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Computer Assisted Unsupervised Extraction and Validation Technique for Brain Images from MRI
Magnetic Resonance Imaging (MRI) of human is a developing field in medical research because it assists in considering the brain anomalies. To identify and analyze brain anomalies, the research requires brain extraction. Brain extraction is a significant clinical image handling method for quick conclusion with clinical perception for quantitative assessment. Automated methods of extracting brain from MRI are challenging, due to the connected pixel intensity information for various regions such as skull, sub head and neck tissues. This paper presents a fully automated extraction of brain area from MRI. The steps involved in developing the method to extract brain area, includes image contrast limited using histogram, background suppression using average filtering, pixel region growing method by finding pixel intensity similarity and filling discontinuity inside brain region. Twenty volumes of brain slices are utilized in this research method. The outcome is achieved by this method is approved by comparing with manually extracted slices. The test results confirm the performance of this strategy can effectively section brain from MRI. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Computer implemented method for generating notification during call without using internet in wireless communication /
"Patent Number: 201911047977, Applicant: Asik Rahaman Jamader.
The present invention disclosure is related to a computer implemented method for generating notification during calls without usinginternet in wireless communication. The present invention provides an internet free method for advance notification system from the caller. This advance notification generated by calling time on calling partys devise. The incoming calls getting an assigned text as subject of calling from a caller. Caller party can recognize the caller identity and a picture on the incoming telephone call." -
Computer modelling of trace SO2 and NO2 removal from flue gases by utilizing Zn(ii) MOF catalysts
SO2 and NO2 capture and conversion have been investigated via density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations using a novel hydrogen-bonded 3D metal-organic framework (MOF) containing a Zn(ii) centre and a partially fluorinated (polar -CF3) long-chain dicarboxylate ligand with a melamine (basic -NH2) co-ligand. Initially, computational single-component isotherms have been determined for SO2 and NO2 gases. These simulations have shown exothermic adsorption enthalpies of ?36.4 and ?28.6 kJ mol?1 for SO2 and NO2, respectively. They have also indicated that SO2 has a high affinity for polar -CF3 and basic -NH2 binding sites of the ligand in the framework pore walls. The lower adsorption capacity of NO2 compared with SO2 is due to weaker electrostatic interactions with the framework. Furthermore, MOF adsorbent selectivity for removing trace amounts of SO2 and NO2 in flue gases has been estimated through the co-adsorption of ternary gas mixtures (SO2/CO2/N2 and NO2/CO2/N2). Together with DFT, the climbing image nudged elastic band (CI-NEB) method has been used for investigating the plausible mechanisms for HbMOF1 catalyzed cycloadditions of SO2 and NO2 with epoxides leading to the formation of cyclic sulphites and nitrates, respectively. 2023 The Royal Society of Chemistry. -
Computer simulation of diesel fueled engine processes using matlab and experimental investigations on research engine
The depletion of conventional fuel source at a fast rate and increasing environmental pollution have motivated extensive research in combustion modeling and energy efficient engine design. In the present work, a computer simulation incorporating progressive combustion model using thermodynamic equations has been carried out using MATLAB to evaluate the performance of a diesel engine. Simulations at constant speed and variable load have been carried out for the experimental engine available in the laboratory. For simulation, speed and Air/Fuel ratios, which are measured during the experiment, have been used as input apart from other geometrical details. A state-of-the-art experimental facility has been developed in-house. The facility comprises of a hundred horsepower water cooled eddy current dynamometer with appropriate electronic controllers. A normal load test has been carried out and the required parameters were measured. A six gas analyzer was used for the measurement of NOx, HC, CO2, O2, CO and SOx. and a smoke meter was used for smoke opacity. The predicted Pressure-Volume (PV) diagram was compared with measurements and found to match closely. It is concluded that the developed simulation software could be used to get quick results for parametric studies. Copyright 2017 ASME. -
Computer Vision Based Automatic Margin Computation Model for Digital Document Images
Margin, in typography, is described as the space between the text content and the document edges and is often essential information for the consumer of the document, digital or physical. In the present age of digital disruption, it is customary to store and retrieve documents digitally and retrieve information automatically from the documents when necessary. Margin is one such non-textual information that becomes important for some business processes, and the demand for computing margins algorithmically mounts to facilitate RPA. We propose a computer vision-based text localization model, utilizing classical DIP techniques such as smoothing, thresholding, and morphological transformation to programmatically compute the top, left, right, and bottom margins within a digital document image. The proposed model has been experimented with different noise filters and structural elements of various shapes and size to finalize the bilateral filter and lines and structural elements for the removal of noises most commonly occurring due to scans. The proposed model is targeted towards text document images and not the natural scene images. Hence, the existing benchmark models developed for text localization in natural scene images have not performed with the expected accuracy. The model is validated with 485 document images of a real-time business process of a reputed TI company. The results show that 91.34 % of the document images have conferred more than 90 % IoU value which is well beyond the accuracy range determined by the company for that specific process. 2023, Crown. -
Computer Vision Based Indian Sign Language Recognition Using Deep Learning
Speech is a human default and unique modality for language development and communication which is essential for memory and overall cognitive development. Excellency in language permits a child to be extrovert enriching the development of cognitive and psychosocial skills; whereas, for auditory deprived children, the misalignment of the brain and ear makes them impotent to communicate with the society which creates a central dogma that hearing-loss is a disability which further ignores their psycho-social identity. To fill such gaps and make their community more freewheeling in India, Indian Sign Language (ISL) - a complete language with its own linguistic and verbal elements was framed. Though ISL is appropriate and absolute in every linguistic approach, lack of prerequisite and proficiency enforces dedicated teachers to teach the curriculum through contrived signs for the sake of convenience that not only diminishes the distinctiveness of ISL but also dislodges the idea of learning their mother tongue. This creates an imbalance in the analogous learning of communication and curriculum language. In order to balance the level in learning, effective vision-based days of the week ISL model is developed through Convolution Neural Network (CNN) architecture which boasts independent learning of ISL. The proposed model comprises of six stages: dataset creation, preprocessing, splitting dataset into train, validation and test, applying various types of image augmentation techniques according to split, constructing CNN model for feature extraction and classification and finally evaluating the result through evaluation measures. Initially, an image dataset is created as there is a scarcity of standard ISL datasets in internet sources. The images are created on vision-based technique to avoid of carrying additional superfluous hardware gadgets for human computer interaction. -
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Computer-aided drug design and green synthesis of novel pyrazole analogues as potential sars-cov-2 main protease inhabitors against anti-covid-19 protein targets /
Patent Number: 202141026750, Applicant: Santhosh Govindaraju.
COVID-19 pandemic has significantly increased high deaths, infectivity and hospitalizations. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a foremost problem in the world presently. Currently, all research institutions and pharmaceutical industries are keen in developing new vaccines and more effective drugs that could inhibit the SARS-CoV-2 virus and help in the treatment of patients. This provoked us to design medicinally effective drug candiadates which can prevent the SARS-CoV-2 virus of the infected patients. -
Computer-based Intelligence and Security
There is a massive increase in the incidence of cyberattacks day by day in the modern enterprise environment. Although humans are behind this task due to the rapid growth in the incidence human intervention is unable to control this. Therefore, something more than human intervention is required to have a check over it. With cyberattacks evolving at a rapid pace and with the increase in the use of devices in todays world, Artificial Intelligence (AI) and Machine Learning (ML) can help to have a check over cybercrime incidences, automate the process of detection of threat, and handle these in a better way as compared to the conventional methods used for controlling cybercrime and cyberattack. AI and ML has shown good results in data information security as these technologies are capable of analyzing a large and wide variety of data and can track threats related to cyberattacks that may cause phishing attacks. As these technologies are capable of learning and improving from past experiences, they can even predict and tell the new variety of attacks that may occur in the coming days. This chapter describes the use of AI in controlling cyberattacks and cybercrime and the expert views on this matter. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Computerized grading of brain tumors supplemented by artificial intelligence
For effective diagnosis of health conditions, there is a need to process medical images to obtain meaningful information. The diagnosis of brain tumors begins with magnetic resonance imaging (or MRI) scan. This is followed by segmentation of the medical images so obtained which can prove cumbersome if it were to be performed manually. Determining the best approach to do segmentation remains challenge among multiple computerized approaches. This paper combines both the identification and classification of tumors from the MRI results and is backed by a cloud-based framework to provision the same. The phase of extraction of features includes the utilization of a Hadoop framework and Gabor filter along with variations in terms of orientation and scale. Artificial bee colony algorithm and support vector machine classifier have been used to designate the degree of optimal features and categorize the same. The grading of brain tumors from MRI images can be fulfilled by the aforementioned approach. The said approach is believed to deliver promising results in terms of accuracy, which has also been verified experimentally. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Concentration-dependent luminescence characterization of terbium-doped strontium aluminate nanophosphors
The present investigation describes the synthesis of luminescent terbium-doped strontium aluminate nanoparticles emitting bright green light, which were synthesized through a solid-state reaction method assisted by microwave radiation. Various samples containing different concentrations of Tb were synthesized, and an analysis of their structural and morphological features was conducted using powder x-ray diffraction, Fourier transform infrared spectroscopy and field emission scanning electron microscopy. The band gaps of the samples were determined utilizing the KubelkaMunk method. The quenching mechanism observed was identified to be due to dipoledipole interaction using the Dexter theory. The optimized sample with a terbium concentration of 4at.% has a luminescence lifetime of 1.05 ms with 20.62% quantum efficiency. The results of this study indicate that the terbium-doped strontium aluminate fluorescent nanoparticles exhibit promising potential for a wide range of applications, including bioimaging, sensing and solid-state lighting. 2024 John Wiley & Sons Ltd. -
Concept Drift Detection for Social Media: A Survey
The research over information retrieval from social media data has progressed for streaming data since the last decade. Recently, academic researchers have witnessed users' changing topics, trends, and intent on social media. This change of information with time takes into account the temporal attribute for real-time data, and thus, advances in this domain are exponentially growing. Although concept drift is still not explored due to a shortage of available datasets, concept drift for social media is minimally explored. This manuscript makes attempts to identify the types of concept drift for social media data, discuss the historical perspective of concept drift on social media, and enlist the possible research directions. 2021 IEEE. -
Concept Mapping of Issues of Students Life in University
The undergraduate student body forms around 85.9% of the total number of students enrolled in India, which is a significant population. It has become imperative to understand the issues that these students face during their undergraduate years as a precursor to developing mechanisms and strategies to enable student progress, both academically and developmentally. This study aimed at developing a concept map to outline the various aspects and issues of the undergraduate students life in India utilizing the concept mapping method. Data from participants (n = 141) at different phases was analysed resulting in 49 unique life issues and aspects and 8 clusters. The emerging issues have relevance and implications for teachers, parents, administrators and other stakeholders in structuring and developing services targeted towards undergraduate students in India. 2015, National Academy of Psychology (NAOP) India. -
Conceptual comprehension analysis of a student using soft cosine measure
Knowledge is the substantial wealth of a man and he possesses an innate thirst to acquire it. Knowledge embodies facts or ideas acquired through study, investigation and observation or experience. In this context, technology with its varied techniques comprising endless algorithms in natural language processing (NLP) plays an imperative role in the pursuit of knowledge. Inferences thus gathered are a clear pointer to the content teaching of students. Soft cosine measure algorithm is used in this analysis process to provide an answer regarding the grasping ability of each student with optimum learner participation and creativity. After each lecture, students have to upload their corresponding notes and this in turn would be compared with the teacher's lecture notes. The soft cosine computation gives individual results, on how much each student has comprehended a concept. This new methodology is a much awaited contribution of the educational field. 2022 Author(s). -
Conceptual framework of artificial intelligence in human resource management /
Patent Number: 202221003373, Applicant: Rishu Roy.
Developing a conceptual framework for using artificial intelligence in human resource management is the goal of this study (HRM). The six main human resource management theory characteristics are integrated with AI technology's possible applicability. Human resource management encompasses a variety of facets, including human resource strategy and planning, recruiting, training and development, performance assessment, and management of employee relations. -
Conclusion
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Conclusion
We all can agree at one point: the COVID-19 pandemic has had a massive and unanticipated impact on all the lives of all tourists. The global tourism and hospitalityindustry has been heavily damaged, but the societal impact cannot be overlooked. Consumer behavior, and ultimately consumer spending, has been and will continue to change, and company planning must adapt to these new realities. The major findings of this edited book in the contexts of tourism, destination recovery and crisis management thus have value for the industry and for researchers seeking to understand these changes. Chapter 1 analyses evolution of tourism and hospitality during times of crisis and how these businesses might rebound. Academics in the field of tourism and hospitality can use this collection to understand the most recent studies on crises and recovery. The impact of the COVID-19 crisis on tourism and hospitality was examined in several published pieces. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Conclusion
Digital data produced through data-processing algorithms has fundamental advantages of transportability, proficiency, and accuracy; but on the other hand, the data thus produced brings in several redundancies. To solve this challenging problem with data transmission in network surroundings, research on information security and forensics provides efficient solutions that can shield the privacy, reliability, and accessibility of digital information from malicious intentions. Despite two decades of rigorous research, multicarrier communications still suffer from high complexity and low convergence, which have an immense practical impact. It is also more challenging to ensure proper transmission of multimodal data. Novel techniques have been proposed that can effectively abate these problems and provide good symbol error rate performance. The singularity expansion method provides a superior way to identify targets. This type of method may be useful for addressing contemporary problems faced by radar and antenna researchers. 2021 John Wiley & Sons Ltd. -
Conclusion
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Conclusion and future research directions
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