Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems

This paper explores the potential of Field-Programmable Gate Arrays (FPGAs) for accelerating both neural network inference and training. We present a comprehensive analysis of FPGA-based systems, encompassing architecture design, hardware implementation strategies, and performance evaluation. Our study highlights the advantages of FPGAs over traditional CPUs and GPUs for neural network workloads, including their inherent parallelism, reconfigurability, and ability to tailor hardware to specific network needs. We delve into various hardware implementation strategies, from direct mapping to dataflow architectures and specialized hardware blocks, examining their impact on performance. Furthermore, we benchmark FPGA-based systems against traditional platforms, evaluating inference speed, energy efficiency, and memory bandwidth. Finally, we explore emerging trends in FPGA-based neural network acceleration, such as specialized architectures, efficient memory management techniques, and hybrid CPU-FPGA systems. Our analysis underscores the significant potential of FPGAs for accelerating deep learning applications, particularly those requiring high performance, low latency, and energy efficiency.

Reconfigurable Acceleration of Neural Networks: A Comprehensive Study of FPGA-based Systems

Machireddy,

Chella

2024

Description of Regular m-Bipolar Fuzzy Graphs

RamaKishore K.,

Ramprasad C.H.,

Varma P.L.N

2024

The first and second basic sequences in an m-BPFG are defined in this work along with an example of an adjacency sequence of a node. A few instances are created to demonstrate that, even if an m-BPFG is regular, the crisp underlying graph may or may not be regular or have a uniform adjacency sequence for all of the nodes. Additionally, it is demonstrated that not all of the nodes need to have the same adjacency sequence if an m-PPFG and its crisp underlying graph are regular. The notion of adjacency sequences is used to create a necessary and sufficient requirement for an m-BPFG to be regular if it has no more than four nodes. Along with, there are several definitions of what it means for a regular m-BPFG's complement, line graph, and other properties to be regular.

Determination of Colorectal Cancer and Lung Cancer Related LncRNAs based on Deep Autoencoder and Deep Neural Network

TOPRAK

2024

Until recently, non-coding RNAs were considered junk RNA and were always ignored, but studies have revealed that many non-coding RNAs such as miRNA, lncRNA, and circRNAs play important roles in biological processes. A subclass of non-coding RNAs with transcripts longer than 200 nucleotides, called lncRNAs, play important roles in many cellular processes such as gene regulation. For this reason, since wet experimental studies to identify disease-related lncRNA are time-consuming, computational methods are used. Many researchers have applied similarity-based and machine learning-based computational methods and achieved very successful results. Due to its high success rate, the deep learning technique is applied to many fields today. In this study, we used the Deep Autoencoder and Deep Neural Network method to predict disease related lncRNAs. As input data of Deep Autoencoder, the concatenated feature vector obtained from integrated disease similarity and integrated lncRNA similarity was used. To train the deep neural network for predicting relationships between lncRNAs and diseases, the features extracted from the autoencoder’s output were utilized. The prediction performance of our method was evaluated with the commonly used 5-fold cross validation and an AUC value of 0.9575 was obtained. It can be seen that the method we proposed is more successful than other compared methods. Additionally, case studies on colorectal cancer and lung cancer were conducted and confirmed with the literature. As a result, the Deep Autoencoder and Deep Neural Network method can be used reliably to identify candidate disease-related lncRNAs.