Digitization of health records in public health facility and its instant availability in the form of electronic records anywhere any time health service is yet to be implemented in developing nations like India and other countries. In India, patient care is mainly delivered through 3 levels namely Primary/Community Healthcare Centre (PHC/CHC), Secondary healthcare centre (District Hospital), and Tertiary Healthcare Centre (National level). The healthcare facilities face many challenges in collecting, processing, and storing these data and managing it without compromising security and privacy. Presently, some of the secondary and tertiary care facilities have started implementing healthcare IT application in terms of Hospital Information System, Hospital Management Information System, Electronic Medical Records (EMR) etc. to manage the patient data in electronic format. However, these systems are developed by different vendors by using different programming languages and databases. This approach makes the system unique but the patient details remains in the same hospital and cannot be shared with other hospitals when patient moves from one hospital to other for advanced or specialized treatment. This is because the data is not interoperable and semantic. In the proposed work, a standard secure Electronic Health Record(EHR) framework is developed using standard medical terminology and coding standards. Implementation of EHR framework for Indian health system will improve the work-flow of health services to the population. EHR at all levels of healthcare system enable efficient and continuous care to the patient.

Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual’s identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information.

The Internet of Things is touching all spheres of life, be it in connecting cities together, making agricultural farms and health care smarter, predictable and more secure, and in industries it is set out to bring about changes that are similar to those of the industrial revolution that took place in the 19th and 20th century. It is estimated by pundits that in next 5 to 10 years, the Internet of Things will become a 50 billion dollar industry by itself, encompassing everything that it touches and goes upon. In order to get healthcare enabled into the IoT ecosystem, the sensors and the actuators related to it must be able to support the protocols that is required for the acquisition, processing and storing of data from the sensors to the IoT based infrastructure. Here, for a proposed model for a health care monitor using Internet of Things, the sensors characteristics, working principal, the protocol associated with it, its internal mechanism, and the results obtained when interfaced using a Raspberry Pi are discussed, laying the framework for the future of the sensors that need to be adapted to stay relevant in the future, when IoT transitions from concept to reality.

Databases containing lexical properties are of primary importance to psycholinguistic research and speech-language therapy. Several lexical databases for different languages have been developed in the recent past, but Kannada, a language spoken by 50.8 million people, has no comprehensive lexical database yet. To address this, KannadaLex , a Kannada lexical database, is built as a language resource that contains orthographic, phonological, and syllabic information about words that are sourced from newspaper articles from the past decade. Along with these vital statistics such as the phonological neighborhood, syllable complexity summed syllable and bigram syllable frequencies, and lemma and inflectional family information are stored. The database is validated by correlating frequency, a well-established psycholinguistic feature, with other numerical features. The developed lexical database contains 170K words from varied disciplines, complete with psycholinguistic features. This KannadaLex is a comprehensive resource for psycholinguists, speech therapists, and linguistic researchers for analyzing Kannada and other similar languages. Psycholinguists require lexical data for choosing stimuli to conduct experiments that study the factors that enable humans to acquire, use, comprehend, and produce language. Speech and language therapists query these databases for developing the most efficient stimuli for evaluating, diagnosing, and treating communication disorders, and rehabilitation of speech after brain injuries.

Analyzing the semantic similarity of cross-lingual texts is a crucial part of natural language processing (NLP). The computation of semantic similarity is essential for a variety of tasks such as evaluating machine translation systems, quality checking human translation, information retrieval, plagiarism checks, etc. In this paper, we propose a method for measuring the semantic similarity of Kannada–English sentence pairs that uses embedding space alignment, lexical decomposition, word order, and a convolutional neural network. The proposed method achieves a maximum correlation of 83% with human annotations. Experiments on semantic matching and retrieval tasks resulted in promising results in terms of precision and recall.

In the realm of millimeter wave (mmWave) communication, massive multiple-input-multiple-output (mMIMO) systems have grabbed greater attention due to advancements in the 5G cellular networks. This mMIMO with a lens antenna array has proven its efficacy by limiting the number of radio frequency (RF) chains, thus minimizing hardware cost, MIMO dimension, and power consumption. However, channel estimation is tedious with limited RF chains. In addition, the use of compressive sensing (CS) algorithms for signal recovery hampers their practical implementation due to several iterations causing high complexity. To address these concerns, this proposal investigates and develops a deep learning (DL)-based beamspace channel estimation approach. This is achieved by refining the learned approximate message passing (LAMP) network by deriving a new soft threshold shrinkage function to develop a new mixture of Gaussian distribution-LAMP (MoG-LAMP) algorithms. This captures the prior distribution of the beamspace channel. In each MoG-LAMP layer, the Gaussian variance parameters of the sparse channel are updated by a deep neural network, which efficiently learns complicated channel sparsity structures. Furthermore, this developed algorithm is extended to propose a hybrid approach, which is a combination of LAMP and MoG-LAMP, to showcase its effectiveness in achieving higher channel estimation accuracy. The simulation results are verified with the DeepMIMO dataset and the Saleh–Valenzuela (SV) channel model. The developed MoG-LAMP algorithm outperforms conventional methods, delivering satisfactory accuracy even at low SNRs. The proposed hybrid approach yields improvements of 38% and 26% over the LAMP and MoG-LAMP algorithms, respectively, at an SNR of 10 dB.