication enables the collection and distribution of real-time essential data regarding road network condition. In this scenario, energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban computing. With this motivation,…
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  •   Views:42       Downloads:37        Download PDF
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