• A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes 

  Omslandseter, Rebekka Olsson; Jiao, Lei; Oommen, John (Lecture Notes in Computer Science;12799, Peer reviewed; Journal article, 2021)

  The Object Migration Automata (OMA) has been used as a powerful tool to resolve real-life partitioning problems in random Environments. The virgin OMA has also been enhanced by incorporating the latest strategies in Learning ...

• On the Theory and Applications of Hierarchical Learning Automata and Object Migration Automata 

  Omslandseter, Rebekka Olsson (Doctoral dissertations at University of Agder;no. 444, Doctoral thesis, 2023)

  The paradigm of Artificial Intelligence (AI) and the sub-group of Machine Learning (ML) have attracted exponential interest in our society in recent years. The domain of ML contains numerous methods, and it is desirable ...

• Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes 

  Omslandseter, Rebekka Olsson; Jiao, Lei; Oommen, John (IFIP Advances in Information and Communication Technology;627, Peer reviewed; Journal article, 2021)

  Solving partitioning problems in random environments is a classic and challenging task, and has numerous applications. The existing Object Migration Automaton (OMA) and its proposed enhancements, which include the Pursuit ...

• Convolutional Tsetlin Machine-based Training and Inference Accelerator for 2-D Pattern Classification 

  Tunheim, Svein Anders; Lei, Jiao; Shafik, Rishad Ahmed; Yakovlev, Alexandre; Granmo, Ole-Christoffer (Peer reviewed; Journal article, 2023)

  The Tsetlin Machine (TM) is a machine learning algorithm based on an ensemble of Tsetlin Automata (TAs) that learns propositional logic expressions from Boolean input features. In this paper, the design and implementation ...

• Optimization of Multivariate Hawkes Processes via Decentralized Learning Automata: Towards Learning How to Boost Factual Information on Social Networks 

  Abouzeid, Ahmed Abdulrahem Othman (Doctoral dissertations at University of Agder;no. 439, Doctoral thesis, 2023)

  The increasing amount of misleading information on Social Media (SM) platforms is problematic. The reason is that these platforms have become one of the primary sources of information due to their ease-of-use and cheap ...

• Design of Multiplexers for IoT-Based Applications Using Stub-Loaded Coupled-Line Resonators 

  Idrees, Muhammad; Khalid, Sohail; Rehman, Muhammad Abdul; Sajid Ullah, Syed; Hussain, Saddam; Iqbal, Jawaid (Peer reviewed; Journal article, 2023)

  This paper presents the design of microstrip-based multiplexers using stub-loaded coupled-line resonators. The proposed multiplexers consist of a diplexer and a triplexer, meticulously engineered to operate at specific ...

• Surface Electromyography and Artificial Intelligence for Human Activity Recognition - A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation 

  Rani, Gundala Jhansi; Hashmi, Mohammad Farukh; Gupta, Aditya (Peer reviewed; Journal article, 2023)

  Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological ...

• The role of digital technologies in global climate negotiations 

  Flak, Leif Skiftenes; Sæbø, Øystein (Peer reviewed; Journal article, 2023)

• Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series 

  Yakkati, Rakesh Reddy; Yeduri, Sreenivasa Reddy; Tripathy, Rajesh Kumar; Cenkeramaddi, Linga Reddy (Peer reviewed; Journal article, 2023)

  In the industry, machinery failure causes catastrophic accidents and destructive damage to the machines. It causes the machinery to stop and reduces production, causing financial losses to the industry. As a result, ...

• FPGA-Based PUF Designs: A Comprehensive Review and Comparative Analysis 

  Kusum, Lata; Cenkeramaddi, Linga Reddy (Peer reviewed; Journal article, 2023)

  Field-programmable gate arrays (FPGAs) have firmly established themselves as dynamic platforms for the implementation of physical unclonable functions (PUFs). Their intrinsic reconfigurability and profound implications for ...

• Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review 

  A., Soumya; C., Krishna Mohan; Cenkeramaddi, Linga Reddy (Peer reviewed; Journal article, 2023)

  Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent ...

• IoT-Based Multi-Dimensional Chaos Mapping System for Secure and Fast Transmission of Visual Data in Smart Cities 

  Ahuja, Bharti; Doriya, Rajesh; Salunke, Sharad; Hashmi, Mohammad Farukh; Gupta, Aditya (Peer reviewed; Journal article, 2023)

  A “smart city” sends data from many sensors to a cloud server for local authorities and the public to connect. Smart city residents communicate mostly through images and videos. Many image security algorithms have been ...

• DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features 

  Sinha, Nidhi; M. A., Ganesh Kumar; Joshi, Amit Mahesh; Cenkeramaddi, Linga Reddy (Peer reviewed; Journal article, 2023)

  One of the leading causes of mortality worldwide is cardiovascular disease (CVD). Electrocardiography (ECG) is a noninvasive and cost-effective tool to diagnose the heart’s health. This study presents a multi-class classifier ...

• A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection 

  Nazir, Anjum; Memon, Zulfiqar; Sadiq, Touseef; Rahman, Hameedur; Khan, Inam Ullah (Peer reviewed; Journal article, 2023)

  The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious ...

• CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection 

  Sharif, Md Haidar; Lei, Jiao; Omlin, Christian Walter Peter (Peer reviewed; Journal article, 2023)

  Video anomaly event detection (VAED) is one of the key technologies in computer vision for smart surveillance systems. With the advent of deep learning, contemporary advances in VAED have achieved substantial success. ...

• An Ensemble-Based IoT-Enabled Drones Detection Scheme for a Safe Community 

  Singh, Jaskaran; Sharma, Keshav; Wazid, Mohammad; Das, Ashok Kumar; Vasilakos, Athanasios (Peer reviewed; Journal article, 2023)

  With the increasing use of Internet of Things (IoT)-enabled drones for various purposes, including photography, delivery, and surveillance, concerns related to privacy and security have arisen. Drones have the potential ...

• Exploring the mechanical, tribological, and morphological characteristics of areca fiber epoxy composites reinforced with various fillers for multifaceted applications 

  Miniappan, P.K.; Marimuthu, S.; Dharani Kumar, Kumar; Sharma, Shubham; Kumar, Abhinav; Salah, Bashir; Sajid Ullah, Syed (Peer reviewed; Journal article, 2023)

  This investigation is primarily concerned with the effect of fly ash, basalt powder, and tungsten carbide (WC) on the mechanical, microstructural, and tribological behaviour of areca fiber-reinforced composites. The fillers ...

• Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs 

  Money, Rohan Thekkemarickal; Krishnan, Joshin P.; Beferull-Lozano, Baltasar (Peer reviewed; Journal article, 2023)

  Online topology estimation of graph-connected time series is challenging in practice, particularly because the dependencies between the time series in many real-world scenarios are nonlinear. To address this challenge, we ...

• Early and late adopter effects between schools in a one-to-one computer initiative 

  Tømte, Cathrine Edelhard; Pedersen, Cathrine; Vennerød-Diesen, Frida Felicia; Daus, Stephan (Peer reviewed; Journal article, 2023)

  Although there are numerous studies on the development of teachers’ professional digital competence, only a few have studied the intermediate levels of the education system and its impact over time. A structural merger of ...

• Online Machine Learning for Inference from Multivariate Time-series 

  Money, Rohan Thekkemarickal (Doctoral Dissertations at the University of Agder; no: 429, Doctoral thesis, 2023)

  Inference and data analysis over networks have become significant areas of research due to the increasing prevalence of interconnected systems and the growing volume of data they produce. Many of these systems generate ...

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