Optimal Resource Management Research Articles

Deep Learning Based Traffic Management in Knowledge Defined Network

In recent Artificial Intelligence developments, large datasets as knowledge are a prime requirement for analysis and prediction. To manage the knowledge of the network, the Data Center Network (DCN) has been considered a global data storage facility on edge servers and cloud servers. In recent research trends, knowledge-defined networking (KDN) architecture is considered, where the management plane works as the knowledge plane. The major network management task in the DCN is to control traffic congestion. To improve network management, i.e., optimized resource management, enhanced Quality of Service (QoS), we propose a path prediction technique by combining the convolution layer with the RNN deep learning model, i.e., Convolution-Long short-term memory network as Convolution-LSTM and the bi-directional long short-term memory (BiLSTM) network as Convolution-BiLSTM. The experimental results demonstrate that, in terms of many metrics, i.e., network latency, packet loss ratio, network throughput, and overhead, our proposed methodologies perform better than the existing works, i.e., OSPF, FlowDCN, modified discrete PSO, ANN, CNN, and LSTM-based routing approaches. The proposed approach improves the network throughput by approximately 30% and 12% as compared to existing CNN and LSTM-based routing approaches, respectively.

A Fish-Counting Method Using Fusion of Spatial Sensing and Temporal Information

In modern aquaculture, accurate and efficient fish counting is crucial for the optimization of resource management and the enhancement of production profitability. Acoustic methods, known for their low energy consumption and extensive detection range, are widely utilized for underwater fish counting. However, traditional acoustic echo methods heavily rely on prior knowledge of fish schools and specific distribution models, leading to complexity and limited adaptability in practical applications. This paper introduces a fish-counting approach that integrates spatial sensing with temporal information. Initially, a spatial sensing matrix is constructed using ultrasonic Frequency-Modulated Continuous Wave (FMCW) technology, which facilitates the extraction of multidimensional features from fish echoes and reduces reliance on prior knowledge of fish schools. Subsequently, temporal information is extracted from echo signals using a Long Short-Term Memory (LSTM) network model, preventing missed detections caused by obstructions in single fish echoes during echo sessions. Finally, by fusing spatial and temporal feature information and employing a data-driven approach, we achieve fish counting while avoiding potential issues arising from improper selection of statistical distribution models. Tests on real fish datasets show that our proposed method consistently outperforms conventional statistical echo methods across all metrics, demonstrating its effectiveness in accurate fish counting.

Trend analysis and learning-based groundwater level modelling over a tropical river basin

Groundwater trend analysis and modeling is challenging due to partially explicable factors and unexplained human influence. Hurst index, Sequential Mann-Kendall, and Classical Mann-Kendall test offers a comprehensive groundwater trend analysis. A learning-based approach is developed to model groundwater levels using climatological variables of rainfall and temperature. The study is on 24 locations over Periyar river basin of Kerala, India, for the years 1996-2019, and during January, April, August, and November (JAAN) months. The significant trends were observed at 14 locations in at least one of the JAAN months, which is about 58%. Out of these, 8 locations exhibited positive trend signaling a decline in groundwater supplies. The developed model yielded notable improvements in precision with 50%, 79%, 75% and 83% of the locations in month-wise order. To gauge the model performance, observed and predicted location clusters obtained using k-means clustering are juxtaposed for the years 2017-2019, on both individual and average basis. This assessment indicated only one well transitioning in August, with the average approach resulting in a closer match to the original clustering for most of the wells. These findings shall benefit future stakeholders and policymakers in optimizing resource management strategies over the basin and wider.

Integrating Sentinel-1 data and machine learning for effective paddy field monitoring in Cauvery Delta Zone, Tamil Nadu, India.

Paddy crop mapping is essential for agricultural monitoring, ensuring food security, and enhancing resource allocation. This study observes the Cauvery Delta Zone (CDZ), recognized as the rice bowl of Tamil Nadu and a crucial area for paddy farming in India. The study seeks to elucidate rice-growing trends over three years (2021-2023) by examining the regional variability of the Radar Vegetation Index (RVI) throughout a paddy crop growing season (June to September). A temporal examination of the RVI and the cross-polarization ratio (VH/VV) demonstrates a good correlation of 0.79, enhancing the comprehension of paddy crop dynamics. Additionally, machine learning algorithms such as random forest (RF), support vector machine (SVM), and decision tree (DT) are utilized on radar data in both VV and VH polarizations to improve the classification of paddy fields. The accuracy evaluation indicates that the RF algorithm exhibits superior performance, achieving accuracies of 86.72% in VH mode and 86.42% in VV mode. The results underscore the efficacy of integrating radar-based indices with machine learning methodologies for proficient agricultural surveillance. These findings offer essential assistance for enhancing crop yield, optimizing resource management, and enabling informed decision-making in the Cauvery Delta Zone.

Resource extraction, FinTech, and social equal access: A quantitative analysis of resource-rich countries

Resource-rich countries hold potential for the alleviation of social disparities and the promotion of equal opportunities. Well-managed natural resource extraction can spur economic growth, create employment, and provide funds for some of the vital social programs that improve social accessibility to education, healthcare, and infrastructure altogether. Contingent upon such change, this study investigates the impact of natural resource extraction on equitable access for all members of society across countries that happen to be endowed with resources, using data for the period 2011–2022. The Driscoll-Kraay and Panel Quantile Regression methods are used to see how an improvement in resource extraction could benefit society by mainly reducing inequality. Key findings confirm that natural resource extraction positively influences equal access across social domains. FinTech improves directly on the degree of social accessibility and moderates toward sustainable, more inclusive outcomes in the effect of natural resource extraction. The control variables point towards the productive capacities supporting equal access. In contrast, military spending and high energy consumption have adverse effects. The results reveal a dual role of resource extraction and FinTech in helping to achieve inclusive development and underscore policies that would ensure optimal resource management in facilitating equitable societal growth.

Data-Driven Agropreneurship (DDA): Empowering Farmers through Predictive Analytics

Data-Driven Agropreneurship (DDA) represents a transformative approach in modern agriculture, utilizing predictive analytics to empower farmers with data-driven decision-making capabilities. This paper explores the significant benefits of adopting DDA, including improved crop yields, optimized resource management, and enhanced market opportunities. Despite these advantages, several challenges impede its widespread adoption, such as data access, technological literacy, and inadequate infrastructure. This paper also discusses potential solutions to overcome these barriers, emphasizing the importance of government support, public-private partnerships, and capacity-building initiatives. By addressing these challenges, stakeholders can foster an environment conducive to the successful implementation of DDA, ultimately leading to a more sustainable, productive, and profitable agricultural sector.

A REVIEW OF MACHINE LEARNING TECHNIQUES APPLICATIONS IN ENVIRONMENTAL SCIENCE

Advances in Machine Learning (ML) and Data Science (DS) hold immense potential to transform various aspects of environmental science (ES). DS, a broad field focused on extracting insights from data using techniques like statistical analysis plays a crucial role in this process. Machine learning, on the other hand, specializes in creating algorithms that enable computers to learn from data and make predictions. Together, these technologies can deepen our understanding of complex environmental systems, refine predictive models for climate change, support conservation efforts, and optimize resource management practices. Such scientific discovery will enhance ES to make autonomous, real-time decisions by deriving valuable insights from extensive data. By analysing large datasets, machine learning algorithms can reveal hidden patterns and insights, empowering scientists to make data-driven decisions and tackle environmental challenges more effectively. This article offers a review of the fundamental concepts of Machine Learning, Deep Learning, and Data Analytics for two groups: individuals familiar with ML who seek to expand their knowledge, and domain scientists passionate about integrating these transformative tools into their research in the environmental science profession.

Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine.

Health data comprise data from different aspects of healthcare including administrative, digital health, and research-oriented data. Together, health data contribute to and inform healthcare operations, patient care, and research. Integrating artificial intelligence (AI) into healthcare requires understanding these data infrastructures and addressing challenges such as data availability, privacy, and governance. Federated learning (FL), a decentralized AI training approach, addresses these challenges by allowing models to learn from diverse datasets without data leaving its source, thus ensuring privacy and security are maintained. This report introduces FL and discusses its potential in transfusion medicine and blood supply chain management. FL can offer significant benefits in transfusion medicine by enhancing predictive analytics, personalized medicine, and operational efficiency. Predictive models trained on diverse datasets by FL can improve accuracy in forecasting blood transfusion demands. Personalized treatment plans can be refined by aggregating patient data from multiple institutions using FL, reducing adverse reactions and improving outcomes. Operational efficiency can also be achieved through precise demand forecasting and optimized logistics. Despite its advantages, FL faces challenges such as data standardization, governance, and bias. Harmonizing diverse data sources and ensuring fair, unbiased models require advanced analytical solutions. Robust IT infrastructure and specialized expertise are needed for successful FL implementation. FL represents a transformative approach to AI development in healthcare, particularly in transfusion medicine. By leveraging diverse datasets while maintaining data privacy, FL has the potential to enhance predictions, support personalized treatments, and optimize resource management, ultimately improving patient care and healthcare efficiency.

Priority/Demand-Based Resource Management with Intelligent O-RAN for Energy-Aware Industrial Internet of Things

The last decade has witnessed the explosive growth of the internet of things (IoT), demonstrating the utilization of ubiquitous sensing and computation services. Hence, the industrial IoT (IIoT) is integrated into IoT devices. IIoT is concerned with the limitation of computation and battery life. Therefore, mobile edge computing (MEC) is a paradigm that enables the proliferation of resource computing and reduces network communication latency to realize the IIoT perspective. Furthermore, an open radio access network (O-RAN) is a new architecture that adopts a MEC server to offer a provisioning framework to address energy efficiency and reduce the congestion window of IIoT. However, dynamic resource computation and continuity of task generation by IIoT lead to challenges in management and orchestration (MANO) and energy efficiency. In this article, we aim to investigate the dynamic and priority of resource management on demand. Additionally, to minimize the long-term average delay and computation resource-intensive tasks, the Markov decision problem (MDP) is conducted to solve this problem. Hence, deep reinforcement learning (DRL) is conducted to address the optimal handling policy for MEC-enabled O-RAN architectures. In this study, MDP-assisted deep q-network-based priority/demanding resource management, namely DQG-PD, has been investigated in optimizing resource management. The DQG-PD algorithm aims to solve resource management and energy efficiency in IIoT devices, which demonstrates that exploiting the deep Q-network (DQN) jointly optimizes computation and resource utilization of energy for each service request. Hence, DQN is divided into online and target networks to better adapt to a dynamic IIoT environment. Finally, our experiment shows that our work can outperform reference schemes in terms of resources, cost, energy, reliability, and average service completion ratio.

A multi-agent deep reinforcement learning approach for optimal resource management in serverless computing

A multi-agent deep reinforcement learning approach for optimal resource management in serverless computing

Predictive Modeling in IoT-Driven Agriculture: A Comparative Study of Regression and Classification with LIME Interpretability

Abstract. This study aims to compare the performance of regression and classification machine learning models in crop prediction by analyzing data from IoT-driven agriculture. Linear regression and random forest regression models were used to predict the percentage of root growth dry matter, while logistic regression and support vector machines were employed to classify crop production regions. To enhance model interpretability, the LIME tool was applied to analyze feature importance. The experimental results demonstrate that the models perform well in terms of prediction accuracy, and LIME provided clear feature explanations, helping identify the variables with the greatest impact on prediction outcomes. This research offers data-driven insights for optimizing resource management in smart agricultural systems.

Implementasi K-Means Clustering Untuk Rekomendasi Kelas Unggulan di SMK 1 Teknologi dan Rekayasa Mimika

New student admission is a strategic step for schools to ensure the quality of education and optimal resource management. This research aims to analyse new student candidate data at SMK Negeri 1 Teknologi dan Rekayasa Mimika using the K-Means clustering method. Student data is grouped into three clusters based on academic grades to detect prospective students who have the potential to enter superior classes. The analysis results show that cluster 3 has the highest average academic score, making it possible for students in cluster 3 to enter the general superior class. cluster 2 showed excellence in certain aspects, particularly E grades, making it suitable for specific programmes oriented towards talent development. Finally, cluster 1 has the lowest average score and requires further assistance to improve academic performance. Evaluating the cluster results using Silhouette Score, all three clusters are in the "Good" category (0.51-0.70), with the highest score in cluster 3 (0.594). The recommendation from this study is to prioritise students from Cluster 3 for general superior classes and some from cluster 2 for special superior programs, so as to support the vision of SMK Negeri 1 Technology and Engineering Mimika in improving the quality of data-based education.

CTDNets: A High-Precision Hybrid Deep Learning Model for Modulation Recognition with Early-Stage Layer Fusion

To further enhance the recognition accuracy of automatic modulation recognition, improve communication efficiency, strengthen security, and optimize resource management, this paper designs a high-precision hybrid deep learning model featuring early-stage layer fusion. This model combines with Convolutional Neural Networks (CNN), Transformers, and Deep Neural Networks (DNN) to enhance the model’s feature extraction capabilities, thereby improving modulation recognition accuracy. Experiments are performed on RadioML2016.10a and RadioML2018.01a, and the results show that this architecture can effectively combine the advantages of different types of models, making the overall performance more robust and suitable for complex automatic modulation recognition problems.

Optimizing Digital Transition: Addressing Challenges in Modernizing Inventory Systems in Primary Healthcare Facilities

This study investigates the challenges and potential solutions associated with modernizing inventory systems in primary healthcare facilities in the National Capital Region (NCR) of the Philippines, emphasizing operational efficiency, resource management and staff training. A descriptive research design was applied, using both quantitative and qualitative approaches. Data was collected from 67 trained staff members through purposive sam-pling, ensuring diverse perspectives across various organizational roles. The questionnaire was evaluated in relation to users' perceptions of satis-faction, operational efficiency, and the perceived difficulties with digital inventory systems. Reliability is established with the help of Chronbach's Alpha, and ANOVA, used in establishing the relationship between user sat-isfaction and operational effectiveness. From the thematic analysis of the questionnaire, lack of infrastructure, lack of training, and fractured supply chains were pointed out as being the obvious challenges that reduce the smooth digital transition. The study found that while digital transfor-mation has the potential to enhance inventory management and healthcare delivery, significant barriers must be addressed to achieve full adoption. Overall, findings help highlight the need for focussed training with strong digital solutions in optimizing resource management and service delivery in public health settings, providing valuable insights for policymakers and healthcare administrators.

A legal approach: artificial intelligence, agribusiness and the 2030 agenda

Artificial intelligence (AI) is revolutionizing the agribusiness sector, offering significant potential to improve efficiency, sustainability and productivity. By analyzing large amounts of data, AI can optimize resource management, predict crop productivity and detect diseases early, leading to increased agricultural production and reduced environmental impact. However, integrating AI into agriculture faces several challenges, including technological infrastructure, data accessibility, and regulatory hurdles. To fully realize the benefits of AI, it is crucial to address these challenges and develop a robust legal framework that protects intellectual property, ensures data privacy and encourages innovation. By aligning AI-driven agricultural practices with the UN Sustainable Development Goals, we can create a more sustainable and equitable future for global food production.

Intelligent Resource Management in Cloud Computing: AI Techniques for Optimizing DevOps Operations

Efficient resource management is a cornerstone of cloud computing, particularly for DevOps operations where automation and scalability are critical. Traditional resource allocation approaches often fall short in dynamic environments, leading to over-provisioning, under-utilization, or service disruptions. This paper explores how artificial intelligence (AI) techniques can optimize resource management in cloud environments, enhancing the performance and efficiency of DevOps workflows. We examine methods such as predictive analytics, reinforcement learning, and anomaly detection, providing case studies and actionable insights for implementing intelligent resource management systems.

Fermatean Fuzzy TOPSIS Method and Its Application in Ranking Business Intelligence-Based Strategies in Smart City Context

With the expansion of smart cities, the use of business intelligence (BI) has emerged as a crucial tool for resource optimization, increasing efficiency, and improving the citizens’ quality of life. BI enables companies to make better strategic decisions by analyzing vast amounts of urban data, helping them remain competitive in the dynamic smart city environment. This study utilizes content analysis and the Fermatean Fuzzy TOPSIS (FF-TOPSIS) method to rank the strategies based on business intelligence in the context of smart city. Initially, relevant criteria were identified through content analysis, and subsequently, five strategies were developed and ranked based on these criteria. The results revealed that the "Development of IOT-enabled smart networks (S2)" ranked highest, as it plays a significant role in optimizing resource management and enhancing urban service performance, thereby contributing greatly to the advancement of smart cities. "Process automation and the deployment of robotic systems (S5)" ranked second, as it enhances efficiency and reduces human errors. "Cloud platform integration for seamless access to data and services (S3)" also proved to be of considerable importance, ranking third, as it provides seamless access to data and services. " Artificial intelligence deployment for predictive analytics and process optimization (S4)" ranked fourth and was vital for predictive analytics and process optimization, while " Big data analytics for smart decision-making (S1)"—despite its importance—ranked fifth. Urban managers should prioritize the development of IOT networks to fully leverage their potential for resource management and efficiency gains. Following this, attention to process automation and AI integration can significantly enhance the quality of life for citizens and reduce urban costs.

Influence of COVAW Capacity Building on Gender-Based Violence Program Performance in Narok County, Kenya

Purpose: The study explores the influence of employee capacity building on the performance of gender-based violence (GBV) programs in Narok County, Kenya. The specific objectives include examining the effect of leadership style, the impact of community empowerment on program effectiveness, the role of resource allocation, and the influence of organizational innovation on program outcomes. Methodology: Utilizing a descriptive survey design grounded in modernization theory and performance and human capital theory, the research targets full-time staff from ten GBV programs operated by COVAW. A sample of 92 respondents is collected through convenience and stratified sampling techniques, incorporating both community participants and program personnel. Data collection employs interview schedules and semi-structured questionnaires, with quantitative data analyzed using SPSS software and qualitative data thematically analyzed. Findings: Findings reveal a prevailing democratic leadership style that fosters collaboration; however, community empowerment faces challenges due to inadequate integration and mobilization. The study concludes that there is a significant interconnectedness among leadership, community engagement, resource management, and organizational adaptability in enhancing GBV program performance. Unique Contribution to Theory, Policy and Practice: Recommendations include adopting diverse leadership styles, strengthening community engagement, and optimizing resource management practices to ensure the sustainability and impact of GBV initiatives.

The genesis of entropy in the conditions of the digital economy and socio-economic relations: globalization challenges and opportunities for sustainable development

The article traces the genesis of entropy in the minds of the digital economy and social economic developments, which stems from the rapid digitalization and accelerated globalization of current economic systems. The author has conducted research to highlight how digital transformations bring increased insignificance, creating new challenges for traditional models of management and social interaction. In the article, special attention is paid to the analysis of the legacy of the shift in the level of entropy for social unevenness, cybersecurity, technological accessibility and adaptation of enterprises to new minds in the market, which is We are important in the current information space. The emphasis is on the fact that the growing information and economic entropy is a critical factor that contributes to the ongoing development, which can lead to the destabilization of social and economic structures. The influx of globalization processes on entropy processes in the economies of the countries that are developing, where digital inequality is a factor in the growth of entropy, has been closely observed. Also, the role of government regulation in minimizing the negative legacies of entropy through promoting the policy of digital integration and supporting the development of harmonization of all processes has been analyzed. Opportunities for changing entropy are seen through the use of new innovative technologies, such as artificial intelligence, great data and automation, which allow optimizing resource management and reducing the level of insignificance. It is emphasized that the genesis of entropy in the digital economy requires further investigation, from which the economic stability of systems, as well as social harmony and equality have access to resources. The article also makes recommendations for further direct investigation of entropy in the minds of Swedish digitalization and globalization, which is aimed at developing strategies for managing complexity and insignificance in current social and economic systems.

Enhanced Recovery After Craniotomy: Global Practices, Challenges, and Perspectives.

The global demand for hospital care, driven by population growth and medical advances, emphasizes the importance of optimized resource management. Enhanced Recovery After Surgery (ERAS) protocols aim to expedite patient recovery and reduce health care costs without compromising patient safety or satisfaction. Its principles have been adopted in various surgical specialties but have not fully encompassed all areas of neurosurgery, including craniotomy. ERAS for craniotomy has been shown to reduce the length of hospital stay and costs without increasing complications. ERAS protocols may also reduce postoperative nausea and vomiting and perioperative opioid requirements, highlighting their potential to enhance patient outcomes and health care efficiency. Despite these benefits, guidelines, and strategies for ERAS in craniotomy remain limited. This narrative review explores the current global landscape of ERAS for craniotomy, assessing existing literature and highlighting knowledge gaps. Experts from 26 countries with diverse cultural and socioeconomic backgrounds contributed to this review, offering insights about current ERAS protocol applications, implementation challenges, and future perspectives, and providing a comprehensive global overview of ERAS for craniotomy. Representatives from all 6 World Health Organization geographical world areas reported that barriers to the implementation of ERAS for craniotomy include the absence of standardized protocols, provider resistance to change, resource constraints, insufficient education, and research scarcity. This review emphasizes the necessity of tailored ERAS protocols for low and middle-income countries, addressing differences in available resources. Acknowledging limitations in subjectivity and article selection, this review provides a comprehensive overview of ERAS for craniotomy from a global perspective and underscores the need for adaptable ERAS protocols tailored to specific health care systems and countries.