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Computationally efficient wavelet domain solver for florescence diffuse optical tomography
Estrogen induced proliferation of mutant cells is a growth signal hallmark of breast cancer. Fluorescent molecule that can tag Estrogen Receptor (ER) can be effectively used for detecting cancerous tissue at an early stage. A novel targetspecific NIRf dye conjugate aimed at measuring ER status was synthesized by ester formation between 17-? estradiol and a hydrophilic derivative of ICG, cyanine dye, bis-1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. In-vitro studies provided specific binding on ER+ve [MCF-7] cells clearly indicating nuclear localization of the dye for ER+ve as compared to plasma level staining for MDAMB-231. Furthermore, cancer prone cells showed 4.5-fold increase in fluorescence signal intensity compared to control.; A model of breast phantom was simulated to study the in-vivo efficiency of dye with the parameters of dye obtained from photo-physical and in-vitro studies. The excitation (754 nm) and emission (787 nm) equation are solved independently using parallel processing strategies. The results were obtained by carrying out wavelet transformation on forward and the inverse data sets. An improvisation of the Information content of system matrix was suggested in wavelet domain. The inverse problem was addressed using LevenbergMarquardt (LM) procedure with the minimization of objective function using Tikhonov approach. The multi resolution property of wavelet transform was explored in reducing error and increasing computational efficiency. Our results were compared with the single resolution approach on various parameters like computational time, error function, and Normalized Root Mean Square (NRMS) error. A model with background absorption coefficient of 0.01 mm-1 with anomalies of 0.02 mm-1 with constant reduced scattering of 2.0 mm for different concentration of dye was compared in the result. The reconstructed optical properties were in concurrence with the tissue property at 787 nm. We intend our future plans on in-vivo study on developing a complete instrumentation for imaging a target specific lipophilic dye. Springer International Publishing Switzerland 2014. -
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 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. -
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. -
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). -
Congestion Avoidance in Vehicular Ad Hoc Network MAC Layer Using Harmony SearchModified Laying Chicken Algorithm (HS-MLCA)
To address congestion in the MAC layer and enhance overall performance, the HS-MLCA is proposed. This algorithm incorporates the principles of both Harmony Search and Laying Chicken Algorithm to optimize resource allocation and congestion control. At the MAC layer, HS-MLCA offers several advantages over traditional congestion control schemes. Firstly, it leverages the Harmony Search algorithm, which is known for its ability to exploit the best outcomes in search processes. By exploring the solution space and exploiting promising regions, HS-MLCA optimizes resource allocation in the MAC layer. The integration of the Laying Chicken Algorithm (LCA) further enhances performance by improving convergence speed and solution accuracy. This hybrid approach leverages the strengths of both Harmony Search (HS) and LCA, resulting in more efficient and effective resource management. The Laying Chicken Algorithm simulates the behavior of laying hens in terms of resource allocation and competition. This approach contributes to provide the solution in quality and convergence speed, as the algorithm adapts to the dynamic nature of the MAC layer and the varying traffic conditions in VANETs. By combining the strengths of Harmony Search and Laying Chicken Algorithm, HS-MLCA offers improved performance in terms of congestion control in the MAC layer. It optimizes resource allocation, minimizes collisions and packet loss, reduces delay, and enhances overall network efficiency. These improvements ultimately lead to better quality of service, increased network capacity, and enhanced user experience in VANETs. It is worth noting that the specific performance improvements and benefits of HS-MLCA may vary depending on the implementation details, network conditions, and the specific VANET scenario. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Consecutive Radio Labelling of Graphs
Radio labelling or radio colouring is an assignment of positive integers to the vertices of a graph such that the difference between labels of any two vertices must be at least one more than the difference between the diameter of the graph and the distance between the vertices themselves. A graph G admits consecutive radio labelling when the radio number of the graph equals the order of the graph. In this paper, we study certain graphs admitting consecutive radio labelling and identify certain properties of such graphs. Moreover, we characterize the graphs with diameter two admitting consecutive radio labelling and examine certain properties of the labelling under some graph operations. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Consolidation of Cloud Computing in Smart and Sustainable Environment
Cloud computing has revolutionized IoT device data collection, administration, and analysis by offering a scalable and sustainable solution for managing vast amounts of data. The paper highlights cloud computing's benefits in data processing, device management, cost efficiency and scalability. However, challenges related to security, data ownership, and vendor lock-in require attention. A novel sustainable cloud-IoT model is presented by integrating smart computing with cloud infrastructure. It is observed that the model records promising performance. The mean response delay is 1.9 seconds and the 89.5% is the generated mean computational storage accuracy rate. In conclusion, the cloud computing empowered sustainable model can be used in organizations to gain insights from IoT data and make informed decisions, shaping future research in this rapidly evolving field. 2023 IEEE. -
Constraint Governed Association Rule Mining for Identification of Strong SNPs to Classify Autism Data
Autism is a heterogeneous neuro developmental disorder found among all age groups. Nowadays more patients are detected with autism but very less awareness is prevailing in the society related to it. This paved a way for many researchers to carry out serious study on autism and its characteristics. Studying behavior and characteristics of Autistic patients is very important for diagnosing the level of autism. Classifying the association of different characteristic in autistic patients at gene level using machine learning techniques can give an important insight to the doctors and the care takers of the patients. Research is being carried out to identify the genes responsible for autism. The changes in gene sequence may lead to different characteristics in different people. Thus genotypic research is found to reveal well defined insight about various characteristics in autistic patients and their associations with genes. Single Nucleotide Polymorphism (SNP) being high in features indicate human genome variability and is associated with identification of traits for many human diseases including autism. The main aim of the proposed work is to identify SNP sequences which are responsible for carrying the autistic traits. This paper explore the application of Constraint Governed Association Rule Mining (CGARM) technique on SNP data for dimensionality reduction and thereby selecting the strong predominant SNP features which are relevant enough to accomplish classification with high accuracy. The research work incorporates the application of CGARM and is carried out in two stages. In the first stage CGARM was used to choose significant SNP features resulting in dimensionality reduction. In the second stage classification was carried out by subjecting the selected features to Artificial Neural Network (ANN) algorithm. The main advantage of the proposed work is its ability to reduce the dimensions without compromising the quality i.e. using CGARM strong SNPs were selected by applying various constraints like Syntactical constraints, Semantical constraints and Dimensionality Constraints resulting in higher accuracy. The CGARM technique is applied on Autism data collected from National Center for Biotechnology Information (NCBI) repository. The data is divided into a set of 118 features, out of 118 features CGARM contributed in identifying 22 predominant SNPs. Further by applying forward selection method top 17 features were selected and were given as input to ANN. The 10 fold cross validation resulted in 76.9% accuracy which was found to be 50% more than that of original features. The proposed work contributed in reducing the dimension by 85% and provided 76.9% accuracy with the help of only 15% features. 2020 IEEE. -
Construction of Virtual Simulation Practice Teaching Platform for Business Majors Based on Fuzzy Control Algorithm
Simulation plays an important role in control research. Information technology and various related technologies, the research of simulation technology is also deepening. At present, there is no unified platform for the design and simulation of adaptive fuzzy controller, and the simulation algorithms of various controllers are different. With the strong advocacy of national education departments, virtual simulation technology has been widely used in academic education, and has gradually become an important means to improve traditional teaching. Cross-professional comprehensive training of business has almost become the preferred course of combining theory with practice in general colleges and universities. It requires students from different majors to participate together, cooperate and communicate deeply in teams, and compete and confront each other among groups, which helps to improve graduates' innovative and entrepreneurial ability. Through teaching practice, the design of teaching system, the joint training between schools and enterprises, and the consideration of virtual and actual combat are further improved. Explain the teaching application of virtual simulation experiment teaching platform. The virtual simulation experiment teaching platform is convenient for students to complete intelligent control experiments, and carry out secondary development and innovative experiments. 2023 IEEE. -
Consumer Characteristics and Consumption Patterns of Soft Drinks
A soft drink is generally treated as very common product aimed at a very casual consumption. Normally, not much of attention is paid to this product, which has almost become 'commoditized'. But, a deeper and more careful observation would reveal that soft drinks are strong demographic descriptors of their consumers. Key insights into the characteristics and consumption patterns of consumers can be obtained through an incisive study of the soft drinks market. This research paper makes a concerted effort at unearthing the demographic details and consumption contours of the soft drink users in Kanpur, Agra, Varanasi, Allahabad, and Lucknow - the five representative cities of Uttar Pradesh, the most populous state of India. It has been conclusively established through this research that the residents of these five cities - which are demographically similar in nature - exhibit varying consumption patterns when it comes to soft drinks. It was also found that demographic variables like age, gender, educational qualification, income, and marital status do not significantly impact the consumption of soft drinks, whereas employment status is a key influencer of the same. 2021 IEEE. -
Content Based Deep Factorization Framework for Scientific Article Recommender System
With the advancement in technology and the tremendous number of citations available in the digital libraries, it has become difficult for the research scholars to find a relevant set of reference papers. The accelerating rate of scientific publications results in the problem of information overload because of which the scholars spend their 70% of the time finding relevant papers. A citation recommendation system resolves the issue of spending a good amount of time and other resources for collecting a set of papers by providing the user with personalised recommendations of the articles. Existing state of art models do not take high-low order feature interactions into consideration, due to which the recommendations are not up to the desired level of performance. In this paper, we propose a content-based model which combines Deep Neural Network (DNN) and Factorization Machines (FM) where no pre-trainings are required for providing the citation recommendations. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Content Based Scientific Article Recommendation System Using Deep Learning Technique
The emergence of the era of big data has increased the ease with which scientific users can access academic articles with better efficiency and accuracy from a pool of papers available. With the exponential increase in the number of research papers that are getting published every year, it has made scholars face the problem of information overload where they find it difficult to conduct comprehensive literature surveys. An article recommendation system helps in overcoming this issue by providing users with personalized recommendations based on their interests and choices. The common approaches used for recommendation are Content-Based Filtering (CBF) and Collaborative Filtering (CF). Even though there is much advancement in the field of article recommendation systems, a content-based approach using a deep learning technology is still in its inception. In this work, a C-SAR model using Gated Recurrent Unit (GRU) and association rule mining Apriori algorithm to provide a recommendation of articles based on the similarity in the content were proposed. The combination of a deep learning technique along with a classical algorithm in data mining is expected to provide better results than the state-of-art model in suggesting similar papers. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Content-Restricted Boltzmann Machines for Diet Recommendation
Nowadays, society is leading towards an unhealthy and inactive and lifestyle. Recent studies show the rapid growth of people suffering from diseases caused due to unhealthy lifestyles and diet. Considering this, recognizing the right type and amount of food to eat with a suitable exercise set is essential to obtain good health. The proposed work develops a framework to recommend the proper diet plans for thyroid patients, and medical experts validate results. The experiments results illustrate that the proposed Content-Restricted Boltzmann Machines (Content-RBM) produces more relevant recommendations with content-based information. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Context Driven Software Development
The Context-Driven Software Development (CDSD) is a novel software development approach with an ability to thrive upon challenges of 21st century digital and disruptive technologies by using its innovative practices and implementation prowess. CDSD is a coherent set of multidisciplinary innovative and best practices like context-aware and self-adaptive system modelling, human-computer interaction, quality engineering, software development-testing-and continuous deployment frameworks, open-source tools-technology-and end-to-end automation, software governance, engaging stakeholders, adaptive solutioning, design thinking, and group creativity. Implementation prowess of CDSD approach stems from its three unique characteristics, namely, its principles, Contextualize-Build-Validate-Evolve (CBVE) product development element, and iterative and lean CDSD life cycle with Profiling, Contextualizing, Modelling, Transforming, and Deploying phases with in-process and phase-end Governance and Compliances. CDSD approach helps to address issues like complexity, software ageing, risks related to internal and external ecosystem, user diversity, and process-related issues including cost, documentation, and delay. 2021, Springer Nature Switzerland AG. -
Contextual Recommendation System: A Revolutionary Approach Using Hadoop, Spark, NLP and LLMs
This study presents a novel framework for contextual recommendations on platforms like Wikipedia, integrating Hadoop, Spark, NLP, and LLMs. Leveraging these technologies, the framework aims to enhance user experiences by delivering personalized article suggestions aligned with their current interests. Through scalable data processing, advanced NLP techniques, and LLM-powered semantic understanding, the framework offers a transformative approach to recommendation systems, promising to revolutionize knowledge exploration on digital platforms. 2024 IEEE. -
Continuance Intention of ChatGPT Use by Students
ChatGPT, an AI language model, has gained significant attention for its potential to enhance educational experiences and foster interactive learning environments. The potential of student interaction via ChatGPT has engendered significant debate around educational technology. It is apparent that the current literature has yet to fully explore the role of ChatGPT in management education. Amidst the increasing integration of ChatGPT into educational contexts, the concept of continuance intention takes center stage. This research paper delves into the nuanced landscape of students continuance intention regarding the use of ChatGPT in educational settings. We ground our study in Technology Continuance Theory and Theory of Planned Behavior to examine students continuance intention to use ChatGPT. By investigating the determinants that shape this intention, we aim to provide insights that inform educators and educational technology designers in optimizing the integration of AI-driven tools like ChatGPT. This study contributes to the growing body of research at the intersection of AI and education, offering valuable implications for both theory and practice. 2024, IFIP International Federation for Information Processing. -
Controlling Node Failure Localization in Data Networks Using Probing Mechanisms
In this paper, we prospect the potency of node failure localization in network communication from dual states (normal/fizzle) of the source to destination paths. To localize the failure nodes individually in the scheduled nodes, dissimilar path states must connect with various events of failure nodes. But, this situation is inapplicable or not easier to investigate or apply on enormous networks due to the obligation of any viable failure nodes. This objective is to deploy the set of adequate conditions for recognizing a set of failures in a set of arbitrary nodes which can be verified in a stipulated time. To avoid the above situation, probing mechanisms are assimilated additionally as a combination for network topology and locations of scrutinizes. Three probing mechanisms are considering which vary depending on measurement paths. Both the procedures can be transformed into single-node possessions by which they can be calculated effectively based on the given conditions. The exceeding measures are proposed for measuring the potency of failure localization which can be utilized for assessing the effect of different factors, which comprises topology, total monitors, and probing mechanisms. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Controlling the Accuracy and Efficiency of Collision Detection in 2d Games using Hitboxes
Collision detection is a process in game development that involves checking if two or more objects have intersected or collided with each other. It is a fundamental aspect of game mechanics that cannot be overlooked. Games invloves assets/sprites, which tend to be drawn digitally with the help of a computer program. This paper discusses controlling and detecting collisions in games that make use of PNG images as game assets. The conventional way to detect collision in a game is to check if the object is within the bounding box of another object or asset. However, such a method lacks realism and doesn't work well with much complex shapes as the game might register a hit when another object collides with the transparent part of the object being checked for collision. In order to overcome these limitations, the proposed algorithm divides the entire image into smaller rectangles and stores its coordinates in an array. The array is then pruned by removing coordinates with no translucent or opaque elements. Collision is detected by checking if any of the points of the collision object is inside the image array. 2023 IEEE. -
Convective instability in a horizontal porous layer saturated with a chemically reacting Maxwell fluid
The problem of onset of convective instability in a horizontal inert porous layer saturated with a Maxwell viscoelastic fluid subject to zero-order chemical reaction is investigated by linear stability analysis. Modified Darcy-Maxwell model is used to describe the fluid motion. The horizontal porous layer is cooled from the upper boundary while an isothermal boundary condition is imposed at the lower boundary. Closed form solution pertaining to the basic quiescent state is obtained. The resulting eigenvalue problem is solved approximately using the Galerkin method. The Rayleigh number, characterizing the stability of the system, is calculated as a function of viscoelastic parameter, Darcy-Prandtl number, normalized porosity, and the Frank-Kamenetskii number. The possibility of oscillatory instability is discussed. 2013 AIP Publishing LLC.