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The human heart produces electrical signals to contract and relax its muscles that helps in its blood pumping activities. These electrical impulses give rise to electric potentials on the body surface and tiny magnetic field around the thorax. These functional activities can be investigated using Electro/Magnetocardiogram (E/MCG). The more challenging task in the E/MCG research is to image the cardiac dysfunctions in three dimensions not at the surface level but at the source level and this is called the inverse problem. To solve this, one has to model a generic structure of the discretised heart enclosed in the thorax mesh and their spatial relation with location of the MCG detectors, called forward problem. In this current research, sources in a homogeneous volume conductor model is used in the construction of forward problem. A novel algorithm is implemented that uses Vectocardiography (VCG) signals in the forward problem of MCG. Another objective of this paper includes the utilization of dynamic lead field based on VCG orientations in the inverse problem. In this study, the ill-posed problems are solved using Bayesian approach and the results are compared with the deterministic approach for measurements on noise signals. The analysis revealed that the proposed algorithms with VCG priors (that are extracted from the VCG signals) in the Bayesian framework significantly improved MCG source localization in cases of Myocardial Ischemia. Analysis from the study showed that the proposed algorithms with VCG priors in probabilistic methods significantly captured a good region of spread of the reconstructed borders of inverse solutions. The average spread for deterministic methods was around 3.5 - 4.3cm in the diseased cases with simulated true ruptured region of spread being 2.5cm. In contrast, Bayesian methods and total variation methods with VCG priors reduced the spread to 2.9 to 3.03cm, respectively. The introduction of VCG signals in the forward problem of MCG not only increases the accuracy of cardiomagnetic imaging but also provides a path for more reliable diagnostic tools in cardiology.

In this paper, the inverse problems of cardiac sources using analytical and probabilistic methods are solved and discussed. The standard Tikhonov regularization technique is solved initially to estimate the under-determined heart surface potentials from Magnetocardiographic (MCG) signals. The results of the deterministic method subjected to noise in the measurements are discussed and compared with the probabilistic models. Hierarchical Bayesian modeling with fixed Gaussian prior is employed to quantify the uncertainties in source reconstructions. A novel application of Variational Bayesian inference approach has been presented to estimate the heart sources. The reconstruction results of Variational Bayesian model with non-stationary priors are compared with solutions of simplistic Bayesian approach; and the performances are evaluated using Root Mean Square Error (RMSE) and correlation co-efficient metrics. The Bayesian solutions in the study are also extended to localize the MCG sources for two types of Myocardial infarction cases.

Purpose The reliable estimation of the vertebral body posture helps to aid a safe and effective spine surgery. The proposed work aims to present an MR to X-ray image registration to assess the 3D pose of the vertebral body during spine surgery. The 3D assessment of vertebral pose assists in analyzing the position and orientation of the vertebral body to provide information during various clinical diagnosis conditions such as curvature estimation and pedicle screw insertion surgery. Methods The proposed feature-based registration framework extracted vertebral end plates to avoid the mismatch between the intensities of MR and X-ray images. Using the projection matrix, the segmented MRI is forward projected and then registered to the X-ray image using binary image matching similarity and the CMA-ES optimizer. Results The proposed method estimated the vertebral pose by registering the simulated X-ray onto pre-operative MRI. To evaluate the efficacy of the proposed approach, a certain number of experiments are carried out on the simulated dataset. Conclusion The proposed method is a fast and accurate registration method that can provide 3D information about the vertebral body. This 3D information is useful to improve accuracy during various clinical diagnoses.

Analysis of scoliosis requires thorough radiographic evaluation by spinal curvature estimation to completely assess the spinal deformity. Spinal curvature estimation gives orthopaedic surgeons an idea of severity of spinal deformity for therapeutic purposes. Manual intervention has always been an issue to ensure accuracy and repeatability. Computer assisted systems are semi-automatic and is still influenced by surgeon’s expertise. Spinal curvature estimation completely relies on accurate identification of required end vertebrae like superior end-vertebra, inferior end-vertebra and apical vertebra. In the present work, automatic extraction of spinal information central sacral line and medial axis by computerized image understanding system has been proposed. The inter-observer variability in the anatomical landmark identification is quantified using Kappa statistic. The resultant Kappa value computed between proposed algorithm and observer lies in the range 0.7 and 0.9, which shows good accuracy. Identification of the required end vertebra is automated by the extracted spinal information. Difference in inter and intra-observer variability for the state of the art computer assisted and proposed system are quantified in terms of mean absolute difference for the various types (Type-I, Type-II, Type-III, Type-IV, and Type-V) of scoliosis.

Speech recognition system extract the textual data from the speech signal. The research in speech recognition domain is challenging due to the large variabilities involved with the speech signal. Variety of signal processing and machine learning techniques have been explored to achieve better recognition accuracy. Speech is highly non-stationary in nature and therefore analysis is carried out by considering short time-domain window or frame. In the speech recognition task, cepstral (Mel frequency cepstral coefficients (MFCC)) features are commonly used and are extracted for short time-frame. The effectiveness of features depend upon duration of the time-window chosen. The present study is aimed at investigation of optimal time-window duration for extraction of cepstral features in the context of speech recognition task. A speaker independent speech recognition system for the Kannada language has been considered for the analysis. In the current work, speech utterances of Kannada news corpus recorded from different speakers have been used to create speech database. The hidden Markov tool kit (HTK) has been used to implement the speech recognition system. The MFCC along with their first and second derivative coefficients are considered as feature vectors. Pronunciation dictionary required for the study has been built manually for mono-phone system. Experiments have been carried out and results have been analyzed for different time-window lengths. The overlapping Hamming window has been considered in this study. The best average word recognition accuracy of 61.58% has been obtained for a window length of 110 msec duration. This recognition accuracy is comparable with the similar work found in literature. The experiments have shown that best word recognition performance can be achieved by tuning the window length to its optimum value.

Choosing the most suitable treatment for the scoliosis relies heavily on accurate and reproducible spinal curvature measurement from radiographs. Our objective is to reduce the variability in spinal curvature measurement by reducing the user intervention and bias. In order to determine the reliability of the spinal curvature measurement as it is in the clinical measurement of scoliosis a methodological survey has been carried out that concludes with inter and intra observer error variation. The proposed method list out horizontal inclination of all the vertebrae’s in terms of slopes using active contour models and morphological operators. This facilitates the radiologist to decide end vertebrae and hence inter/intra observer variation is completely eliminated. Tables  1 and 2 shows the observer error variation between manual and proposed methods in terms of mean and standard deviation.

The adoption of cloud computing in different areas has shown benefits and given solutions to applications. The cloud provider offers virtualized platforms through virtual machines for the cloud users to store the data and perform computations. Due to the distributed nature of cloud, there are many challenges and security is one of the challenges. To address this challenge, verification method is implemented to achieve high level security in the cloud environment. Many researchers have provided different authentication mechanisms to safeguard virtual machines from attacks. In this paper, Multi Stage Authentication is proposed to overcome the threats from attackers towards virtual machines. In order to authorize and access the virtual machine, multistage authentication incorporating the factors like username, email id, password and OTP is carried out. Mealy Machine model is applied to analyze the state changes with factors supplied at multiple stages and trust built with each stage. Experimental results prove that system is safe achieving data integrity and privacy. The proposed work gives the protection against unauthorized users, provides secure environment to the cloud users accessing the virtual machines.

Choosing the right Transmission Control Protocol (TCP) congestion control algorithm matters more in shared cloud environments than is often appreciated, yet head-to-head comparisons across heterogeneous, concurrently running workloads remain rare in the literature. We evaluated three widely used variants Cubic, Reno, and Bottleneck Bandwidth and Round-trip propagation time (BBR) under four workload types: synthetic throughput testing with iperf3, real-time stream processing using Apache Kafka and Apache Flink, distributed I/O, and compute-intensive sorting via Hadoop TeraSort. All experiments ran inside a dumbbell network topology built on four Amazon Web Services (AWS) m7i-flex.large Elastic Compute Cloud (EC2) instances, with a dedicated forwarding node and static routing forcing every flow through that single contention point. Across every metric we measured throughput, end-to-end latency, retransmission count, and job completion time the three variants behaved quite differently depending on the workload. The results give concrete, workload-specific guidance for operators choosing a congestion control policy in multitenant cloud deployments.

Project management requires the chaotic but important task of estimating software development effort. Several soft computing approaches have been proposed to increase estimation accuracy, and optimization techniques are utilized to concentrate on key aspects. However, a majority of works use data processing that has been found to be unreliable, time-consuming and typically leads to greater error rates. Therefore, the research proposes an efficient software development effort prediction model employing unique deep learning technology to alleviate the existing limitations. Data collection, pre-processing, feature selection and software development effort prediction are only a few of the varied phases of the proposed objective. After data collection, the data are pre-processed, including data cleaning, normalization, missing values and imputation. The expanded archer fish optimization method (Ext_AFO) is used to choose the best features from the pre-processed data. The Multilayer Perceptron Assisted Honey Bidirectional Gated Recurrent Feed Forward Network (Multi-HBiG) is built into this research work to provide an intelligent prediction model for software development effort estimation. The model parameters are adjusted using the Adaptive Honey Badger Optimisation Algorithm (A-Hba) to improve the overall estimation performance. The Albrecht dataset, China dataset, Desharnais dataset, Kemerer dataset, Maxwell dataset, Kitchenham dataset and Cocomos81 dataset are the datasets used in this study. The proposed approach surpassed other models when compared against Mean Relative Error (MRE), Mean Magnitude of Relative Error (MMRE), Mean Balanced Relative Error (MBRE) and Mean Inverted Balanced Relative Error (MIBRE) in the results section. The proposed model was assessed in this study using the MAE in each dataset, and it achieved 0.0753 for the China dataset, 0.0763 for the Cocomos81 dataset, 0.0737 for the Desharnais dataset, 0.0754 for the Kemerer dataset, 0.0759 for the Kitchenham dataset, 0.0734 for the Maxwell dataset and 0.0737 for the Albrecht dataset, respectively.

The present study was conducted on the social adjustment and academic achievement of preuniversity students in Mangalore taluk. The main objective of this study is to find out the relationship between social adjustment and academic achievement of pre-university students from rural and urban areas. The study was carried out on 320 students belonging to rural and urban regions of Mangalore taluk. For data collection, a simple random sampling technique was used. The researchers prepared and validated a questionnaire consisting of 45 questions. The collected data was analysed using Pearson’s product-moment correlation method. The study reveals that there is a significant positive relationship between the social adjustment and academic achievement scores of rural and urban preuniversity students.