International Journal of Imminent Science & Technology.

Role of ICT in Building Resilience of Medium-Sized Enterprises During Economic Crises.

2026-04-02T03:18:11+00:00

Economic crises pose significant challenges for medium-sized enterprises (MSEs), which often lack the financial resilience of large corporations or the flexibility of smaller businesses. This study examines the critical role of Information and Communication Technology (ICT) in enhancing the resilience of MSEs during periods of economic instability. Utilizing a mixed-methods approach, the research combines quantitative analysis of sector-specific ICT adoption rates with qualitative insights from case studies and expert interviews. This methodology enables a comprehensive understanding of how ICT adoption influences business continuity, innovation, and market adaptability. The findings highlight those technologies such as cloud computing, remote work solutions, and enterprise resource planning systems are instrumental in mitigating supply chain disruptions, maintaining workforce productivity, and identifying new market opportunities. Additionally, the study identifies key barriers to ICT adoption, including resource constraints and technological readiness, which affect its impact across diverse contexts. The data reveal a 30% higher recovery rate for ICT-enabled MSEs compared to non-ICT-enabled ones, emphasizing the transformative potential of digital tools in fostering resilience. The study offers actionable insights for policymakers, business leaders, and ICT developers to design tailored strategies that support MSEs in navigating crises. By situating ICT as a cornerstone of economic resilience, this research contributes to the broader discourse on digital transformation and sustainable business practices in the face of economic disruptions.

IoT Security Challenges: A Multi-Layered Approach to Securing Smart Devices

2025-01-29T07:37:09+00:00

The Internet of Things (IoT) has transformed industries by connecting devices to automate processes and generate data-driven insights. Despite these advancements, the proliferation of IoT devices has brought about critical security concerns. Many devices are deployed with insufficient safeguards, exposing them to potential cyber threats. The complexity of IoT networks, characterized by a wide range of device capabilities, communication protocols, and resource limitations, further compounds these vulnerabilities. This study introduces a comprehensive, multi-layered strategy to fortify IoT security, targeting vulnerabilities at the physical, network, application, and data levels. By employing measures such as encryption, secure communication protocols, access control mechanisms, and data integrity verification, this approach seeks to mitigate risks effectively. Additionally, the research investigates how cutting-edge technologies like artificial intelligence and blockchain can bolster IoT security, fostering robust and adaptable systems capable of withstanding evolving threats.

Optimization of Machine and Deep Learning Algorithms in Blood Cancer Classification.

2025-03-15T10:02:33+00:00

Accurate classification of blood cancer subtypes, such as Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL), is crucial for personalized treatment strategies. This study employs a quantitative methodology to classify blood cancer subtypes using gene expression data from 72 patients with 7,129 distinct gene expressions. Advanced preprocessing techniques, including Principal Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE), were applied to handle high dimensionality and class imbalance. The dataset was split into 80% training and 20% testing sets. We evaluated ML algorithms such as Support Vector Machine (SVM), Logistic Regression, Random Forest, and K-Nearest Neighbor (KNN), alongside DL architectures like Convolutional Neural Networks (CNNs) and a hybrid CNN-LSTM model. Performance was assessed using metrics like accuracy, precision, recall, F1-score, and AUC-ROC. SVM and Logistic Regression achieved 100% accuracy, while the CNN-LSTM model achieved 99.1% accuracy, demonstrating superior performance in capturing complex gene expression patterns.

External validation on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets confirmed the models' robustness, with slight performance drops due to dataset variability. Biological interpretation using Gene Ontology (GO) enrichment analysis identified known biomarkers (e.g., FLT3 for AML and PAX5 for ALL) and potential novel biomarkers (e.g., GATA2 and RUNX1). A comparative analysis with state-of-the-art methods, including SVM with Recursive Feature Elimination (RFE) and XGBoost, showed that the proposed models consistently outperformed existing techniques. This study highlights the potential of ML and DL in blood cancer classification, offering a foundation for automated diagnostic systems that enhance clinical decision-making and personalized treatment strategies. The findings contribute to advancing personalized medicine and improving patient outcomes.