Bilal Khan¹, Muhammad Arshad², Sarwar Shah Khan^3,4,*

Journal of Cyber Security, Vol.5, pp. 1-11, 2023, DOI:10.32604/jcs.2023.042501

Abstract The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks. Portable Document Format (PDF) files have emerged as a major attack vector for malware due to their adaptability and wide usage. Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts, exploits, and malicious URLs. This paper presents a comparative analysis of machine learning (ML) techniques, including Naive Bayes (NB), K-Nearest Neighbor (KNN), Average One Dependency Estimator (A1DE), Random Forest (RF), and Support Vector Machine (SVM) for PDF malware detection. The study… More >

P. Sumathi^1,*, V. V. Karthikeyan², M. S. Kavitha³, S. Karthik³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1545-1559, 2023, DOI:10.32604/csse.2023.027580

Abstract Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around. Hence, the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield. In present decade, the application of deep learning models in many fields of research has created greater impact. The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model, leads to the incorporation of deep learning method to predict the soil quality. With that concern, this… More >