International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Introduce a new dynamic classifying algorithm in this paper to recognize and monitor fish activity to simultaneously better comprehend their synapomorphies. The pre-trained Mask Regional Convolutional Neural Network (Mask-R-CNN) is trained using a set of test models extracted from recorded video recording. The approach suggested subsequently yields well-enhanced feature vectors. The system’s automatic fish detection and tracking capabilities are improved, enhancing underwater investigation for supervising ecological biodiversity. The publicly accessible field-truth dataset assesses recall, F1-score, and classification and tracking precision. Utilizing current tracking R-CNN algorithms like Lowest Output Sum of Siamese Mask (SiamMask), Sequential Non-Maximum Suppression (Seq-NMS), and Squared Errors (MOSSE), comparative performance testing is conducted. In comparison to Siam-Mask (84%), Seq-NMS (78%) and MOSSE (75%), more than 120 of 170 specific bream were detected using the pre-trained Mask-R-CNN of the proposed algorithm (87%). This pre-trained Mask R-CNN system was used in the evaluation, and it was discovered that detection and tracking accuracy had increased significantly. This suggests that the ocean ecosystem could benefit from applying the proposed approach for management of ecology.

This study investigates a fuzzy controller technique for autonomous robot navigation in both the static and dynamic environmental conditions and an excessive number of pathways to the destination. The design and implementation of a novel obstacle avoidance technique for autonomous robots are developed using the fuzzy controller-based multi-agent system. This method allows the Robot to identify dynamic or static unidentified objects while directing the Robot to prevent collisions and advance toward the objective. The Robot is capable of moving in a variety of environments. The Robot may communicate and travel in dynamic space by perceiving its surroundings and pursuing a free-collision route. This study covers creating a multi-agent system that includes fuzzy logic to regulate the robotic movements along a path reactive for effective Navigation. This project aims to develop an algorithm that allows the Robot to do distinct tasks to accomplish a unified objective, autonomous Navigation in a slightly unfamiliar environment. Under such a situation, the usage of a multi-agent system is advantageous. As a result, we created a framework made up of four agents responsible for sensing, Navigation, dynamic, and static obstacle avoidance. These agents communicate with one another via a coordinating mechanism.

A study of geographic information has become a significant field of concentrate in software engineering because of the expansion of much information created by electronic gadgets fit together geographic data from people, like advanced mobile phones and GPS gadgets. Area facts allow a deeper understanding of users’ options and actions by bridging the gap between the physical and digital worlds. This expansion of wide geo-spatial datasets has inspired studies into novel recommender systems that aim to make users’ travels and social interactions easier. This huge amount of data generated by these devices has prompted an increase in the number of research activities and procedures aimed at breaking down and recovering useful data based on these large datasets. The aim of this challenge is to study GPS directions from a variety of people, as well as examine and apply computational strategies to recover useful data from those directions, useful data from GPS directions, including areas of interest and people’s proximity, and then create an instrument for information representation. This paper demonstrates how data mining techniques is used to recover valuable information from spatial data. As well as how such data can be useful in understanding people and areas within a district. Depending on the outcomes, we recommend a HITS (Hypertext Induced Topic Search) based POI recommendation calculation that can take into account the effect of social connections when recommending POIs to individual users.

Face recognition and head posture estimation have aroused a lot of academic interest recently since the inherent information improves the performance of face-related applications such as face alignment, augmented reality, healthcare applications, and emotion detection. The proposed work explores the challenges of identifying people and determining head posture. An analysis of the features produced at intermediate layers by limiting the number of kernals is performed and improved the performance of detecting the person. In addition, the learned features are sent to forest trees in order to determine the exact head attitude of the detected person. The proposed Forest CNN (FCNN) architecture is tested for head pose estimation methods on the Pointing 04 and Facepix benchmark databases.

The clinical diagnosis of heart disease in most situations is based on a difficult amalgamation of pathological and clinical information. Because of this complication, there is a significant level of curiosity among many diagnostic healthcare professionals and researchers who are keenly interested in the efficient, accurate, and early-stage forecasting of heart disease. Deep Learning Algorithms aid in the prediction of heart disease. The main focus of this paper is to develop a method for predicting heart disease through Modified Rough K means$++$ (MRK$++$) clustering along with the Restricted Boltzmann Machine (RBM). This paper is categorized into two modules: (1) Propose a clustering component based on Modified Rough K-means$++$; (2) disease prediction based on RBM. The input Cleveland dataset is clustered using the stochastic probabilistic rough k-means$++$ clustering technique in the module for clustering. The clustered data is acquired and used in the RBM, and this hybrid structure is then used in the heart disease forecasting module. Throughout the testing procedure, the most valid result is chosen from the clustered test data, and the RBM classifier that correlates to the nearest cluster in the test data is based on the smallest distance or similar parameters. Furthermore, the output value is used to predict heart disease. There are three different types of experiments that are performed: In the first experiment comprises modifying the rough K-means$++$ clustering algorithm, the second experiment evaluates the classification result, and the third experiment suggests hybrid model representation. When the Hybrid Modified Rough k-means$++$ - RBM model is compared with any single model, it provides the highest accuracy.

This paper presents an automated Kannada subtitle generator from Kannada video which is implemented to assist people with auditory problems for watching videos. Henceforth the subtitle generation has become an important task for supporting such special people and it integrates an audio extraction and a speech recognition module. Three phases of the proposed technique were implemented, such as extracting audio from video, Recognition of Speech and Generation of Subtitle. An adaptive speech recognition module is implemented AMFCC for feature extraction which was an alternative to the most commonly used FFT. Hankel transform which was similar to FFT, but includes no elementary particles such as FFT. In addition to it, in the decoder acoustic module, such as Adaptive Hidden Markov Model using the Baum-Welch algorithm is utilized instead of a Viterbi algorithm to reduce the computational time and memory usage. The text file from the speech recognition module is rendered to synchronize the missing offset with the video using parallel processing by defining the start time, the end time, the delay time. Best outcomes are demonstrated by the experimental results of the proposed technique with 98.4% of accuracy compare with existing techniques. The proposed technique which gives 3.8% better accuracy performance compare with existing technique i.e. MFCC, DNN and CNN.

Clustering is one of the essential branches of data mining, which has numerous practical uses in real-time applications.The Kernel K-means method (KK-means) is an extended operative clustering algorithm. However, this algorithm entirely dependent on the kernel function’s hyper-parameter. Techniques that adequately explore the search spaces are needed for real optimization problems and to get optimal answers. This paper proposes an enhanced kernel K-means clustering by employing a pigeon optimization algorithm in clustering. The suggested algorithm finds the best solution by tuning the kernel function’s hyper-parameter and alters the number of clusters simultaneously. Based on five biological and chemical datasets, the results acquired the potential result from the suggested algorithm that is compared to other approaches based on intra-cluster distances and the Rand index. Moreover, findings confirm that the suggested KK-means algorithm achieves the best computation time. The proposed algorithm achieves the necessary support for data clustering.

The future holds the possibility of hospitals sharing medical images obtained through non-invasive systems to patients remotely. The advent of cloud and the storage and deployment of medical healthcare images in the cloud has resulted in the increased need for application of Cryptographic techniques to protect them from unauthorized access and malicious attacks. The Digital Imaging and Communication in Medicine (DICOM) standard is more compatible across medical imaging instruments globally. The pixel data of DICOM images requires more privacy and security. A novel ECDS based cryptographic approach is suggested to encrypt the original DICOM image as well as the ROI pixel data extracted from DICOM images. Results computed experimentally have proved that medical image encryption via ECDH is more robust, efficient and faster than existing medical image encryption schemes.

Fluid - Level measurement is required in recognizing the state variable of a Level processing plant for monitoring the level deviations in most the industrial plants. This issue has mostly been addressed by conventional methods using level sensors and level transmitters. However, the cost associated with these mechanisms can be reduced. It has been identified the chances with the applications machine learning approach in this scenario. Such a solution first of its kind is proposed and tried in this article. Gabor feature selection is established with SMOTE (Synthetic Minority Oversampling Technique) to overcome the imbalanced data set to improve the accuracy performance using Weka software. This paper aims to construct a level predictor based on machine learning algorithms and to demonstrate the training and testing performance validated with more accuracy. At this juncture, we consider the fluid level images for analyzing and predicting the level measurement by building supervised models with selected classifiers like IBk, JRip, J48, and Random Forest machine learning algorithms. The java-implemented tool Weka is subjected to get the maximum accuracy of 76.4286% and weighted average ROC (Receiver Operator Characteristics) values of 0.903. Hence such smart measurements using Machine learning algorithms, part of Artificial intelligence provide us a vital role in measuring the level parameter in less time using image processing filters.