IAES International Journal of Artificial Intelligence (IJ-AI)
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Two-steps feature selection for detection variant distributed denial of services attack in cloud environment
Get PDFThe prevalence of cloud computing among organizations poses a significant problem in ensuring security. Specifically, distributed denial of services (DDoS) attacks targeting cloud computing networks can lead to financial losses for consumers of cloud computing services. This assault has the potential to render cloud services inaccessible. The detection system serves as a remedy to prevent more substantial losses. This research aims to enhance the efficacy of the system detection model by integrating feature selection with three machine learning algorithms: decision tree (DT), random forest (RF), and naïve Bayes (NB). Therefore, our study suggests combining two phases of feature selection into the DDoS attack detection procedure. The first phase uses the information gain (IG) feature selection technique approach, and the second phase uses the principal component analysis (PCA) feature extraction approach. The technique is referred to as two-step feature selection. The test findings indicate that the implementation of two-step feature selection can enhance the performance of the DT and RF detection models by around 9%
A comprehensive review of interpretable machine learning techniques for phishing attack detection
Get PDFPhishing attacks remain a significant and evolving threat in the digital landscape, demanding continual advancements in detection methodologies. This paper emphasizes the importance of interpretable machine learning models to enhance transparency and trustworthiness in phishing detection systems. It begins with an overview of phishing attacks, their increasing sophistication, and the challenges faced by conventional detection techniques. A range of interpretable machine learning approaches, including rule-based models, decision trees, and additive models like Shapley additive explanations (SHAP), are surveyed. Their applicability in phishing detection is analyzed based on computational efficiency, prediction accuracy, and interpretability. The study also explores ways to integrate these methods into existing detection systems to enhance functionality and user experience. By providing insights into the decision-making processes of detection models, interpretable machine learning facilitates human supervision and intervention, strengthening overall system reliability. The paper concludes by outlining future research directions, such as improving the scalability, accuracy, and adaptability of interpretable models to detect emerging phishing techniques. Integrating these models with real-time threat intelligence and deep learning approaches could boost accuracy while preserving transparency. Additionally, user-centric explanations and human-in-the-loop systems may further enhance trust, usability, and resilience in phishing detection frameworks
Generative Indonesian chatbot for university major selection using transformers embedding
Get PDFSelecting a university major is a crucial decision that impacts students' future career paths and personal fulfillment. Traditional guidance methods often lack the personalization and timeliness needed to support students effectively. This study explores the use of Indonesian generative artificial intelligence (AI) chatbots and transformer embeddings to enhance decision-making for university major selection. By leveraging advanced AI techniques, such as bidirectional encoder representations from transformers (BERT) and Gemini embeddings, the research aims to provide personalized, interactive, and contextually relevant guidance. Experiments showed that BERT embeddings achieved the highest accuracy, with recurrent neural network (RNN) and long short-term memory (LSTM) models also performing well but facing issues with overfitting. Gemini embeddings provided strong performance but slightly less effective than BERT. The findings suggest that BERT-based models with RNN are superior for developing decision-support systems in 92% accuracy. Future work should focus on further optimization and integration of user feedback to ensure the relevance and effectiveness of these AI tools in educational settings
Enhancing traditional machine learning methods using concatenation two transfer learning for classification desert regions
Get PDFDeserts cover a significant portion of the earth and present environmental and economic difficulties owing to their harsh conditions. Satellite remote sensing images (SRSI) have evolved into an important tool for monitoring and studying these regions as technology has advanced. Machine learning (ML) is critical in evaluating these images and extracting valuable information from them, resulting in a better knowledge of hard settings and increasing efforts toward sustainable development in desert regions. As a result, in this study, four ML approaches were enhanced by hybridizing them with pre-training methods to achieve multi model learning. Two pre-training approaches (Xception and DeneseNet201) were used to extract features, which were concatenated and fed into ML algorithms light gradient boosting model (LGBM), decision tree (DT), k-nearest neighbors (KNN), and naïve Bayes (NB). In addition, an ensemble voting was used to improve the outcomes of ML algorithms (DT, NB, and KNN) and overcome their flaws. The models were tested on two datasets and hybrid LGBM outperformed other traditional ML methods by 99% in accuracy, precision, recall, and F1 score, and by 100% in area under the curve (AUC)-receiver operating characteristic (ROC)
Driving agricultural evolution: implementing agriculture 4.0 with Raspberry Pi and internet of things in Morocco
Get PDFThe purpose of this project was to investigate the use of embedded system and smartphone technologies in conjunction with Raspberry Pi and NodeMCU to create an intelligent system for smart farming (SF). By means of experiments and comparative analysis carried out in several agricultural contexts, the research evaluated the efficacy of the intelligent system. Results showed that the system was able to handle pertinent agricultural activities and effectively monitor important environmental factors including temperature, humidity, soil moisture, and climatic quality. The system's remote accessibility helped farmers by allowing them to effectively oversee agricultural operations at any time and from any location. As a consequence, SF techniques produced more production, lower costs, and maintained assets
Applications of artificial intelligence in indoor fire prevention and fighting
Get PDFIn this study, we design and analysis of artificial intelligence (AI) in indoor fire prevention and fighting. The application of image recognition processing technology has progressed from the early stages using color recognition and feature extraction methods, a newer approach is optical flow using image sequence data to identify motion regions. Image recognition processing technology, a subset of computer vision and AI, has numerous applications across different industries. It allows machines to interpret and make decisions based on visual data, such as photos, videos, or live camera feeds. Recently, AI has many applications in the field of indoor fire prevention and firefighting, leveraging real-time data analysis, predictive modeling, and automation to enhance safety and efficiency. With the application of a neural network, the simulated flame features in the laboratory are used as the input; The image containing the flame from the animation and the features of the image are fed into the artificial neural network obtained from the image from the charge-coupled device camera
Challenges of recommender systems in finance and banking: a systematic review
Get PDFRecommender systems are widely applied in various domains, including e-commerce, marketing, and education. Despite their popularity, recommender systems are not widely used in finance and banking. This paper aims to identify the challenges associated with using recommender systems in finance and banking and recommend directions for future research. Using a systematic literature review (SLR) method, 52 papers were selected and analyzed. A three-step process was used to make the selection. First, a keyword search was made to identify a seed list of sources. A snowball technique with specific inclusion and exclusion criteria was applied to expand the list. Finally, a quick study was made to produce the final list of sources to consider. Through the study of the 52 relevant papers, three main challenges: i) transparency, ethics, and data privacy; ii) handling complex content information and accounting for multiple user behaviors; and iii) explainability of AI models were identified. This study has established the barriers to adopting recommender systems in the finance and banking industry. Specific subjects of concern identified include cold-start problems, personalization, fraud detection, transparency, and data privacy. The study recommends further research leveraging advanced machine learning models and emerging technologies to fill the gap
Transformation of Islamic values in the era of artificial intelligence
Get PDFThe emergence of artificial intelligence (AI) such as ChatGPT has brought significant changes in the way humans’ access and understand information, including in the religious field. This research aims to examine how the transformation of Islamic values occurs through ChatGPT responses in the aspects of educational ethics, Islamic law, da'wah, and Qur'anic interpretation. This study applied a qualitative case study method and data was collected from indexed scientific articles from academic databases, ChatGPT responses, and online news articles. The study findings show that the use of ChatGPT in the context of Islam requires caution. While technology can answer a variety of questions, there are fundamental flaws related to the accuracy of citations, unverified sources of information, and a lack of understanding of the sharia context. In fact, there are errors in the mention of Qur'anic verses that have the potential to cause confusion. This emphasizes the importance of the sanad principle in Islamic scholarship as a valid reference. The paper proposes the need to develop more ethical and contextual AI systems in understanding religious questions, as well as the involvement of scholars and academics in training machines to conform to Islamic values
Music genre classification using Inception-ResNet architecture
Get PDFMusic genres help categorize music but lack strict boundaries, emerging from interactions among public, marketing, history, and culture. With Spotify hosting over 80 million tracks, organizing digital music is challenging due to the sheer volume and diversity. Automating music genre classification aids in managing this vast array and attracting customers. Recently, convolutional neural networks (CNNs) have been used for their ability to extract hierarchical features from images, applicable to music through spectrograms. This study introduces the Inception-ResNet architecture for music genre classification, significantly improving performance with 94.10% accuracy, precision of 94.19%, recall of 94.10%, F1-score of 94.08%, and 149,418 parameters on the GTZAN dataset, showcasing its potential in efficiently managing and categorizing large music databases
Enhancing precision agriculture: a comprehensive investigation into pathogen detection and management
Get PDFAgriculture is an important sector of Indian agronomy for human livelihood. All areas are affected by the effects of environmental toxic farms, which makes managing various difficult situations more challenging. Agriculture must adopt new technology in accordance with daily environmental changes if it is going to benefit from a crop from the perspectives of farmers and end users. Farmers will benefit from early detection of agricultural diseases rather than risking their lives in dangerous circumstances. Computer technology will be very helpful in maintaining sustainable and healthy crops for the objective of identifying crop diseases in addition to the farmer's close observation. Deep learning (DL) techniques are very influential among various computing technologies. In this work, we explore several current approaches to precision agriculture, such as artificial intelligence (AI), DL, and machine learning (ML). The findings of the study make clear modern methods, their drawbacks, and the knowledge lacking that needs to be addressed to explore precision agriculture fully