International Journal of Multidisciplinary Sciences and Technology
International Journal of Multidisciplinary Sciences and Technology (IJMST) is a scholarly peer-reviewed journal that invites original research, review articles, and case studies that bring together knowledge from various scientific and technological disciplines to foster innovative solutions for modern challenges. The journal aims to provide high-quality research in a wide array of multidisciplinary areas in science, technology, engineering, social sciences and other interdisciplinary applications. IJMST probably focuses on how several disciplines might be integrated to meet today's global issues through scientific breakthroughs, technological innovation, and cooperative research. As the world becomes more interconnected and complex, the challenges faced by society require innovative solutions that span beyond traditional disciplinary boundaries. IJMST provides a platform that encourages collaboration across diverse fields, enabling researchers to tackle global problems from a multifaceted perspective.
AI-Driven Cloud-Native Microservices Framework for Secure Digital Communication, Software Reliability, and Enterprise Deployment Optimization
Secure digital communication has become a foundational requirement for modern enterprises that operate across distributed cloud platforms, regulated data environments, and high-velocity software delivery pipelines. However, many organizations continue to rely on fragmented communication services, monolithic security controls, reactive monitoring, and manually governed deployment processes that are insufficient for dynamic microservices environments. The increasing adoption of cloud native architectures improves modularity and scalability, but it also expands the operational attack surface, increases service dependency complexity, and creates new reliability risks across application, infrastructure, and communication layers. This paper proposes an AI driven cloud native microservices framework for secure digital communication, software reliability, and enterprise deployment optimization. The framework integrates microservice decomposition, zero-trust communication controls, AI-assisted anomaly detection, service dependency intelligence, deployment telemetry, and reliability-aware governance into a vendor-neutral reference architecture. The major contribution of this paper is a structured architectural model that connects secure communication, predictive software quality, and continuous deployment optimization within a single enterprise-oriented framework. The paper also compares conventional microservice practices with AI augmented cloud native approaches and identifies practical gaps in observability, security automation, root cause analysis, and deployment governance. The analysis indicates that AI-driven microservices frameworks can support proactive reliability engineering, improved communication security, and more adaptive deployment decisions when implemented with appropriate governance, monitoring, validation, and human oversight. The paper is conceptual and architecture-based; therefore, it does not claim experimental proof. Instead, it offers a research-grounded model that can guide enterprise implementation, comparative evaluation, and future empirical validation.
A Hybrid HMM-LSTM Model for Advanced Cyber Attack Detection
The growth, development and connectedness of digital networks and systems have resulted in a dramatic increase in the complexity of cyber attacks, presenting new challenges for conventional intrusion detection systems (IDS). Traditional methods may fail to effectively model temporal correlations and attack patterns in network traffic. To overcome these limitations, we present a hybrid approach in this work that combines the probabilistic nature of Hidden Markov Models (HMM) with the temporal feature learning of Long Short-Term Memory (LSTM) networks. Experiments are conducted on a variety of attack vectors present in benchmark datasets such as the CICIDS2017 dataset and NSL-KDD dataset, which include both contemporary and traditional attacks. Our findings show that the hybrid model outperforms individual HMM and LSTM models in terms of accuracy, precision, recall and F1-score. The combination of statistical and deep learning approaches enhances the capabilities to identify various types of cyber threats, while minimizing false positives. This approach offers a scalable and robust approach for next-generation cloud-based intrusion detection in real-world network settings.
Bovine Cysticercosis in Durame Municipal Abattoir, Central Ethiopia Region
A cross-sectional study was conducted at the Durame municipal abattoir with the aim of determining the prevalence of bovine cysticercosis in slaughtered cattle. A total of 384 randomly selected cattle of different ages, sexes, breeds, body conditions, and origins were examined through routine ante-mortem and detailed post-mortem inspection procedures. The overall prevalence of bovine cysticercosis was found to be 9.6% (37/384). Although slight variations in prevalence were observed among different categories: breed, sex, age, body condition, and origin. None of these factors showed a statistically significant association with infection (p>0.05), indicating widespread environmental exposure. Anatomical distribution of cysts revealed a higher predilection for the tongue (56.82%), followed by masseter muscles (22.72%), shoulder muscles (11.36%), and heart (9.1%). The findings demonstrate that bovine cysticercosis is moderately prevalent in the study area and poses both public health and economic concerns. Therefore, integrated control measures focusing on improved sanitation, enhanced meat inspection, and public awareness on safe meat consumption are essential to reduce the burden of the disease.
Designing Scalable AI Systems for Continuous Monitoring, Fault Detection, and Operational Intelligence
Modern digital platforms are expected to operate continuously across heterogeneous clouds, microservices, data pipelines, AI-enabled applications, and cyber-physical workflows. In such environments, conventional monitoring is no longer sufficient because it reports isolated symptoms instead of generating system-level understanding. This paper proposes an architecture-centered research framework for scalable AI systems that unify continuous monitoring, fault detection, and operational intelligence into a closed-loop capability. The framework integrates telemetry collection, semantic data normalization, learning-based anomaly detection, graph-based dependency reasoning, and policy-aware decision intelligence so that systems can move from reactive alerting to evidence-based adaptation. The paper synthesizes advances in observability engineering, log intelligence, software defect prediction, automated testing, vulnerability detection, and domain-specific operational analytics to define a layered reference architecture suitable for software-intensive and distributed environments. Rather than presenting fabricated benchmark outcomes, the paper contributes a research-grade design blueprint, a model taxonomy, and an evaluation agenda for future empirical validation. The framework is intentionally cross-domain: it can support cloud-native software systems, healthcare and pharmacy operations, customer AI platforms, financial systems, manufacturing, and supply-chain networks. The paper argues that scalable operational intelligence emerges when telemetry, prediction, and action are treated as a single architectural problem rather than as disconnected toolchains.
Privacy-Enhancing Machine Learning with Differential Privacy
Privacy concerns in machine learning have become increasingly critical as AI systems process sensitive personal, financial, healthcare, and behavioral data. Traditional machine learning approaches often require centralizing large datasets, raising risks of data breaches, unauthorized access, and privacy violations. Differential privacy (DP) has emerged as a formal framework to quantify and guarantee privacy protection while enabling model training on sensitive data. By introducing controlled noise into data or model computations, differential privacy ensures that the inclusion or exclusion of a single individual’s data has minimal impact on model outputs, providing strong mathematical privacy guarantees. Privacy-enhancing machine learning (PEML) techniques incorporating differential privacy are applied across domains such as healthcare, finance, recommendation systems, and federated learning. These approaches balance the trade-off between data utility and privacy, enabling collaborative AI without exposing sensitive information. Challenges include maintaining model accuracy under privacy constraints, computational overhead, and adaptive threat resistance. Future directions involve integrating DP with federated and decentralized learning, adaptive privacy budgets, algorithmic optimization, and explainable privacy-aware AI. Differential privacy represents a cornerstone in building trustworthy, privacy-preserving AI systems in an increasingly data-driven world.
Swarm robotics and cooperative AI represent a transformative approach in robotics and artificial intelligence, drawing inspiration from collective behaviors observed in natural systems such as ant colonies, bird flocks, and fish schools. By leveraging distributed control, decentralized decision-making, and local communication among multiple agents, swarm robotics enables scalable, robust, and flexible multi-agent systems capable of tackling complex tasks. Cooperative AI integrates algorithms for coordination, task allocation, and collaborative learning to optimize group performance while maintaining individual autonomy. Applications span autonomous exploration, search and rescue, environmental monitoring, logistics, agriculture, and defense. This article explores the principles, architectures, coordination strategies, learning mechanisms, and real-world applications of swarm robotics and cooperative AI. It also addresses challenges including scalability, communication constraints, fault tolerance, ethical considerations, and human-swarm interaction. Future directions involve integrating advanced AI techniques such as reinforcement learning, neuro-symbolic reasoning, and edge intelligence to enhance adaptability, resilience, and efficiency in collective robotic systems. Swarm robotics and cooperative AI promise to redefine automation, multi-agent intelligence, and collective problem-solving in dynamic and uncertain environments.
Structure, Thermal Stability, and Morphology of ZnS Nanoparticles Prepared by Chemical Displacement Routes
Zinc sulfide (ZnS) nanoparticles have evoked research interest because of its extensive band gap, thermal stability, and potential applications in optoelectronic, photocatalytic and sensing applications. This paper has also used a controlled chemical displacement route in synthesizing ZnS nanoparticles, which provides a less complex and inexpensive approach to particle preparation and better control over the process. Optimized reaction conditions were used to perform the synthesis in order to get phase-pure material with homogeneous nanoscale dimensions. The crystalline structure of the ZnS was ascertained through structural analysis, which established the crystal to be of ZnS with the prevalence of cubic crystals, and morphological analysis showed the presence of almost spherical nanoparticles with minimal agglomerates. The thermal characterization also showed that the synthesized ZnS nanoparticles showed high stability, thus making them suitable to the high temperatures and device-oriented purposes. An assessment on the effects of the synthesis parameters on crystallite size, surface morphology and thermal behavior was done systematically in order to develop clear structure-property relationships. Results indicate that the chemical displacement-based method allows high resolution in the nanoparticle design without involving complicated and energy-demanding preparation procedures. In general, this paper identifies chemical displacement synthesis as an effective and viable method of manufacturing ZnS nanoparticles with desirable structural stability, morphological conformity, and thermal stability, which in turn, allows their usage in demanding functional nanomaterials.
Sustainable Design and Application-Driven Selection of Thermoplastic Elastomers in Modern Rubber Manufacturing
The present paper offers a holistic, practice-based model in the choice of thermoplastic elastomers (TPEs) in modern rubber production with a focus on the sustainable design approach. The thermoplastic elastomers consisting of a combination of the elasticity characteristic of traditional vulcanized rubber with the ease with which thermoplastics are processed have become key to decarbonization, recycling, and lightweighting in the automotive, consumer, medical and industrial industries. But to be truly sustainable, it is not only a solution that involves the replacement of materials but a systemic overall approach that involves the sourcing of raw-materials (biobased feedstuff, reclaimed/rubber grinding), compounding and additives (compatibilizers, sustainable fillers), processing (energy and solvent reduction), and end-of-life (reuse, mechanical and chemical recycling, biodegradation when suitable). The paper integrates the latest innovations in TPE chemistry and processing, such as polyolefin TPEs (TPO/TPE-O), thermoplastic vulcanizates (TPV), thermoplastic polyurethanes (TPU), styrenic block copolymers (SBC/TPS), and thermoplastic polyester elastomers (TPEE), and projects them in envelopes in application performance indicators needed by the contemporary rubber products such as dynamic abrasion resistance, compression set, fuel/oil resistance, low-temperature flexibility, and long- The approach is pairing property-process matrices, lifecycle assessment (LCA) heuristics, and selection flowcharts to rank options of material selection by the criticality of use, manufacturing feasibility, environmental indicators (embodied carbon, recyclability, toxicity), and cost. The paper records the representative case studies, e.g., automotive door seals (high weathering, moderate abrasion), soft-touch consumer grips (aesthetics and tactile performance), and reclaimed tire-based TPE composites (circular economy approach) and illustrates how formulation options, e.g. grade of TPU vs TPV, presence of plasticizer, content of nanofiller, compatibility of compatibilizer, etc., compromise performance and sustainability. Quantitative data are given: the range of predicted embodied carbon (kg CO 2 -eq/kg) of major TPE families, approximate thresholds of recycled content with current technology, meeting standard mechanical property requirements, and comparative energy consumptions between extrusion and injection moulding pathways. The paper also reviews additive technologies which reduce environmental impact, bio-based plasticizers, silica and lignin fillers, low-ZnO curing aids, and non-toxic crosslinking strategies and how they impacted processability and long-term performance. Lastly, we also suggest an implementable selection algorithm (flowchart and decision tables) to industry practitioners combining regulatory requirements (e.g., RoHS, REACH), recyclability goals and end-use performance, thereby permitting a traceable, auditory path between the product requirement and material/formulation selection. The framework specifically references and develops literature in the field of rubber additives and sustainability (such as the article Emerging Trends in Rubber Additives for Enhanced Performance and Sustainability by the user), a way to pragmatically implement in manufacturing lines, yet provides insight of research gaps, including standardized eco-metrics of TPE blends, scalable devulcanization routes to reclaimed tire rubber (GGR) integration and long-term field testing of bio-based TPEs. The goal of this contribution is to have both a technical reference to materials engineers and a decision support tool to product designers who may want to re-engineer rubber products that have a lower carbon footprint, and still maintain good performance.
Thermoplastic Polyester Elastomers (Tpee)
Information Application and Organisational Performance in Commercial Banks in Yenagoa, Bayelsa State
This study examined the relationship between information application and organisational performance in commercial banks operating in Yenagoa, Bayelsa State, with particular focus on responsiveness and innovativeness. The study adopted a descriptive survey research design to enable the collection and analysis of quantitative data from respondents within their natural work environment. The population comprised employees of selected commercial banks in Yenagoa, including management, supervisory, and operational staff who are directly involved in the application and use of information systems. A sample size of ninety-two (92) respondents was selected using a purposive sampling technique. Primary data were collected through a structured questionnaire designed on a five-point Likert scale. The validity of the instrument was ensured through expert review, while reliability was confirmed using the Cronbach’s alpha method. Data collected were analysed using descriptive statistics such as mean and inferential statistics, specifically the Pearson Product-Moment Correlation Coefficient, with the aid of the Statistical Package for Social Sciences (SPSS) Version 23. The findings revealed that information application has a significant and positive relationship with organisational responsiveness and innovativeness in commercial banks in Yenagoa, Bayelsa State. The study concludes that effective application of information enhances organisational performance and recommends increased investment in information systems and staff capacity development to sustain competitiveness in the banking sector.
Information Management Procedures and Organizational Performance in the Banking Industry, Bayelsa State
The research examined the relationship between information management procedures and organizational performance in commercial banks in Bayelsa State. The population is made up of information processing skilled-employees of the studied commercial banks, Bayelsa state. From the human resource units, the figure provided for employees is summed up to be two hundred and forty-one (241). The sample size of this research is calculated by using Krejcie and Morgan's (1978) sample size determination table. To arrive at a sample size of 143. Questionnaire was used as the primary instrument for data collection. Descriptive and inferential analysis was utilized to analyzed collected data and test the formulated hypotheses. The results showed a positive and significant relationship between the study dimensions and the dependent variable, which means that information storage and information safety have a positive and significant relationship with organizational performance. Based on this result, the study concludes that information management procedures have a significant relationship with employee organizational performance in commercial banks, Bayelsa State. There is a need for managers to take advantage of the technological capability to support information application processes. In particular, organizations should use technology to map the location of specific types of knowledge, thereby facilitating the application and sharing of information.
Structural Communication Barrier and Administrative Deficiency in Bayelsa State Ministries
This study examines the interface between structural communication barriers and administrative deficiencies within ministries in Bayelsa State, Nigeria. Specifically, it examines how rigid structures, delayed information dissemination, and ineffective feedback channels affect underachievement and ineffectuality in selected Ministries, Departments, and Agencies (MDAs). Utilizing a descriptive survey research design, the study collected data through structured questionnaires administered to 154 administrative staff using a combination of census and simple random sampling techniques. The analysis of data, done using SPSS version 25.0, utilized descriptive statistics and Spearman's Rank Correlation Coefficient in measuring communication structures and administrative outcomes. The findings demonstrated a strong to moderate positive relationship between administrative weaknesses and communication structural barriers, in terms of 0.511 and 0.409 correlation coefficients for ineffectiveness and poor performance, respectively. The study confirms that structural communication weakness in ministries significantly hinders staff performance and delivery of services. It demands serious internal audits of communication systems, training of staff on communication, and engagement with digital communication tools, as well as policy-level change in order to enhance communication effectiveness.
The Expanding Complexity of Medicine–Herb–Food Combinations
Modern pharmacology usually tests one drug at a time. However, in the real world, patients take pharmaceuticals alongside herbal supplements and bioactive food components.This article looks at the massive complexity of these combinations and what they mean for patient safety. Using data from DrugBank and botanical databases, we estimate that humans regularly encounter about 33,987 bioactive substances. This leads to more than 570 million potential pairwise interactions a scale that makes it impossible for doctors to memorize or for labs to test. Adverse drug reactions should be seen as a result of a complex system rather than just a "bad drug." We argue for a shift toward systems pharmacology and the use of computer tools to manage the unpredictability of modern medicine.
Complementary and Alternative Medicine (CAM)
Automated Prescription Analysis and Alternative Drug Recommendation System Using OCR and NLP
The integration of Optical Character Recognition (OCR) and Natural Language Processing (NLP) has revolutionized medical applications, particularly in automating prescription management systems. This paper presents a comprehensive system architecture that combines advanced OCR techniques for handwritten prescription recognition with NLP methodologies for semantic extraction and processing of medical text. The proposed solution effectively addresses the challenges posed by handwritten prescription variability, complex medical terminologies, and diverse pharmacy settings. Detailed technical implementations, including image preprocessing, model training, and data annotation strategies, are discussed. Experimental results validate the system's efficacy, highlighting significant improvements in accuracy, operational efficiency, and error reduction compared to existing manual and automated methods. Case studies and user feedback from pharmacy deployments demonstrate practical advantages and challenges, providing insights into system impact and performance. The paper concludes with a discussion on limitations, regulatory considerations, and future directions for integrating advanced AI technologies to further enhance automated prescription management
Real-Time Reservoir Management in Petroleum Engineering Using Proxy Modeling and AI-Based Optimization Techniques
Managing reservoirs well helps maximize the output of oil and gas, keeps costs low and protects the environment. The study looks at how using proxy models and AI optimization can make real-time reservoir management more effective in petroleum production. Proxy models are used as simplified versions of reservoir simulations to save time in evaluation and decision-making. Using AI, such as machine learning and genetic algorithms, helps to manage reservoirs under uncertain circumstances. We provide examples of these methods being used and discuss in detail the advantages, difficulties and future prospects they bring.
Federated Learning and AI-Driven Edge Computing for Secure and Efficient Healthcare Data Processing
FL and AI-driven edge computing promise to boost both the safety, efficiency, and privacy of processing healthcare information. This work discusses how FL and edge computing come together to solve problems in healthcare, such as data security, performance, and immediate processing of information. Our report covers the current topics in research, outlines the major problems, and provides stories outlining how these technologies are applied in practice. Besides, we develop a plan for a good healthcare system design that depends on FL, edge computing, and analyze it by running simulations and working with real data. At the conclusion, the paper considers future studies and how these technologies will affect healthcare as a whole.
Design and Implementation of a Blockchain-Assisted Federated Learning Framework for Privacy-Preserving Healthcare Data Analytics
The use of both federated learning and blockchain ensures better privacy for healthcare data analysis. Traditional approaches to machine learning in healthcare are affected by risks related to data breaches, data privacy, and having data from several sources that may be biased. In federated learning, several medical institutions join forces in training data without giving away patients’ private details. Even so, there are problems that FL has to face, such as hidden practices, the possibility of bad quality in training, and the struggle to ensure trust and justice for all participants. Because of these obstacles, a blockchain-enabled federated learning platform for healthcare analytics is presented in this paper. The use of blockchain allows the framework to guarantee clear, unchanged, and trackable model updates, along with effective administration of members and encouragement for honest teamwork among them. The architecture design involves incorporating bias-reduction measures in the FL process, relying on blockchain to gather and study models, encourage fairness, and identify any dishonest or malicious parties. Simulated healthcare settings have proven that the framework increases the precision and fairness of models and significantly cuts down the chances of private information being leaked by putting parameters under decentralized control. Because the system resists single issues of failure, meets different needs, and is scalable, healthcare analytics benefit from it, and this helps improve fairness and quality of healthcare. Advanced ways of motivating users and using new technologies, such as edge computing, could be added in the future to protect privacy and enhance performance.
Deep Transfer Learning for Automated Diagnosis of Rare Diseases in Medical Imaging
Finding and identifying rare diseases through images is a lasting challenge in routine medical work because of the diseases' uncommon nature and limited data. When it comes to seeing how many diagnostic tools work, Convolutional Neural Networks (CNNs) stand out with their great potential. One difficulty with these models is that their outcomes are less impressive when the data they receive is not adequate, which is often true for rare diseases. To overcome this drawback, this research looks at applying deep transfer learning to support stronger identification of rare diseases using knowledge from common or large-scale images. Here, we review and analyze various models (e.g., VGG, ResNet, DenseNet) fully, and we study how fine-tuning these models works for datasets such as ocular melanoma, Gaucher disease and neurofibromatosis. We rely on a broad method that uses data filtering, adapts the model to the clinical environment, measures the results using metrics and includes the Grad-CAM technique for interpreting what the model learns. It is clear from the findings that using transfer learning leads to better classification accuracy, sensitivity and specificity for every dataset. The highest accuracy, of 92.4%, was found in Gaucher disease classification using the modified DenseNet201 model with a hybrid loss function. Being able to show the problem areas in patient images with Grad-CAM made it easier for medical experts to trust the model’s performance. The research confirms that transfer learning is helpful for diagnosing rare diseases with the help of images and suggests it could improve early diagnosis and treatment.
An Energy-Efficient Multi-Layer Secure Routing Protocol for Post-Quantum Cryptographic Networks in Internet of Medical Things (IoMT)
With IoMT, healthcare is being changed rapidly by allowing data from patients to be monitored and managed remotely. But the fact that IoMT devices have weak resources, need strict timing and send sensitive data makes it challenging to secure and operate efficiently. As quantum computers are almost ready to break conventional cryptography, it is important to create post-quantum cryptographic methods for the IoMT. The paper introduces an Energy-Saving, Layered and Safe Routing Protocol (EEMSRP) for use in post-quantum cryptographic networks in the IoMT. It includes several layers of security, using lattice-based post-quantum cryptography and improved routing methods to cut back on energy use. The framework uses hierarchical architecture to organize devices, adopts clustering and relies on lightweight algorithms for post-quantum key exchange. Results from simulations find that EEMSRP is able to save up to 30% of the energy used by existing quantum-resistant protocols, but still remains secure against eavesdropping, man-in-the-middle attacks and quantum-based cryptanalysis. Because of its multi-layer defense, the protocol guarantees the privacy, accuracy and continued accessibility of IoMT data. This research provides a base for upcoming secure and power-efficient communication standards in the post-quantum world, which is important for guarding private medical details and guaranteeing reliable IoMT use.