This paper presents our findings on the automatic summarization of Java methods within Ericsson, a global telecommunications company. We evaluate the performance of an approach called Automatic Semantic Augmentation of Prompts (ASAP), which uses a Large Language Model (LLM) to generate leading summary comments (Javadocs) for Java methods. ASAP enhances the LLM's prompt context by integrating static program analysis and information retrieval techniques to identify similar exemplar methods along with their developer-written Javadocs, and serves as the baseline in our study. In contrast, we explore and compare the performance of four simpler approaches that do not require static program analysis, information retrieval, or the presence of exemplars as in the ASAP method. Our methods rely solely on the Java method body as input, making them lightweight and more suitable for rapid deployment in commercial software development environments. We conducted experiments on an Ericsson software project and replicated the study using two widely-used open-source Java projects, Guava and Elasticsearch, to ensure the reliability of our results. Performance was measured across eight metrics that capture various aspects of similarity. Notably, one of our simpler approaches performed as well as or better than the ASAP method on both the Ericsson project and the open-source projects. Additionally, we performed an ablation study to examine the impact of method names on Javadoc summary generation across our four proposed approaches and the ASAP method. By masking the method names and observing the generated summaries, we found that our approaches were statistically significantly less influenced by the absence of method names compared to the baseline. This suggests that our methods are more robust to variations in method names and may derive summaries more comprehensively from the method body than the ASAP approach.

Implementing machine learning models on resource-constrained platforms such as hardware devices requires sparse models that can generalize well. This article analyzes the effect of parameter (or weight) quantization on the performance, number of support vectors, model size in bits,$L_2$norm and training time on various Support Vector Machines (SVM) and Minimal Complexity Machhines (MCM)-based kernel methods. We show that, Empirical Feature Space (EFS) and hinge loss-based MCM algorithms result in comparable accuracy, (8–190)x smaller model size in bits and (10k–16k)x smaller$L_2$norm at full precision compared with LIBSVM. The Least Squares (LS) variants of MCM based methods results in$\approx$2% improvement in accuracy, upto 16x reduction in model size and upto 3x reduction in$L_2$norm at full precision compared with its state-of-the-art counterpart Sparse Fixed Size variant of LS-SVM (SFS-LS-SVM). We quantize the weights of the compared variants post-training and demonstrate that our methods can retain their accuracies even with 7 bits as opposed to 10 and 14 bits used by LIBSVM and SFS-LS-SVM, respectively. Our experiments illustrate that quantization further improves upon the model sizes used by our methods by upto 300x and 30x compared with the LIBSVM and SFS-LS-SVM. This has significant implications for implementation in Internet of Things (IoT) devices, which benefit from model sparsity and good generalization.

Management of high patient loads at tertiary hospitals presents a significant challenge in streamlining healthcare service delivery. Patients often need to queue up at various service areas in hospitals such as at registration, laboratory test and bill payment counters. Queue Management Systems (QMS) present a viable solution for patient management in such scenarios. However, conventional QMS are not generic by design, depend on hardware components or do not provide a comprehensive end-to-end solution that caters to the complete patient workflow. This paper presents a mobile-augmented smart queue management system that can be easily configured with an operational Hospital Management Information System (HMIS). It provides multiple interfaces for token generation and consumption on mobile devices integrated with hospital service counters, while using smart algorithms for token generation and allocation. The solution is comprehensive as that it caters to streamlined queue management across multiple hospital service areas using a single token for a patient, which improves patient experience and also helps the hospital administration track and optimize key performance metrics. We present the architectural and operational design of this system, along with an illustration of the use of this system in tracking productivity of service counter operators in a pilot implementation.

This paper introduces a novel approach for dengue fever classification based on online learning paradigms. The proposed approach is suitable for practical implementation as it enables learning using only a few training samples. With time, the proposed approach is capable of learning incrementally from the data collected without need for retraining the model or redeployment of the prediction engine. Additionally, we also provide a comprehensive evaluation of machine learning methods for prediction of dengue fever. The input to the proposed pipeline comprises of recorded patient symptoms and diagnostic investigations. Offline classifier models have been employed to obtain baseline scores to establish that the feature set is optimal for classification of dengue. The primary benefit of the online detection model presented in the paper is that it has been established to effectively identify patients with high likelihood of dengue disease, and experiments on scalability in terms of number of training and test samples validate the use of the proposed model.

Billing and payment modules of a Hospital Information System (HIS) are vital components responsible for monetization of healthcare delivery services provided by such systems. In today's age of digital payments and cashless transactions, it has become imperative to design these payment modules to support payments via multiple user interfaces and financial instruments. This would enable seamless operation of these modules, while also minimizing processing time in approval, and eventually, the delivery of healthcare services. In this paper, we present the concept of a "patient wallet", which provides mobile and web interfaces to enable patients to directly make digital payments for requisitions raised during different hospital workflows. The patient wallet maintains a user account balance and directly fetches transactions from the HIS, thereby enabling patients to easily make payments without the need for visiting the billing counter at medical facilities or referring to third party transaction instruments. The patient wallet is closely integrated with the operational architecture of the HIS, hence making its use simple and versatile, while minimizing integration effort and providing ubiquitous healthcare services with minimal delay.

Classification of ElectroEncephaloGram (EEG) signals has found several applications in developing Brain Computer Interfaces (BCIs), as well as other clinical and nonclinical applications based on EEG signals. Processing of EEG signals in this context is challenged by its non-stationarity, high dimensionality and the problem of class imbalance for training classifiers, particularly in case of multi-class classification. Our recent work demonstrated the utility of Twin Support Vector Machine (TWSVM) classifiers for robust classification of imbalanced datasets, specifically EEG signal classification. However, the architecture of the TWSVM is not scalable for large datasets as it involves computing the kernel and matrix inversion operations. In this paper, we present an application of the recently proposed neural network architecture for the Twin SVM, the Twin Neural Network (Twin NN), for robust classification of EEG signals. Results on datasets from BCI competitions illustrate the improved generalization and scalability of the Twin NN for the binary and multi-class classification tasks.

This paper extends the idea of Least Squares Minimal Complexity Machines [1] (LS-MCMs) to non-Mercer kernels. There are no efficient solvers for LS-SVMs with non-Mercer kernels for large scale datasets. Here, we propose two variants of our novel multiclass loss LS-MCMs. Firstly, with an L1regularizer and secondly, with an explicit margin regularizer along with L1norm in the Empirical Feature Space (EFS). Both these methods can be scaled to large datasets with the use of “prototype vectors” selected from the dataset. The first optimization algorithm can be solved efficiently using Stochastic Gradient Descent (SGD) directly as the problem remains convex in the parameter space. The second problem however, is solved using difference of convex functions (DC) programming with SGD due to the non-convex nature of margin regularizer. Our method also obtains a sparse solution as opposed to the one obtained using LS-SVMs which tend to be non-sparse.

Minimal complexity machines (MCMs) are a class of hyperplane classifiers that try to minimize a tight bound on the Vapnik-Chervonenkis dimension. MCMs can be used both in the input space and in a higher dimensional feature space via the kernel trick. MCMs tend to produce very sparse solutions in comparison to support vector machines, often using three to ten times fewer support vectors. However, large datasets present significant challenges in terms of storage and operations on the kernel matrix. In this paper, we present a stochastic subgradient descent solver for large-scale machine learning with the MCM. The proposed approach uses an explicit feature map-based approximation of the kernel, to improve the scalability of the algorithm.

This paper describes a variant of the Incremental Ant Colony Optimization algorithm for continuous optimization (IACOℝ). The original IACOℝapproach estimates the Probability Density Function (PDF) using Gaussians constructed around candidate solutions to generate new solutions. We use Support Vector Regression (SVR) to fit a regressor to the candidate solutions. The minima of the fitted regressor are found using a variant of EigenAnt. This approach is based on the observation that minima tend to be clustered in real problems, and estimating the landscape of minima is more efficient than estimating the landscape of the original function. We present results on two fronts. We demonstrate the effect of the use of SVR and modified EigenAnt. Further, we also demonstrate the performance of our approach on the Soft Computing (SOCO) benchmark functions for global optimization, and obtain appreciable results.

Identifying wrist and finger flexions from surface Electromyogram (sEMG) signals finds several applications for developing prosthesis-based device control. However, sEMG signals can be corrupted by muscular activity from multiple sources at the site of acquisition, and hence the identification of intents from these signals presents a challenge. Moreover, there can be multiple intents which need to be recognized, hence a robust classifier is required. The accurate recognition of these movements is imperative as it enables reliable control of devices. In this paper, we use the Twin Support Vector Machine (Twin SVM) classifier to identify 15 classes of wrist and finger flexions using one-v/s-rest classification approach. Our work uses sEMG data obtained from nine subjects, including an amputee volunteer. We compare the improved accuracy obtained in using Twin SVM against LIBSVM (a standard SVM implementation) to demonstrate the effectiveness of the classifier. We use a simple feature - the Root Mean Square (RMS) value of the signal during the trial as features for the classifier. Our results demonstrate the effectiveness of using the Twin SVM in a multi-class scenario with unbalanced datasets, which holds significance in addressing the broader challenges in classification presented in several applications based on processing of biomedical signals.

A widely known approach for continuous global optimization has been the Incremental Ant Colony Framework (IACOR). In this paper, we propose a strategy to introduce hybridization within the exploitation phase of the IACOR framework by using the Multi-Trajectory Local Search (Mtsls1) algorithm and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms. Our approach entails making a probabilistic choice between these algorithms. In case of stagnation, we switch the algorithm being used based on the last iteration. We evaluate our approach on the Soft Computing (SOCO) benchmark functions and present results by computing the mean and median errors on the global optima achieved, as well as the iterations required. We compare our approach with competing methods on a number of benchmark functions, and show that the proposed approach achieves improved results. In particular, we obtain the global optima in terms of average value for 14 out of 19 benchmark functions, and in terms of the median value for all SOCO benchmarks. At the same time, the proposed approach uses fewer function evaluations on several benchmarks when compared with competing methods, which have been found to use 54% more function evaluations.

We present an algorithm to generate unique identifiers to be used for patient identification in the Electronic Health Record (EHR) system for India. A robust EHR system is essential for ubiquitous provision of technology-enabled health-care services. Conventional EHR systems use identifiers such as Social Security Numbers to identify patients. However, in developing nations, adoption of such systems are in its infancy, leading towards the need of an independent identifier generation mechanism to enable patients without conventional IDs to register and avail health-care services. In this paper, we present an approach to generate Unique Health IDentifiers (UHID) for patients to register with EHR systems in the Indian scenario. The approach uses a combination of patient attributes to generate the identifier, and is non-obvious to a naive user. It is capable of generating upto 46; 656 unique identifiers for similar attributes, and offers the added advantage of efficient duplicate detection by using Soundex codes. The effectiveness of the proposed approach has been benchmarked on an actual patient dataset of 1; 992; 102 records, drawn from C-DAC's Hospital Management Information System (HMIS).

Though the Extreme Learning Machine (ELM) has become quite popular in recent years, there are no performance guarantees; the resultant networks also tend to be densely connected. The complexity of a learning machine may be measured by the Vapnik-Chervonenkis (VC) dimension, and a small VC dimension leads to good generalization and lower test set errors. The Minimal Complexity Machine (MCM), that has been proposed very recently, shows that it is possible to learn a classifier with minimal VC dimension, leading to sparse representations and good generalization. In this paper, we draw on results from the MCM to propose a hybrid variant of the ELM, termed the Minimal Complexity - Extreme Learning Machine (MC-ELM), in order to realize a robust classifier that minimizes an exact bound on the VC dimension. The MC-ELM solves a linear programming problem for the last layer and offers the advantages of large margin and low VC dimension. In effect, the learning paradigm elucidated in this paper helps us build a classifier which is based on a minimal representation of the training data owing to MCM, and high training speed attributed to ELM. This makes it feasible for use in complex machine learning applications, where these advantages are of significance.