Frequency: Quarterly E- ISSN: 2250-2904 P- ISSN: 2349-3453 Abstracted/ Indexed in: Ulrich's International Periodical Directory, Google Scholar, SCIRUS, Genamics JournalSeek, EBSCO Information Services
Quarterly published in print and online "Inventi Impact: Artificial Intelligence" publishes high quality unpublished as well as high impact pre-published research and reviews catering to the needs of researchers and professionals. It focuses on multidimensional aspects of artificial intelligence, particularly - artificial intelligence and philosophy, automated reasoning and inference, cognitive aspects of AI, commonsense reasoning, intelligent robotics, etc.
3D gestural interaction provides a powerful and natural way to interact with computers using the hands and body for a variety of\r\ndifferent applications including video games, training and simulation, and medicine. However, accurately recognizing 3D gestures\r\nso that they can be reliably used in these applications poses many different research challenges. In this paper, we examine the state\r\nof the field of 3D gestural interfaces by presenting the latest strategies on how to collect the raw 3D gesture data from the user and\r\nhow to accurately analyze this raw data to correctly recognize 3D gestures users perform. In addition, we examine the latest in 3D\r\ngesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of\r\n3D gestural interaction. Finally, we present ideas for future research in this thriving and active research area....
3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations....
Most of the mechanical systems in industries are made to run through induction motors (IM).\nTo maintain the performance of the IM, earlier detection of minor fault and continuous monitoring\n(CM) are required. Among IM faults, bearing faults are considered as indispensable because of its\nhigh probability incidence nature. CM mainly depends upon signal processing and fault detection\ntechniques. In recent decades, various methods have been involved in detecting the bearing fault\nusing machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing\ntechnology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection\nand the detailed study about the role of ML and AI to detect the bearing fault, the present study is\nperformed. A comprehensive study is conducted by considering various diagnosis methods from ML\nand AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the\ndierence between the diagnosis approach and their effectiveness in detecting an IM bearing fault.\nIt is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted\nfeatures are used to train the algorithm. The application is extended by comparing the result of ML\nand AI, and then explaining the specific purpose of use....
This paper presents a comparative study between optimization-based andmarket-based approaches used for solving theMultirobot\r\ntask allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used\r\nto find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches\r\nwere extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained\r\nMRS applications that include extended number of tasks and robots. Finally, a comparative study is implemented between the two\r\napproaches and the results show that the optimization-based approach outperforms themarket-based approach in terms of optimal\r\nallocation and computational time....
We propose a novel Cultural Algorithm for the representation of mitochondrial population in mammalian cells as an autonomous\r\nculture. While mitochondrial dysfunctions are highly associated with neurodegenerative diseases and related disorders, an\r\nalternative theoretical framework is described for the representation of mitochondrial dynamics. A new perspective of bioinspired\r\nalgorithm is produced, combining the particle-based Brownian dynamics simulation and the combinatorial representation of\r\nmitochondrial population in the lattice, involving the optimization problem of ATP production in mammalian cells....
Activity selection is critical for the smart environment and Cyber-Physical Systems (CPSs) that can provide timely and intelligent services, especially as the number of connected devices is increasing at an unprecedented speed. As it is important to collect labels by various agents in the CPSs, crowdsourcing inference algorithms are designed to help acquire accurate labels that involve highlevel knowledge. However, there are some limitations in the algorithm in the existing literature such as incurring extra budget for the existing algorithms, inability to scale appropriately, requiring the knowledge of prior distribution, difficulties to implement these algorithms, or generating local optima....
Recently, device-to-device (D2D) communications have been attracting substantial attention\nbecause they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to\nconventional cellular communications. They are also indispensable for the mobile caching network,\nwhich is an emerging technology for next-generation mobile networks. We investigate a cellular\noverlay D2D network where a dedicated radio resource is allocated for D2D communications to\nremove cross-interference with cellular communications and all D2D devices share the dedicated\nradio resource to improve the spectral efficiency. More specifically, we study a problem of radio\nresource management for D2D networks, which is one of the most challenging problems in D2D\nnetworks, and we also propose a new transmission algorithm for D2D networks based on deep\nlearning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector\nindicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the\ntrained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that\nthe accuracies of the proposed deep learning based transmission algorithm reach about 85% Approximately 95% in\nspite of its simple structure due to the limitation in computing power....
Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature vectors of two feature points is used as a feature matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points. Quantitative experimental results achieved on FVC2002 DB1 and FVC2000 DB1 public domain fingerprint databases demonstrate the good performance and feasibility of the proposed framework in terms of achieving average recognition rates of 95% and 92.5% for FVC2002 DB1 and FVC2000 DB1 databases, respectively....
Human motion prediction aims at predicting the future poses according to the motion dynamics given by the sequence of history\nposes. We present a new hierarchical static-dynamic encoder-decoder structure to predict the human motion with residual CNNs.\nSpecifically, to better mine the law of the motion, a new residual CNN-based structure, v-CMU, is proposed to encode not only the\nstatic information but also the dynamic information. Based on v-CMU, a hierarchical structure is proposed to model different\ncorrelations between the different given poses and the predicted pose. Moreover, a new loss function combining the static and\ndynamic information is introduced in the decoder to guide the prediction of the future poses. Our framework features two-folds:\n(1) more effective dynamics mined due to the fusion of information of the poses and the dynamic information between poses and\nthe hierarchical structure; (2) better decoding or prediction performance, thanks to the mid-level supervision introduced by the\nnew loss function considering both the static and dynamic losses. Extensive experiments show that our algorithm can achieve\nstate-of-the-art performance on the challenging G3D and FNTU datasets. The code is available at https://github.com/liujin0/\nSDnet....
The complexity and the dynamism of oil spillages make it difficult for planners and responders to produce robust plans towards\ntheir management. There is need for an understanding of the nature, sources, impact and responses required to prevent or control\ntheir occurrence. This paper develops an intelligent hybrid system driven by Sugeno-Type Adaptive Neuro Fuzzy Inference\nSystem (ANFIS) for the identification, extraction and classification of oil spillage risk patterns. Dataset consisting of 1008\nrecords was used for training, validation and testing of the system. Result of sensitivity analysis shows that Cause, Location\nand Type of spilled oil have cumulative significance of 85.1%. Optimal weights of Neural Network (NN) were determined via\nGenetic Algorithm with hybrid encoding scheme. The Mean Squared Error (MSE) of NN training is 0.2405. NN training,\nvalidation and testing results yielded R > 0.839 in all cases indicating a strong linear relationship between each output and\ntarget data. Rule pruning was performed with support (15%) and confidence (10%) minimum thresholds and antecedent-size of\n3. The performance of the ANFIS was evaluated with eight different types of membership functions (MFs) and two learning\nalgorithms. The model with triangular MF gave the best performance among all other given models while hybrid-learning\nalgorithm performed better than back propagation algorithm. The ANFIS model reported in the paper adopted triangular MF\nand hybrid learning algorithm for the predication and classification of oil spillage risk patterns. Average training and testing\nMSE of the model is 0.414315 and 0.221402 respectively. The knowledge mining results show that ANFIS based systems\nprovide satisfactory results in the prediction and classification of oil spillage risk patterns....
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