International Journal of Computer Communication and Informatics

A Smart Mobile Application for Integrated Personal Health Management

2026-08-05T06:05:01+00:00

Mobile health applications can support routine self-management by consolidating appointment scheduling, medication reminders, personal health records, first-aid information, and emergency contacts within a single interface. This paper presents the design and prototype implementation of an Android-based personal health management application developed using Kotlin/Java and Firebase services. The application provides email-based authentication, real-time database synchronisation, appointment management, configurable medication reminders, a rule-based symptom information module, first-aid guidance, and an emergency contact function. Functional testing was conducted at the module and interface levels to verify authentication, database read/write operations, notification scheduling, navigation, and screen-level workflow consistency. The prototype successfully executed the intended software functions under the tested conditions. However, the symptom module is not a diagnostic device, and the present work does not establish clinical accuracy, treatment effectiveness, or patient outcome improvement. The study therefore positions the application as a technical proof of concept requiring formal usability testing, cybersecurity assessment, clinical content review, and prospective evaluation before deployment in healthcare settings.

Calibration of Ooty Radio Telescope Module Response using Crab Nebula Transit Observations

2026-08-05T06:34:09+00:00

The Ooty Radio Telescope (ORT) is a large equatorially mounted cylindrical radio telescope operating near 326.5 MHz. Reliable module-to-module response assessment is necessary because gain variations can introduce systematic differences into long-duration transit observations. This study evaluates the relative response of selected ORT modules using repeated Crab Nebula transit measurements acquired over approximately five months. For each observing session, ON-source and OFF-source power sequences were aligned, visually inspected, converted to a logarithmic scale where required, and compared through peak excess and baseline behaviour. Time-series and module-wise plots were generated in GNUPLOT to identify repeatability, anomalous sessions, and relative gain differences. The processed observations consistently exhibited the expected rise–peak–fall transit morphology, while the measured peak excess varied across dates and modules. For the illustrated November subset, Module 21 showed values of approximately 1.59–3.28 dB, indicating measurable session-to-session variability. Because the available dataset does not include a fully documented absolute flux-density transfer, system-temperature measurement, uncertainty budget, or independent reference calibration, the analysis supports relative response normalisation rather than an absolute sensitivity determination in Jy K⁻¹ or system-equivalent flux density. The workflow provides a practical screening method for identifying comparatively weak or unstable modules and for selecting observations suitable for subsequent calibration. Future work should incorporate calibrated noise injection, contemporaneous weather and radio-frequency-interference logs, automated baseline fitting, uncertainty propagation, and comparison with a source of traceable flux density.

A Wearable Multisensor Platform for Neurofibromatosis Surveillance Using Bioimpedance, Bioelectrical Signals, and TinyML

2026-08-05T08:04:10+00:00

Neurofibromatosis comprises genetically distinct tumour-predisposition disorders that require lifelong, risk-adapted clinical surveillance. Current follow-up relies principally on clinical examination and imaging, which are essential but intermittent and may not capture short-term changes in peripheral-nerve function or local tissue electrical properties. This paper presents the design and bench-level evaluation of a wearable, non-invasive proof-of-concept platform that combines multifrequency bioimpedance sensing, amplified peripheral bioelectrical-signal acquisition, embedded feature extraction, and TinyML-based risk-pattern classification. An AD5933 impedance-converter circuit is used to estimate impedance magnitude and phase, whereas an AD620 instrumentation-amplifier stage conditions low-amplitude nerve- or electromyography-like signals. An ESP32 microcontroller performs data acquisition, filtering, feature computation, model inference, and Bluetooth Low Energy transmission. The proposed feature set includes mean impedance, phase angle, root-mean-square amplitude, and zero-crossing rate. Bench testing with simulated impedance loads and generated bioelectrical waveforms demonstrated stable acquisition and real-time wireless display. However, the reported thresholds and classification outputs are engineering labels for prototype verification and are not validated diagnostic criteria. Patient recruitment, clinical reference standards, sensitivity, specificity, calibration, and longitudinal outcome analysis remain necessary before any clinical interpretation. The present work therefore establishes an embedded-systems architecture and feasibility workflow rather than a clinically validated neurofibromatosis diagnostic device.

AI-Powered Intelligent Code Review and Pull Request Analysis System

2026-08-06T06:24:32+00:00

Modern code review is essential for defect prevention, maintainability, knowledge transfer, and collaborative software development, but manual review is frequently delayed by reviewer workload and inconsistent review depth. This paper presents ReviewPilot, a web-based prototype that integrates repository import, pull request analysis, static checks, large-language-model-assisted review, configurable review rules, issue summarisation, and a composite Code Health Score within a unified dashboard. The proposed workflow ingests repository and pull request metadata, validates and parses changed files, applies deterministic rules and conventional static analysis, and then invokes an AI review layer to generate context-sensitive observations. The results are organised by severity, file, and review category before being displayed through repository, pull request, rules, review-history, and dashboard interfaces. Functional interface testing confirms that the principal modules and navigation flow operate as intended in the simulated environment. However, the present work is a proof-of-concept system demonstration rather than a comparative evaluation of defect-detection accuracy, review usefulness, latency, security, or developer productivity. Accordingly, the paper defines an evaluation framework for future studies using labelled code changes, expert reviewer judgements, precision, recall, false-positive rate, response time, and user-centred measures. ReviewPilot therefore demonstrates a technically coherent architecture for AI-assisted review while recognising that human oversight, reproducible benchmarking, privacy controls, and hallucination mitigation remain necessary before production deployment.

Quantum Computing in Drug Discovery: A Comparative Analysis of Classical, Quantum, and Hybrid Approaches

2026-08-06T06:57:42+00:00

Drug discovery is a complex, costly, and data-intensive process that depends increasingly on computational methods for molecular modelling, virtual screening, property prediction, and lead optimisation. Classical high-performance computing, molecular simulation, and machine-learning methods currently provide the practical foundation for computer-aided drug discovery; however, their accuracy and scalability can be constrained when electronic correlation, large conformational spaces, or combinatorial optimisation must be treated explicitly. Quantum computing offers an alternative computational paradigm in which molecular electronic states and selected optimisation problems may be represented more naturally. This narrative comparative review examines the respective roles of classical, quantum, and hybrid classical-quantum approaches across major drug-discovery tasks. The analysis considers computational accuracy, scalability, hardware maturity, data requirements, interpretability, workflow integration, and evidence of practical utility. Current evidence indicates that classical methods remain superior for routine high-throughput screening and production-scale modelling, whereas quantum methods are presently restricted mainly to small molecular systems, proof-of-concept studies, and carefully selected subproblems. Variational quantum eigensolvers, quantum machine-learning models, and hybrid optimisation workflows are promising, but their performance remains sensitive to noise, circuit depth, data encoding, active-space selection, and benchmark design. The most credible near-term pathway is therefore not wholesale replacement of classical computing, but targeted integration of quantum routines into validated classical pipelines. Progress toward pharmaceutical utility will require chemically meaningful benchmarks, fair comparisons against strong classical baselines, uncertainty reporting, reproducible software stacks, and experimental validation of quantum-generated candidates.