Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.

The aging effects on Static Random-Access Memory Physical Unclonable Functions (SRAM PUFs) pose significant security and reliability challenges for current hardware systems. SRAM PUFs utilize process variations in integrated circuits for secure key generation and device authentication. However, aging effects such as bias temperature instability (BTI) and hot carrier injection (HCI) may change SRAM cell characteristics and affect PUF responses. This study addresses various aging-induced variation challenges, identifies security vulnerabilities, and provides innovative approaches to risk reduction. Furthermore, this study focuses on concerns about SRAM PUF reliability. In this regard, mitigation approaches like adaptive reconfiguration, error correction codes, and multi-modal PUFs are introduced to enhance the resilience of SRAM PUFs. This study concludes by outlining future research directions and prospects for improving SRAM PUF-based security solutions by several orders of magnitude. This is carried out with various complexities related to semiconductor device aging.

Machine learning models play a critical role in applications such as image recognition, natural language processing, and medical diagnosis, where accuracy and efficiency are paramount. As datasets grow in complexity, so too do the computational demands of classification techniques. Previous research has achieved high accuracy but required significant computational time. This paper proposes a parallel architecture for Ensemble Machine Learning Models, harnessing multicore CPUs to expedite performance. The primary objective is to enhance machine learning efficiency without compromising accuracy through parallel computing. This study focuses on benchmark ensemble models including Random Forest, XGBoost, ADABoost, and K Nearest Neighbors. These models are applied to tasks such as wine quality classification and fraud detection in credit card transactions. The results demonstrate that, compared to single-core processing, machine learning tasks run 1.7 times and 3.8 times faster for small and large datasets on quad-core CPUs, respectively.

Hardware Trojans are deliberate malicious hardware modifications inserted in semiconductor Integrated Circuits (ICs) for the purpose of stealing or leaking sensitive information, as well as disrupting critical systems upon activation. Emerging hardware security research highlights the criticality of employing AI for effective detection within the semiconductor IC supply chain. The efficient detection of these malicious Trojan circuits is of utmost significance, as it holds paramount importance in cultivating trust within the semiconductor IC supply chain. However, prevailing detection methodologies, predominantly reliant on Side-Channel Analysis (SCA), often necessitate the utilization of golden chips for validation. This article heralds a new era in hardware Trojan detection, harnessing the prowess of unsupervised machine learning in conjunction with SCA to eliminate the need for golden data. Employing unsupervised clustering, the methodology not only showcased a superior false-positive rate but also demonstrated a comparable accuracy level when compared to supervised counterparts. Notably, the proposed model exhibited an impressive accuracy rate of 93%, particularly excelling in pinpointing diminutive Trojans triggered by concise events, surpassing the capabilities of preceding techniques. In conclusion, this research advances a paradigm in hardware Trojan detection, emphasizing its potential in enhancing the integrity of semiconductor IC supply chains.