Authors - Gavin Singh Pandha, Umar Khokhar, Binh Tran Abstract - This research initiative investigates the growing role of artificial intelligence (AI) in the automation of cyber-attacks and its emerging impact on modern cybersecurity. This study explores how AI models can be used to per-form web application intrusions by simulating attacks against deliberately vulnerable applications. Performance metrics from these simulations are compared with traditional, manually executed intrusion techniques, in addition to examining real world cases for AI misuse, ethical concern, industry standard, and the growing risk of autonomous threat actors
Authors - M. V. Rama Sundari, Bhuvan Unhelkar, Pravin Kshirsagar, Supriya Nandikolla Abstract - The management of nutrient content in soil is vital for improving productivity of crops, as well as sustainable agriculture. Conventional processes of establishing the best ratios of macronutrients tend to overlook intrinsic relation-ships that exist among soil properties, crop, weather. Main purpose is to come up with a smart prediction system that will help forecast Macronutrient needs by infusing particular domain-specific expertise in agriculture into machine learning models to improve interpretability and predictive accuracy. The suggested methodology utilises a Graph Convolutional Network (GCN) to simulate spatial and relational relationships amongst soil, crop and environmental parameters. To provide the model with a better semantic understanding, a Knowledge Graph (KG) is created to encode the relationships between domains. Embedding algorithms, such as TransE and DistMult, are then added to the GCN to form a KG-embedded GCN model that can learn feature-based as well as semantic relationships to predict nutrients. The experimental analyses on the ICFA Crop Recommendation dataset reveal that the baseline GCN got the R² scores of 0.806, 0.729, and 0.768, and the TransE GCN and DistMult GCN models have been advanced to the R² scores of 0.808-0.821, 0.746-0.762, and 0.800-0.814 for Nitrogen, Phosphorus and Potassium respectively. These findings indicate that the predictive strength is greatly advanced by the incorporation of domain knowledge. The model can, however, perform differently on unknown crop varieties and soils, which suggests that more work needs to be done in the future on larger and region-specific datasets.
Authors - Antonio Cortes Castillo Abstract - The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed data center operations. The widespread adoption of AI services has introduced new requirements for hosting AI systems, prompting significant modifications in the design and construction of modern data centers. As a result, data center operators must implement comprehensive strategies to address the challenges associated with AI integration. This study explores the application of machine learning techniques using neural networks and Multilayer Perceptron (MLP) models for data center optimization. The research focuses on critical metrics, including power consumption, liquid cooling, fiber-optic systems, and the number of fiber-optic routing paths, which pose significant challenges for data center operators. Experimental results are analyzed using simulation tools, including SPSS, to demonstrate enhancements in data center performance and efficiency.
Authors - Hera Khairunnisa, Naomi Helena Elizabeth, Dwi Kismayanti Respati, Ayatulloh Michael Musyaffi, Gentiga Muhammad Zairin Abstract - This study presents a bibliometric analysis of the literature on accounting and non-profit organizations (NPOs) based on 248 documents retrieved from the Scopus database. Data were processed and visualized using Biblioshiny (R Studio) and Scopus web analysis. The findings indicate a significant growth in publications since 2000, peaking in 2020–2024. Accounting, Auditing and Accountability Journal emerged as the most dominant source, and the distribution of journals is consistent with Bradford's Law. The United States and United Kingdom lead in scientific contributions, while Indonesia shows a growing presence. Keyword analysis reveals a shift toward contemporary themes such as blockchain, sustainability, and social accounting. This study provides a systematic mapping of the intellectual landscape of NPO accounting research and identifies opportunities for future investigation.
Authors - Himangshu Sarma, Madhumita Banerjee, Chandrajit Choudhury Abstract - Diabetic Retinopathy starts at a light level with no visible symptoms, but it can lead to severe pain and blindness as the disease progresses. Clinically, DR is diagnosed by looking for retinal detachment or utilizing imaging techniques like fundus imaging or optical tomography. The Early Diabetic Retinopathy Study is one of the established DR staging schemes. Image Processing has played a significant role in improving the methods used to detect the disease automatically. It has a huge role in assisting ophthalmologists in the screening process, as manually screening by ophthalmologists consumes more time, sometimes there may also be error. Although there are a number of algorithms used in detection, therefore, in this work we have studied and implemented various DR detection methods. To differentiate among various classes, we also prepared KAGGLE APTOS dataset containing the GLCM, LTP, GLRLM, LMeP, CLBP, CSLBP and LBP features extracted from the image dataset. And the classification is performed in the dataset, whilst achieving an accuracy of 97% on testing dataset and 96% for training dataset using SVM multi classifier.
Authors - Najera R. Umpar, Minsoware S. Bacolod Abstract - In this study, the readiness of teachers in adopting Artificial Intelligence (AI) in teaching and learning, and the variables that influence their acceptance or resistance towards it, were investigated. Following a qualitative research approach, semi-structured interviews were employed to gather the data. It was found that a range of factors including generation of teachers, teaching discipline or specialization, institutional support, and ethical issues influence teachers' readiness in implementing AI in the teaching process. Young teachers, along with those who are specialized in STEM subject area, expressed confidence, preparedness, and enthusiasm in embracing AI in teaching. Experienced teachers, and teachers who taught other subject area, expressed concerns toward relevance and teachers' autonomy. Institutional support (were considered as a factor that would significantly impact teachers' readiness toward AI integration. Ethical concern such as student privacy, bias algorithm, and student monitoring have also contributed to teachers' beliefs on AI implementation. More important, the study identified "hybrid readiness" where teachers believe AI can serve as the "co-teacher" of them and contribute to individual learning and pedagogical practice. It suggests that there are variety of factors influencing the teachers' readiness toward AI implementation thus it requires more comprehensive planning in order to have more efficient integration of AI in the classroom. It is found that teachers' readiness is still not homogeneous and context dependent, therefore differentiated training and education as well as support policy are necessary to enhance teacher readiness and promote the integration of AI in education.
