Continual federated learning for network anomaly detection in 5G Open-RAN

Abstract. This dissertation offers a unique federated continual learning setup for anomaly detection in the fast growing 5G Open Radio Access Network (O-RAN) environment. Conventional AI techniques frequently fall short of meeting the security automation needs of 5G networks, owing to their outstanding latency, dependability, and bandwidth demands. As a result, the thesis provides an anomaly detection system that does not only use federated learning (FL) to solve inherent privacy problems and resource constraints but also incorporates replay buffer concept in the training phase of the model to eradicate catastrophic forgetting. To allow the intended federated learning architecture, anomaly detectors are incorporated into the Near-real time RIC, while aggregation servers are installed within the Non-real time RIC. The configuration was carefully tested using the 5G NIDD Dataset, revealing a considerable boost in detection accuracy by reaching close to 99% for almost all datasets after including the continual learning process. The thesis also investigates the notion of transfer learning, in which pre-trained local models are evaluated against a hybrid Application layer DDoS dataset that includes benign samples from the CICIDS 2017 dataset and attack flows generated in proprietary SDN environment. The captured results show almost over 99% of accuracy, confirming the suggested system’s efficacy and flexibility. The study represents a significant step forward in the development of a more secure, efficient, and privacy-protecting 5G network architecture

Nutrient Removal From Stormwater By Using Green Sorption Media

High nitrogen and phosphorus content in storm water runoff has affected groundwater, springs and surface water by impacting ecosystem integrity and human health. Nitrate may be toxic and can cause human health problem such as methemoglobinemia, liver damage and even cancers. Phosphorus may trigger the eutrophication issues in fresh water bodies, which could result in toxic algae and eventually endanger the source of drinking waters. Sorption media with mixes of some recycled materials, such as sawdust and tire crumb, combined with sand/silt and limestone, becomes appealing for nutrient removal in environmental management. This paper presented is a specific type of functionalized filtration media, Langmuir and Freundlich isotherms with reaction kinetics for nutrient removal using a suite of batch tests represented. Pollutants of concern include ammonia, nitrite, nitrate, orthophosphate and total dissolved phosphorus. Application potential in storm water management facilities, such as dry ponds, is emphasized in terms of life expectancy and reaction kinetics. As compared to the natural soil that is selected as the control case in the column test, our green sorption media mixture is proved relatively effective in terms of removing most of the target pollutants under various influent waste loads

Dispersion Study of a Broadband Terahertz Focusing Reflecting Metasurface for 6G Wireless Communication

In 6G wireless communications, functional terahertz reflecting metasurfaces are expected to play increasingly important roles such as beamforming and beamsteering. This paper demonstrates the design of a functional and efficient beamforming metasurface in the burgeoning D-band (0.11-0.17~THz). In addition to achieving broadband operation (0.135-0.165~THz), this design is polarization-maintaining, diffraction limited, simple in design, exhibits 64.1\% broadband efficiency (1.9 dB insertion loss) and 20\% fractional bandwidth. Despite being formed by an array of highly dispersive resonators, the metasurface exhibits very low temporal dispersion, which avoids pulse reshaping and its consequent limitations on achievable data rate. The design and performance of the focusing reflector are presented followed by a group delay and group delay dispersion analysis revealing that a 2.83\% temporal broadening of the pulse is observed at the focus

Assessing the Relationship between Service Quality and Customer’s Propensity to Switch Brands in the Banking Industry of Bangladesh

The significance of banks and other financial institutions is highlighted by the contribution they make to the economic development of a country. With a number of state-owned, private-owned and foreign banks in Bangladesh, it reflects the wide range of options available to the customers, indicating the presence of severe competition. Such relentless competition in the banking industry of Bangladesh has led banks to seek ways to differentiate their services in the market, ultimately aiming to satisfy customers and preventing them to switch to a competing brand. Despite the efforts, banks are constantly hounded by the challenge of customers moving to another organization in search for better products and services. As expressed by the literary work of various academicians and researchers, service quality plays a vital role in determining the possibility of customers to switch. This research therefore, aims to investigate whether the components of service quality identified by the Servqual model discourage customers’ willingness to switch to another brand. Based on the analysis of diverse literature, hypotheses were developed and in order to test those, primary data collected from 250 respondents were analyzed through SPSS. Eventually, the findings reveal that the service quality dimensions positively influence customer satisfaction, which in turn are negatively associated with customers’ brand switching intention. Furthermore, the dimensions directly, without using customer satisfaction as a mediating factor, also have negative relationship with brand switching intentions. However, in the analysis, amongst all the dimensions of service quality only one element (i.e. responsiveness) was found less significant. Keywords: service quality, customer satisfaction, and brand switching intentio

Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature

Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The disease can lead a person to death or a high level of body damage. To avoid significant damage, patients need sugar. The research aims at implementing an automatic system to detect hypoglycemia and perform automatic sugar injections to save a life. Receiving the benefits of the internet of things (IoT), the sensor data was transferred using the hypertext transfer protocol (HTTP) protocol. To ensure the safety of health-related data, blockchain technology was utilized. The glucose sensor and smartwatch data were processed via Fog and sent to the cloud. A Random Forest algorithm was proposed and utilized to decide hypoglycemic events. When the hypoglycemic event was detected, the system sent a notification to the mobile application and auto-injection device to push the condensed sugar into the victims body. XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and decision tree were implemented to compare the proposed models performance. The random forest performed 0.942 testing accuracy, better than other models in detecting hypoglycemic events. The systems performance was measured in several conditions, and satisfactory results were achieved. The system can benefit hypoglycemia patients to survive this disease

Phase Transformation in Micro-Alloyed Steels

Phase transformation in crystalline solid is an important factor that designs the microstructure and plays a great role in alloy development. Iron has an allotropic form, and this unique metallurgical property leads to phase transformation. Addition of micro-alloying elements enhances the phase transformation scenarios in steels. Phase transformation due to the addition of micro-alloying elements, together with exceptional precipitation hardening capabilities, substantially improves mechanical properties of steels of different grades. Ferrite transforming to other phases reduces the hardenability of steels. Micro-addition of elements forms precipitation in ferrite and austenite, which controls the microstructure and hence the mechanical properties of steels. Besides, interactions between different deformation sequences used in the production of steel and addition of elements as solute or precipitates regulate the microstructure. Ferrite grain refinement depends on the refinement of austenite grain size in one case, and austenite grain size growth can be varied by addition of various elements. Thus, a variety of elements influences phase transformation that leads to significantly modified properties

Investigating Customer Churn in Banking: A Machine Learning Approach and Visualization App for Data Science and Management

Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and, after that, end their connection with the bank. Therefore, customer retention is essential in today’s extremely competitive banking market. Additionally, having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele. These factors make reducing client attrition a crucial step that banks must pursue. In our research, we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers. We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics. In addition, we developed a Data Visualization RShiny app for data science and management regarding customer churn analysis. Analyzing this data will help the bank indicate the trend and then try to retain customers on the verge of attrition

A comprehensive study of mental health issues: impact on overseas university students

Purpose: This research aimed to find out the mental problems of overseas students and how they can remove it. Research Methodology: In-depth interview method was done on the basis of Google form. After the interview, it was interpreted by thematic analysis. Results: 80% of overseas students face mental health disorders. Limitations: A larger sample size, including students residing other parts of the country should be considered in future research. The result is also difficult to generalize due to the unequal participation of male and female respondents.  Contribution: This study can be useful in those universities where overseas students study. Keywords: Overseas, Mental healt

Soil moisture monitoring with LoRa radios and UAVs

Oklahoma Established Program to Stimulate Competitive Research (National Science Foundation). Research Experiences for UndergraduatesGeological Survey (U.S.)Electrical Engineerin