Application of Bayesian Networks for Autonomic Network Management

The electric power transmission network management is changing from decentralized to province-centralized mode to adapt to the development trend of power grid. For the current hierarchical management situation of transmission system, the paper mainly studies the centralized mode. Considering the difficulty of transform, security, the demand of bandwidth and augmentability factors, the paper proposes a hierarchical province-centralized mode based on Resource pool and states its architecture, realization methods and deployment mode of Network Manager System in Cloud, realizing the unified management, configuration and data sharing of the whole province. Deploying the network management system in the Clout is taking full use of the resource sharing, efficient management and on-demand resource of the Cloud to realize the intensification, promoting the scheduling intensive management level of Information and transmission. Introduction For a long time, the layered, graded, zoned management mode of electric power communication networks makes the network management system built and maintained by the way of multi-level scheduling mechanism. In order to fulfill the development trend of power grid, power transmission network management should be developed from the provinces and cities separated pattern to the province-centralized mode to realize unified management, unified configuration and sharing of the communication network data within province and achieve the purpose of concentrate and highly active management, reasonable resource-configuration and optimizing the structure of the network resources. Deploying the communication network management centrally is an effective way to enhance the level of intensive management, which, whereas, will cause the load that systems deal with increase sharply. However, Cloud computing technology integrates resources into form Cloud in order to get higher utilization of computing and storage, having a strong computing power and storage capacity. With the development of Cloud construction, deploying communication network management in the pool can improve data reliability and security. This paper puts forward a hierarchical province-centralized mode based on Cloud by researching on concentrated-model of Transmission Network Management, and describes the model in detail. Discussed Problems Power system communication network is the important technology support and infrastructure platform of the grid. As an important guarantee of power grid security, stability and economic running, communication network management system is the important technology means of the communication network management and maintenance. International Power, Electronics and Materials Engineering Conference (IPEMEC 2015) © 2015. The authors Published by Atlantis Press 596 Communication network management system At present, the general transmission network management system is divided into Element Management System(EMS), Subnet Management System(SNMS) and Network Management (NMS) in terms of the management level. EMS is based on the device manager and realizes the management of safety, configuration, performance and alarm for specific equipment; SNMS can divide the network elements into various subnets, not only manage the elements equipments but also the topological relation between them. NMS is a higher level than the SNMS and manage multiple SNMS systems at the same time through network. It configures the router for each business level, and also shows the network elements’ circumstances of each time slot and the business relationships at all levels in details. The relationship of different level network management system is showed as the figure 1.

Tactical battlefield network management presents unique challenges in so far as to (a) provide seamless end-to-end management operations involving inter-administrative domain management and heterogeneous networking technologies, and (b) support terminal and network equipment mobility management with large user population and over a wide geographical area. Furthermore, the volatile and unpredictable nature of these networks can cause the network elements to disappear sporadically, calling for an adaptive configuration management system that can adapt to the dynamics of the underlying network. Additionally, since the battlefield networks span side geographic domains, an efficient and scalable network management system (NMS) is of paramount importance. We propose a scalable NMS using a manager-agency paradigm together with an adaptive configuration management system (ACMS) for the tactical battlefield networks, that addresses each of the above mentioned challenges. We have also developed a proof-of-concept (POC) simulation model to demonstrate the proposed manager-agency-based NMS and ACMS. The ACMS, manager and agencies have been implemented using CORBA. Additionally a directory service system (DSS) that is designed to help the ACMS, is implemented in LDAP.

The evolution of telecommunications networks has led to a growing demand for more efficient management systems. Long-Term Evolution (LTE) networks, as a key enabler of high-speed mobile data communication, require sophisticated management mechanisms to maintain performance, reliability, and scalability. Network Management Systems (NMS) have traditionally handled these tasks, but the increasing complexity of LTE networks has made manual intervention less efficient. This manuscript explores the integration of Artificial Intelligence (AI) in the management of LTE networks through AI-powered NMS. The objective is to demonstrate how AI technologies can enhance network performance by automating fault detection, optimizing resource allocation, and ensuring seamless service delivery. By applying machine learning algorithms, real-time data analytics, and predictive maintenance, AI-powered NMS can significantly improve operational efficiency. This paper also presents case studies, discusses the challenges, and highlights the future potential of AI integration into network management.

Optimized machine learning approaches for identifying vertical temperature gradient on ballastless track in natural environments

A generic architecture for management and control of end-to-end quality of service over multiple domains

Performance optimization of wireless networks is typically complicated because of high computational complexity and dynamic channel conditions. Considering a specific case, the recent introduction of intelligent reflecting surface (IRS) can reshape the wireless channels by controlling the scattering elements' phase shifts, namely, passive beamforming. However, due to the large size of scattering elements, the IRS's beamforming optimization becomes intractable. In this article, we focus on machine learning (ML) approaches for complex optimization problems in wireless networks. ML approaches can provide flexibility and robustness against uncertain and dynamic systems. However, practical challenges still remain due to slow convergence in offline training or online learning. This motivated us to design a novel optimization-driven ML framework that exploits the efficiency of model-based optimization and the robustness of model-free ML approaches. Splitting the control variables into two parts allows one part to be updated by the outer loop ML approach while the other part is solved by the inner loop optimization. The case study in IRS-assisted wireless networks confirms that the optimization-driven ML framework can improve learning efficiency and the reward performance significantly compared to conventional model-free ML approaches.

Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying ("learning") patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as "black box" for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients' disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.

Background: Adolescent depression is highly prevalent in low- and middle-income countries (LMICs). Identifying top key risk factors is necessary to inform effective prevention program design. Machine learning (ML) offers a powerful approach to analyze complex multidomain of data to identify the most relevant predictors and estimate risks for mental health problems. This paper applies ML to study risks for adolescent depression to enhance adolescent depression prevention efforts in LMICs. Methods: Six ML approaches (e.g., Explainable Boosting Machine, random forests, and XGBoost) were applied to study the risks of depression. Data were drawn from a digital health intervention study conducted in Kenya (year 2024–2025, n = 269). Multiple domains of childhood and adolescent adversity and stress experiences were used to predict adolescent depression (using PHQ9-A). Findings: We found that ML was a valuable approach in the early identification of adolescents at risk for depression. Among the six ML approaches applied, the random forest approach outperformed other ML approaches, especially when multiple domains of risks were included. We also found that childhood adversity or home adversity alone were not strong predictors for depression. Adding adolescent stress experiences and community school adversity experiences significantly improves the accuracy and predictability of depression. Using the top-15 and top-20 ranking factors, we achieved 74.8% and 75.1% accuracy in depression prediction, which was similar to the accuracy when all 49 adverse/stress factors were included in the predictive model (78.3%). Conclusions: Innovative ML and modern predictive modeling approaches have the potential to transform modern preventive mental health care by better utilizing multidomain data to identify individuals at risk for developing depression early and identify top risk factors (for targeted individuals and/or populations). Findings from ML can inform tailored intervention design to better mitigate risks in order to prevent depression problem development. They can also inform the better utilization of resources to target high-need cases and key determinants, which is particularly useful for LMICs and low-resource settings. This paper illustrates an example of how to move toward this direction. Future research is needed to validate the approach.

Artificial Intelligence (AI) and Machine Learning (ML) are becoming common in our daily lives. The AI-driven processes significantly affect us as individuals and as a society, spanning across ethical dimensions like discrimination, misinformation, and fraud. Several of these AI & ML approaches rely on Knowledge Graph (KG) data. Due to the large volume and complexity of today's KG-driven approaches, enormous resources are spent to utilize the complex AI approaches. Efficient usage of the resources like hardware and power consumption is essential for sustainable KG-based ML technologies. This paper introduces the ethical and sustainability considerations, challenges, and optimizations in the context of KG-based ML. We have grouped the ethical and sustainability aspects according to the typical Research & Development (R&D) lifecycle: an initial investigation of the AI approach's responsibility dimensions; technical system setup; central KG data analytics and curating; model selection, training, and evaluation; and final technology deployment. We also describe significant trade-offs and alternative options for dedicated scenarios enriched through existing and reported ethical and sustainability issues in AI-driven approaches and research. These include, e.g., efficient hardware usage guidelines; or the trade-off between transparency and accessibility compared to the risk of manipulability and privacy-related data disclosure. In addition, we propose how biased data and barely explainable AI can result in discriminating ML predictions. This work supports researchers and developers in reflecting, evaluating, and optimizing dedicated KG-based ML approaches in the dimensions of ethics and sustainability.

Several organizations are incorporating the architecture being developed by the Open Systems Interconnection/Network Management Forum into their network management products and systems. It is expected that, as this architecture develops, it will be the basis for multivendor exchange of network management data for the next five to ten years. This architecture and key concepts being developed by the Forum to allow the interoperation of several network management systems using an open interface are summarized. These apply to the full range of networks that must be managed, including computer and communication, local area, wide area international, voice (public and private), and packet data networks. The concepts covered include: Conformant Management Entity (CME), which is a management system that supports the interoperable interface defined by the Forum; managed objects, the abstract representations of the real resources to be managed; and the interoperable interface, which consists of a defined set of protocols (using the OSI seven-layer protocol stack) over which particular messages about managed objects are exchanged.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Intrusion detection is a major challenge for security experts in the cyber world. Traditional IDS failed to detect complex and unknown cyber-attacks. Machine learning has become a vibrant technology for cybersecurity. There exists several machine learning algorithms to detect intrusion. Most classifiers are well suited to detect the attacks. However, improving accuracy and detecting unknown attacks in existing IDSs is a great challenge. Therefore, the detailed comparative study of various machine learning approaches such as artificial neural networks, support vector machine, decision tree, and hybrid classifiers used by researchers for intrusion detection are done. Deep learning is an emerging approach which suits well for large data. Deep learning techniques find optimal feature set and classify low-frequency attacks better than other techniques. This study also summarizes literatures in deep learning approaches such as deep auto-encoder, Boltzmann machine, recurrent neural networks, convolutional neural networks, and deep neural networks. Moreover, the datasets used in various literatures and the analysis of deep learning approaches based on the performance metrics are also done. Future directions to detect intrusion are also provided. This study in fact will be helpful to develop IDS based on artificial intelligence approaches such as machine learning and deep learning.KeywordsMachine learningIntrusion detectionFeature selectionDeep learningCyber securityClassifier

The telecommunication application server (AS) is an important part of the value-added service network in the next generation network(NGN) as the support platform. Remote, unified and secure management to a variety of services is implemented after the remote network management of the telecommunications application server. The network management system based on Simple Network Management Protocol (SNMP) is decided to utilized to analyse and study the network managed objects in AS because of the integration of telecommunications networks and computer networks on the network management and convenient, economical and flexible features of SNMP protocol. The network management agent module of AS is designed and an example is illustrated the details of the model. Results indicate the good performance of the network management agent.

Efficient fertilizer application is vital for enhancing maize production and profitability in Sub-Saharan Africa, where soil fertility varies widely across regions. This study aimed to develop a machine learning approach for generating site-specific fertilizer recommendations for maize production in Ghana and to evaluate its performance against conventional and semi-mechanistic approaches. A random forest machine learning model was trained on 482 maize yield experiments, consisting of 3136 yield observations collected from 1991 to 2020, to predict maize yield response to different fertilizer rates. The model incorporated multiple explanatory variables, including soil properties, climate conditions, and management practices, to generate fertilizer response curves from which fertilizer recommendations were derived for 14 sites across three agro-ecological zones in Ghana where field validation experiments were conducted. On these sites, the recommendations were compared with recommendations derived from the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS), Conventional Fertilizer Dose Response (CFDR), and Updated Conventional Fertilizer Dose Response (UCFDR) approaches and validated through field experiments. The machine learning approach generally recommended lower rates of phosphorus and potassium than the other approaches, while nitrogen recommendations were comparable. In the Guinea Savanna zone, the recommendations from the machine learning approach outperformed those from the other approaches, producing higher mean yields for three out of the four sites in the zone. In the Forest-Savanna Transition (FST) zone, the machine learning model recommendations led to higher mean yields at four sites, while the approaches based on QUEFTS and UCFDR performed best at two other sites. In the Semi-deciduous Forest zone, the recommendations of the QUEFTS approach resulted in the highest mean yields at three sites, and CFDR at one site. Despite high input prices during the period of experimentation, the machine learning approach-based recommendations demonstrated higher net profit margins in the FST zone, suggesting cost-effectiveness in this zone. These findings indicate that site-specific fertilizer recommendations are more efficient than blanket recommendations and that machine learning approaches offer a promising and innovative approach for generating cost-effective, site-specific fertilizer recommendations in tropical climates. • ML and QUEFTS approaches provided site-specific fertilizer recommendations. • ML and QUEFTS outperformed conventional fertilizer recommendation approaches. • CFDR and UCFDR were constrained by generalized, non-site-specific recommendations. • Machine learning supports sustainable fertilizer use.

ABSTRACTDue to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification and regression problems like the determination of stellar atmospheric parameters Teff, $\rm {\log g}$, and [Fe/H]. We propose an automated approach for the classification of stellar spectra in the optical region using convolutional neural networks (CNNs). Traditional machine learning (ML) methods with ‘shallow’ architecture (usually up to two hidden layers) have been trained for these purposes in the past. However, deep learning methods with a larger number of hidden layers allow the use of finer details in the spectrum which results in improved accuracy and better generalization. Studying finer spectral signatures also enables us to determine accurate differential stellar parameters and find rare objects. We examine various machine and deep learning algorithms like artificial neural networks, Random Forest, and CNN to classify stellar spectra using the Jacoby Atlas, ELODIE, and MILES spectral libraries as training samples. We test the performance of the trained networks on the Indo-U.S. Library of Coudé Feed Stellar Spectra (CFLIB). We show that using CNNs, we are able to lower the error up to 1.23 spectral subclasses as compared to that of two subclasses achieved in the past studies with ML approach. We further apply the trained model to classify stellar spectra retrieved from the SDSS data base with SNR &amp;gt; 20.

Machine learning simulation of one-dimensional deterministic water wave propagation