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Hybrid intelligent systems for information retrieval
\"With the digitization of the entire world, the data gets generated at a fast pace. Extracting and retrieving useful information and patterns becomes challenging as information is not always in structured form. Therefore, there is a need for the design and development of Hybrid Intelligent Information Retrieval System. Broadly four approaches are covered in this book for design and development of Intelligent Information Retrieval: 1. Evolutionary approach for optimal Information Retrieval, 2. Novel Matching functions for Information Retrieval, 3. Natural Language Processing for Modern Information Retrieval 4. Development of Semantically enhanced Web Information Retrieval\"-- Provided by publisher.
Book
Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review
In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.
Journal Article
An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface
Wheelchair control requires multiple degrees of freedom and fast intention detection, which makes electroencephalography (EEG)-based wheelchair control a big challenge. In our previous study, we have achieved direction (turning left and right) and speed (acceleration and deceleration) control of a wheelchair using a hybrid brain–computer interface (BCI) combining motor imagery and P300 potentials. In this paper, we proposed hybrid EEG-EOG BCI, which combines motor imagery, P300 potentials, and eye blinking to implement forward, backward, and stop control of a wheelchair. By performing relevant activities, users (e.g., those with amyotrophic lateral sclerosis and locked-in syndrome) can navigate the wheelchair with seven steering behaviors. Experimental results on four healthy subjects not only demonstrate the efficiency and robustness of our brain-controlled wheelchair system but also indicate that all the four subjects could control the wheelchair spontaneously and efficiently without any other assistance (e.g., an automatic navigation system).
Journal Article
Soft Computing Systems
This volume focuses on research developments on intelligent systems in a hybrid environment and its applications in image processing, Internet modelling and data mining. The contributions presented were accepted for the Second International Conference on Hybrid Intelligent Systems (HIS '02).
eBook
Training deep Boltzmann networks with sparse Ising machines
The increasing use of domain-specific computing hardware and architectures has led to an increasing demand for unconventional computing approaches. One such approach is the Ising machine, which is designed to solve combinatorial optimization problems. Here we show that a probabilistic-bit (p-bit)-based Ising machine can be used to train deep Boltzmann networks. Using hardware-aware network topologies on field-programmable gate arrays, we train the full Modified National Institute of Standards and Technology (MNIST) and Fashion MNIST datasets without downsampling, as well as a reduced version of the Canadian Institute for Advanced Research, 10 classes (CIFAR-10) dataset. For the MNIST dataset, our machine, which has 4,264 nodes (p-bits) and about 30,000 parameters, can achieve the same classification accuracy (90%) as an optimized software-based restricted Boltzmann machine with approximately 3.25 million parameters. Similar results are achieved for the Fashion MNIST and CIFAR-10 datasets. The sparse deep Boltzmann network can also generate new handwritten digits and fashion products, a task the software-based restricted Boltzmann machine fails at. Our hybrid computer performs a measured 50 to 64 billion probabilistic flips per second and can perform the contrastive divergence algorithm (CD-
n
) with up to
n
= 10 million sweeps per update, which is beyond the capabilities of existing software implementations.
Probabilistic-bit-based Ising machines implemented on field-programmable gate arrays can be used to train artificial intelligence networks with the same performance as software-based approaches while using fewer model parameters.
Journal Article
Hybrid Algorithms for Solving the Algebraic Eigenvalue Problem with Sparse Matrices
Hybrid algorithms for solving the partial generalized eigenvalue problem for symmetric positive definite sparse matrices of different structures by hybrid computers with graphic processors are proposed, coefficients for the efficiency of the algorithms are obtained, and approbation of the developed algorithms for test and practical problems is carried out.
Journal Article
A probabilistic approach for calibration time reduction in hybrid EEG–fTCD brain–computer interfaces
Background
Generally, brain–computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such calibration requirements may be difficult to fulfill especially for patients with disabilities. In this paper, we introduce a probabilistic transfer learning approach to reduce the calibration requirements of our EEG–fTCD hybrid BCI designed using motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. The proposed approach identifies the top similar datasets from previous BCI users to a small training dataset collected from a current BCI user and uses these datasets to augment the training data of the current BCI user. To achieve such an aim, EEG and fTCD feature vectors of each trial were projected into scalar scores using support vector machines. EEG and fTCD class conditional distributions were learnt separately using the scores of each class. Bhattacharyya distance was used to identify similarities between class conditional distributions obtained using training trials of the current BCI user and those obtained using trials of previous users.
Results
Experimental results showed that the performance obtained using the proposed transfer learning approach outperforms the performance obtained without transfer learning for both MI and flickering MR/WG paradigms. In particular, it was found that the calibration requirements can be reduced by at least 60.43% for the MI paradigm, while at most a reduction of 17.31% can be achieved for the MR/WG paradigm.
Conclusions
Data collected using the MI paradigm show better generalization across subjects.
Journal Article
Effect of Simulated Gastric Acid on Surface Characteristics and Color Stability of Hybrid CAD/CAM Materials
Hybrid computer-aided design and computer-aided manufacturing (CAD/CAM) materials have gained prominence in restorative dentistry due to their advantageous mechanical and esthetic properties; however, their long-term performance may be adversely affected by acidic oral environments, such as those associated with gastroesophageal reflux disease (GERD). This in vitro study aimed to investigate the effects of simulated gastric acid exposure on the surface roughness, gloss, color stability, and microhardness of two hybrid CAD/CAM materials: Vita Enamic and Cerasmart. Standardized rectangular specimens (2 mm thickness) were prepared and polished using a clinically relevant intraoral protocol. Baseline measurements were obtained for surface roughness, gloss, color change (ΔE), and Vickers microhardness. All specimens were then immersed in hydrochloric acid (pH 1.2) for 24 h to simulate prolonged gastric acid exposure, after which the same properties were re-evaluated. Post-immersion analysis revealed significant increases in surface roughness and reductions in gloss and microhardness for both materials (p < 0.05), with Vita Enamic demonstrating greater susceptibility to degradation. Color changes remained below the clinically perceptible threshold, with no significant differences between materials. These findings highlight the potential vulnerability of hybrid CAD/CAM materials to acidic environments and underscore the importance of careful material selection in patients predisposed to acid-related challenges.
Journal Article
Soil type identification model using a hybrid computer vision and machine learning approach
Computer vision and its technologies are being used in the area of agricultural automation to identify, locate, and track targets for further image processing. Mostly, agricultural production has been highly dependent on natural resources like soil, water, and other related natural minerals from the soil. Soil classification is a way of arranging soils that have similar characteristics into groups. Identifying and classifying soils has a great role to play in agricultural productivity as it helps to provide relevant information which aids agricultural experts to recommend the type of crop best suited for a specific type of soil. This study mainly concentrated on classifying soil types such as clay soil, loam soil, sandy soil, peat soil, silt soil, and chalk soil. The soil images were collected from Amhara region at different locations by using a sony digital camera. To reduce image noise due to handshake we used a camera stand or arm to avoid other types of noises like environmental lighting effects and shadow. Once the dataset was collected, preprocessing such as resizing and gamma correction was performed to remove noise from the images, and contrast adjustment was also performed. Experimental research was applied as a general methodology and the experiment was conducted based on two approaches. The first approach used CNN as an end-to-end classifier and the second used a hybrid approach which used CNN as a feature extractor and SVM as a classifier. When CNN was used as an end-to-end classifier, a classification accuracy of 88% was achieved, whereas when the hybrid approach which used CNN as feature extractor and SVM as classifier was employed, a classification accuracy of 95% was achieved. Finally, we conclude that the hybrid approach is better than that of the End-to-End classification using our proposed model.
Journal Article
Pulse Burst Generation and Diffraction with Spatial Light Modulators for Dynamic Ultrafast Laser Materials Processing
A pulse burst optical system has been developed, able to alter an energetic, ultrafast 10 ps, 5 kHz output pulse train to 323 MHz intra-burst frequency at the fundamental 5 kHz repetition rate. An optical delay line consisting of a beam-splitting polariser cube, mirrors, and waveplates transforms a high-energy pulse into a pulse burst, circulating around the delay line. Interestingly, the reflected first pulse and subsequent pulses from the delay line have orthogonal linear polarisations. This fact allows independent modulation of these pulses using two-phase-only Spatial Light Modulators (SLM) when their directors are also aligned orthogonally. With hybrid Computer Generated Holograms (CGH) addressed to the SLMs, we demonstrate simultaneous multi-spot periodic surface micro-structuring on stainless steel with orthogonal linear polarisations and cylindrical vector (CV) beams with Radial and Azimuthal polarisations. Burst processing produces a major change in resulting surface texture due to plasma absorption on the nanosecond time scale; hence the ablation rates on stainless steel with pulse bursts are always lower than 5 kHz processing. By synchronising the scan motion and CGH application, we show simultaneous independent multi-beam real-time processing with pulse bursts having orthogonal linear polarisations. This novel technique extends the flexibility of parallel beam surface micro-structuring with adaptive optics.
Journal Article