International Journal of Engineering Research and Sustainable Technologies (IJERST)

CROSS-MODEL SPATIAL-SEMANTIC FUSION: INTEGRATING MRI AND HISTOPATHOLOGY FOR AUTOMATED BONE-SARCOMA SEVERITY ASSESSMENT

P J.Adit, C.Priya — Thu, 25 Jun 2026 00:00:00 +0000

The accurate diagnosis and severity staging of bone-sarcoma inherently depend on synthesizing macroscopic radiological imaging (MRI) with microscopic histopathological analysis. However, contemporary deep learning frameworks process these modalities in strict isolation, creating a diagnostic bottleneck that limits clinical utility. In this paper, we propose the Cross-Modal Spatial-Semantic Diffusion Network (CM-SSD), a novel multi-modal fusion architecture. CM-SSD synchronously processes macroscopic tumor dissemination from MRI alongside microscopic malignancy patterns from Hematoxylin and Eosin (H&E) slides. By leveraging dual spatial-semantic encoders and a cross-modal attention mechanism, the framework adaptively aligns complementary features across both modalities. This integrated approach not only enhances automated tumor localization but also generates a quantitative, percentage-based severity score. By bridging the gap between radiological and pathological computer-aided diagnostics, CM-SSD provides a unified, highly robust framework for real-time clinical decision support.

EARLY DETECTION OF PARKINSON’S DISEASE USING NKM-SSM MODEL

M.Suwithra, A.R.Arunachalam, S.Bhuvaneshwari — Thu, 25 Jun 2026 00:00:00 +0000

Parkinson's Disease (PD) is the second most common neurodegenerative disease across the globe, impacting more than 10 million people by 2024. At the time when motor symptoms manifest themselves, 50 to 80 percent of dopaminergic cells in the Substantia Nigra pars compacta (SNpc) are irreversibly damaged – "presymptomatic gap" which justifies the necessity for development of biomarker-based artificial intelligence (AI) classifiers. This paper suggests a novel hybrid computation model, NeuroKAN-Mamba, which incorporates Kolmogorov-Arnold Networks (KAN), Mamba State Space Models (SSM) and Random Forest (RF) calibrator to perform a binary classification task on PD vs. Healthy Controls (HC) from multi-modal MRI neuroimaging biomarkers on the PPMI database. On the 4,000 samples from the PPMI database (2,400 PD/1,600 HC) with 20 MRI-based features, NeuroKAN-Mamba reaches 96.25% accuracy, 98.31% AUC-ROC, 96.88% F1-Score, 96.88% Sensitivity and 95.31% Specificity – significantly outperforming all 9 comparison models. Proposed pipeline includes DICOM-based raw neuroimaging data processing, 7-stage pre-processing, learning and evaluation of hybrid model and generation of explanations by means of extracting symbolic formula and calculating SHAP values. The presented model demonstrates clinically plausible biologically-driven results.

SUSTAINABLE ECONOMIC GROWTH THROUGH OPTIMIZED STOCK FORECASTING: A HYBRID ARIMA-LSTM ENSEMBLE WITH ADAPTIVE RANDOM SEARCH

S.Bhuvaneshwari, S. Nirmala Sugirtha Rajini, M.Suwithra — Thu, 25 Jun 2026 00:00:00 +0000

Stock price prediction remains a complex challenge due to market volatility and non-linear dynamics. This study introduces a novel hybrid framework, AdapRandOpt_ARIMA-LSTM, which integrates an upgraded ARIMA model with a tailored Long Short-Term Memory (LSTM) network. The model employs Adaptive Random Search Optimization (AdapRandOpt) to precisely calibrate hyperparameters and determine optimal weight distributions for the ensemble. The framework was evaluated on sixteen leading NSE-listed companies: ASIANPAINT, BEL, CIPLA, DMART, ETERNAL, FACT, GLAXO, HINDLCO, INDIGO, JSWENERGY, KOTAKBANK, LTFOODS, MANKIND, NESTLEIND, RELIANCE, and TATAMOTORS. Beyond standard Root Mean Square Error (RMSE) metrics, the model's efficacy was validated through profitability ratios and directional accuracy, ensuring its practical utility for traders. Results confirm that the hybrid ensemble significantly outperforms standalone models, demonstrating that AdapRandOpt effectively enhances forecasting robustness and predictive precision. This approach provides a computationally efficient, high-accuracy solution for navigating the intricacies of the Indian financial market. The AdapRandOpt_ARIMA-LSTM framework supports SDG 8 (Decent Work and Economic Growth) by providing a high-precision tool that fosters financial stability and informed decision-making within the Indian equity market. Furthermore, by integrating profitability ratios and directional accuracy, the model promotes SDG 12 (Responsible Consumption and Production) through the encouragement of sustainable investment practices and transparent financial resource management.

CARDIO TWIN-H: AN AI-INTEGRATED REAL TIME CARDIOVASCULAR DIGITAL TWIN SYSTEM FOR PROACTIVE RISK REDICTION AND CLINICAL DECISION SUPPORT

Murshid R, C.Akhila, V.Rameshbabu — Thu, 25 Jun 2026 00:00:00 +0000

Cardiovascular diseases are still the topmost killers of people all around the world, claiming approximately 17.9 million lives each year. Traditional tools to assess the risk of developing cardiovascular diseases, including the Framingham Risk Score, do not incorporate interactivity, explainability, and real-time simulative capabilities that would enable doctors to provide proactive and personalized care. In this work, we present CardioTwin-H, an AI-Integrated Real-Time Cardiovascular Digital Twin System with Hardware Sensor Integration. CardioTwin-H is a combination of a MAX30102 pulse oximetry and heart rate sensor connected to a Raspberry Pi 4 microcomputer and a machine learning-based platform. Data are streamed to a FastAPI backend server, where an ensemble of Random Forest, Gradient Boosting, and Logistic Regression algorithms calculates the cardiovascular risk score in real time. Reports based on the SHapley Additive Explanations framework are used to interpret data and provide clinically meaningful explanations. Finally, a scenario simulation engine allows for projecting the influence of clinical intervention on the predicted health state of patients. Overall, the system can be deployed at hardware costs of less than six thousand Indian Rupees.

AI, IoT BASED PRECISION FARMING AND GLOBAL TECHNOLOGY TRANSFER: AN INTEGRATED ANALYSIS

R.Thulasi, C.Priya — Thu, 25 Jun 2026 00:00:00 +0000

Global agriculture faces an existential inflection point: feeding ten billion people by 2050 while confronting accelerating climate disruption, finite water resources, and shrinking arable land. This paper presents an integrated scholarly synthesis of four complementary research perspectives: (I) artificial intelligence (AI) as a driver of crop productivity and ecological sustainability; (II) Israel's necessity-driven agricultural innovation model as a global blueprint; (III) the institutional mechanisms of Israel's agricultural technology transfer to China; and (IV) field-validated IoT-based precision farming architectures deployed at the smallholder scale in Kerala, India. Collectively, these perspectives demonstrate a compelling convergence — AI-mediated precision farming, distributed IoT sensor ecosystems, frontier genomic science, and structured international technology transfer are crystallising into intelligent, resource-efficient, and globally replicable agricultural systems. The paper presents five comparative tables and four statistical figures synthesising quantitative evidence across all four perspectives, alongside structured result analyses. Findings confirm that technology-driven agricultural transformation is an operational present reality, but that its equitable and universal deployment demands commensurate investment in institutional policy, education, finance, and governance.

Editorial Note

Dr. V Rameshbabu — Thu, 25 Jun 2026 00:00:00 +0000

International Journal of Engineering Research and Sustainable Technologies (IJERST)

Volume 4, No.2 Online ISSN: 2584-1394

Message from Editorial Desk 25th June 2026

Dear Readers, Researchers, and Contributors,

We are pleased to share several exciting developments as we continue our journey toward strengthening scholarly communication and promoting high-quality research dissemination.

Following the successful validation of our journal records by the ISSN Centre India (CSIR-NIScPR) and registration in the ISSN International Portal, we are now progressing toward our next major milestone. The process of applying for inclusion in the Directory of Open Access Journals (DOAJ) is currently underway. Necessary editorial, ethical, and technical requirements are being finalized, and the formal application will be submitted shortly. This initiative reflects our continued commitment to international publishing standards, transparency, accessibility, and academic excellence.

We are also delighted to announce the expansion of our journal portfolio with the introduction of two new peer-reviewed international journals:

*International Journal of AI Research and Data Innovation (IJAIRDI)*

and

*International Journal of Research in Humanities, Science and Technology (IJRHST)*

These journals have been established to provide dedicated platforms for researchers, academicians, industry professionals, and practitioners to publish innovative and impactful research across emerging and interdisciplinary domains. Detailed information regarding their aims and scope, editorial boards, publication schedules, and submission guidelines will be shared in the coming weeks.

The current issue presents a diverse collection of scholarly contributions that address contemporary challenges and opportunities across multiple disciplines. We sincerely thank our authors for entrusting us with their valuable research, our reviewers for their insightful evaluations, and our editorial board members for their unwavering dedication to maintaining publication quality.

As we move forward, we invite researchers from around the world to join us in advancing knowledge, fostering innovation, and contributing to the global research community.

Thank you for your continued support and confidence in our journals.

Warm regards,

On behalf of Editorial Team Dr.V.RameshBabu Managing Editor

Dean-University Journal , Dr.M.G.R. Educational and Research Institute Chennai,Tamilnadu,India

dean-univ.journals@drmgrdu.ac.in / ijerst@drmgrjournals.org