Machine learning
New research has shed light on the effective use of Machine Learning (ML) and Deep Learning (DL) in firewall anomaly detection, a critical area of cybersecurity. In a study conducted by researchers at the National Engineering School in Sfax, Tunisia, the team developed advanced techniques leveraging ML and DL to
Machine learning
Researchers from the Affiliated Hospital of Qingdao University have made a significant discovery in the field of colon cancer, identifying efferocytosis-related genes (ERRGs) as key regulators of the disease. The study, published in the Journal of Gastroenterology and Hepatology, reveals that ERRGs are dysregulated in colon adenocarcinoma (COAD) and provide
Machine learning
The global artificial intelligence (AI) in drug discovery market is poised for significant growth, driven by the increasing adoption of AI technologies among pharmaceutical companies. According to a report by Research and Markets, the global AI in drug discovery market was valued at $1.72 billion in 2024 and is
Machine learning
Research conducted at the Hospital for Sick Children in Toronto, Canada, has shed light on the impact of artificial intelligence (AI) scribes in healthcare. The study, published in the journal Applied Clinical Informatics in 2025, aimed to evaluate the effectiveness of AI scribes in reducing documentation time and improving clinician
Machine learning
Researchers from Harvard University have made a groundbreaking discovery in the field of robotics, inspired by the landing behaviors of flying insects. The study, funded by the National Science Foundation, aimed to create a mechanical and control approach for achieving safe and accurate landings in flapping-wing microaerial vehicles. By analyzing
Machine learning
A team of researchers from Chitkara University has developed a novel hybrid method for detecting image splicing forgery (ISF) that integrates an improved convolutional neural network (CNN), support vector machine (SVM) classifier, and contrast-limited adaptive histogram equalization (CLAHE). The proposed methodology employs CLAHE to enhance the extraction of hidden features
Machine learning
Research on designing wrist-inspired joints has made significant progress in enabling flexibility and maneuverability in robotic manipulators and grippers for restricted operating spaces. A team of researchers from Nankai University in Tianjin, China, has developed a wrist-inspired joint with an integrated drive system, allowing for simultaneous pitch and yaw movements.
Machine learning
Researchers from the Shandong Academy of Agricultural Sciences have developed a novel detection and classification method for shiitake mushrooms using the mamba-YOLO algorithm. This method enables the automatic detection and quality grading of shiitake mushrooms, adhering to the picking standards and grade specifications. Experiments conducted on a self-constructed shiitake mushroom
Machine learning
A new patent has been published, detailing a novel bridge design for robotic cleaning of photovoltaic (PV) panels. The invention, by Yanir Allouche, addresses the challenge of cleaning PV panels effectively, particularly when tables are misaligned or tilted. The bridge design enables robotic cleaning solutions to navigate between tables, ensuring
Machine learning
Research conducted by the Indian Institute of Technology (IIT) Indian School of Mines has led to the development of a Surface Plasmon Resonance (SPR) fiber optic refractive index sensor that utilizes machine learning techniques for enhanced detection capabilities. The sensor is capable of detecting the dengue virus with high sensitivity,
Machine learning
Research at the University of Southern California has introduced a novel multi-agent deep reinforcement learning framework that jointly optimizes demand forecasting and inventory management in retail supply chains. The framework leverages data from IoT sensors, RFID tracking systems, and smart shelf monitoring devices to improve inventory decisions. The approach combines
Machine learning
Investigations at Addis Ababa University have led to the development of a novel Multistage Adversarial Attack Defense (MSAAD) framework to protect online DDoS attack detection systems from adversarial attacks. This breakthrough framework consists of three defense layers, designed to detect and prevent DDoS attacks in IoT systems. The MSAAD framework
