Evaluating AI Language Models in News Retrieval: A Comparative Study Of ChatGPT-Plus and DeepSeek (R1)

Omar Al-Janabi · Osamah Mohammed Alyasiri · Elaf Ayyed Jebur · Shahad Mohgoob Nafl

The increasing complexity of how humans interact with and process information has demonstrated significant advancements in Natural Language Processing (NLP), transitioning from task-specific architectures to generalized frameworks applicable across multiple tasks. Despite their success, challenges persist in specialized domains such as translation, where instruction tuning may prioritize fluency over accuracy. Against this backdrop, the present study conducts a comparative evaluation of ChatGPT-Plus and DeepSeek (R1) on a high-fidelity bilingual retrieval-and-translation task. A single standardize prompt directs each model to access the Arabic-language news section of the College of Medicine, University of Baghdad, retrieve the three most recent articles, and translate them into English. ChatGPT-Plus fulfilled the prompt successfully, extracting authentic Arabic content and delivering fluent, semantically accurate English translations. DeepSeek (R1), by contrast, failed to retrieve the requested articles and instead produced only generic procedural advice – evidence of its lack of real-time web access and a retrieval-augmented generation (RAG) mechanism.

Enhancing Malware Detection Through Machine Learning Techniques

Zeina S. Jassim · Mohamad M. Kassir

Malware detection is important to computer network security since it is the principal attack vector against modern enterprises. As a result, firms must remove viruses from computer systems. Using artificial intelligence, namely machine learning techniques, to function in real-time with an IT system is the ideal solution to this problem. This issue has yet to be fixed, but it is still significant because a lack of processing power and memory constrains these features. The most popular method for evaluating systems and intrusion detection models is using the Application Program Interface (API) calls via the KDD-CUP99 data set to give this solution. KDD-CUP99 has more than three hundred thousand samples, each with 54 features. However, the data set attributes were designed and chosen to provide us with a high malware detection rate. The quality of this data was lowered to produce results. To get the desired results, the attributes of this data were reduced. Data transformation and purification are used in this process. Inaccurate, unnecessary, duplicated, or missing information is eliminated by data cleansing. Data cleaning eliminates inaccurate, excessive, redundant, or lacking information. By comparing this study to earlier research that employed lengthy sequences of software interface (API) calls with the same machine-learning classifiers, data transformation includes discretization, which transforms the continuous process of discretizing continuous data into discrete forms is a type of data transformation. Using more advanced algorithms to do the task at hand with the best precision and the least expense increases accuracy and performance. The data set was divided into two categories using a Support Vector Machine (SVM), Decision Tree (DT), and Iterative Dichotomiser 3 (ID3). The findings revealed that little previous research uses a five-class classification strategy for malware detection. The accuracy of several works is comparable to the accuracy acquired in the proposed work.