Journal of Artificial Intelligence and Information Technology
Journal of Artificial Intelligence and Information Technology is a scientific journal focused on the latest research in artificial intelligence (AI) and information technology. It covers various topics, including machine learning, natural language processing, computer vision, expert systems, cybersecurity, blockchain, IoT, robotics, as well as AI ethics and regulations. Through a peer-review process, this journal publishes original research, literature reviews, and case studies that contribute to the advancement of science and technology. It aims to be a key reference for academics, researchers, and practitioners in understanding and applying AI innovations across various sectors. This journal is published 1 year 4 times (February, May, August and November)
International Forum of Researchers and Lecturers
Multi-Horizon Forecasting with Hybrid TCN–GRU and Adaptive Conformal Prediction for Decision Support
Multivariate time-series forecasting is widely deployed in energy and industrial monitoring, where non-stationarity and distribution shift make point forecasts insufficient for risk-aware decisions. This paper studies multi-horizon forecasting on the ETTh1 benchmark (target: oil temperature, OT) and addresses two practical problems: (i) combining local pattern extraction and longer-range dependency modeling in a single lightweight forecaster, and (ii) attaching distribution-free uncertainty that remains useful under temporal drift. We propose a hybrid architecture that uses a causal dilated Temporal Convolutional Network (TCN) as a local feature extractor and a gated recurrent unit (GRU) layer to refine longer dependencies from TCN features. The model directly predicts four horizons (96, 192, 336, and 720 steps) from a fixed lookback window (L=336) using multivariate inputs and time features. To quantify uncertainty, we apply time-aware conformal prediction with a chronological train/calibration/test split, comparing static conformal intervals with an adaptive rolling variant that updates horizon-wise quantiles using recent residuals. On the ETTh1 test set, the hybrid model improves average point accuracy over GRU-only and TCN-only baselines (MAE 4.615 and RMSE 5.382 in OT units), while adaptive conformal increases empirical coverage from 0.790 (static) to 0.870 with a moderate increase in mean interval width (13.80 to 15.27). Finally, a simple decision-support rule based on thresholding the predictive upper bound illustrates how calibrated intervals enable actionable trade-offs between false alarms and missed events. Overall, the proposed pipeline provides an efficient and reproducible recipe for multi-horizon forecasting with uncertainty under drift.
Copyright (c) 2026 Journal of Artificial Intelligence and Information Technology
Cost-Sensitive Fraud Detection with Reliability Calibration: APractical Pipeline with XGBoost and Focal-Proxy Reweighting
Fraud detection on payment transactions is an extremely imbalanced, high-stakes classification task in which deployment decisions depend not only on ranking quality but also on reliable prob ability estimates. We study credit card fraud detection on a standard real-transaction benchmark (284,807 transactions; 492 frauds) and target two deployment requirements: cost-sensitive thresh olding under asymmetric error costs and reliability calibration so model outputs can be interpreted as stable risk scores. We benchmark Logistic Regression and XGBoost, and propose a focal-proxy reweighting scheme for boosted trees via iterative weight updates inspired by focal loss. Probabili ties are calibrated on validation using Platt scaling, temperature scaling, and isotonic-style monotone calibration; the best calibrator is selected by minimum validation Brier score. For decision making, we choose the operating threshold that minimizes expected cost Cost(t) = 10 · FN(t) + 1 · FP(t) on validation, then evaluate on a held-out test set. On the benchmark split (train 199,364; valida tion 42,721; test 42,722), the calibrated XGBoost baseline achieves AUROC 0.973, AUPRC 0.812, fraud-class F1 0.767, and expected cost 154 with very low calibration error (ECE = 1.1 × 10−4). Overall, calibration reduces ECE and improves or maintains Brier score, while cost-aware thresh olding makes the FN/FP trade-off explicit via decision curves. The focal-proxy variant does not improve expected cost in this
Explainable Clinical Risk Prediction from EHR Tabular Data using Monotonic Constraints and Calibrated Probabilities
Tabular clinical risk models are widely used in decision support, yet two practical gaps often limit trust: (i) model responses may contradict basic clinical ordering (e.g., higher age reducing predicted risk), and (ii) raw predicted probabilities can be miscalibrated, making threshold-based actions unreliable. We study explainable binary risk prediction using the processed Cleveland subset of the UCI Heart Disease dataset as a public clinical tabular benchmark. We propose a lightweight, CPU-friendly pipeline that trains an XGBoost classifier with domain-informed monotonic constraints on clinically plausible features and then calibrates probabilities on a held-out validation set using post-hoc calibration (Platt scaling, temperature scaling, and isotonic regression). We evaluate discrimination (AUROC, AUPRC, F1, sensitivity, specificity) and probability reliability (ECE and Brier score), and we audit monotonicity via counterfactual feature sweeps that quantify violation rates. To support downstream use, we perform risk stratification into low/medium/high bands and report event rates per band. Results indicate that isotonic calibration improves reliability (lower ECE/Brier) while preserving strong discrimination, and the monotonicity audit shows no observed violations for constrained features under the sweep protocol. Overall, combining monotonic constraints with calibrated probabilities yields more decision-ready risk estimates for threshold-based clinical decision support, while retaining transparency through SHAP-based analyses.
Trust Driven Artificial Intelligence Architecture for Secure Knowledge Extraction and Information Management in Decentralized Digital Platforms
The rapid advancement of decentralized digital platforms has highlighted the need for secure, reliable, and trustworthy systems to manage knowledge extraction and information dissemination. Traditional security models have often failed to address the complexities of decentralized environments, where data is distributed across multiple nodes and stakeholders. To address these challenges, this paper introduces a trust driven AI architecture designed to enhance security, reliability, and trust in decentralized systems. By integrating trust modeling, intelligent knowledge extraction, and secure information management, the proposed architecture ensures that knowledge flows securely and efficiently across decentralized platforms. The architecture utilizes blockchain technology to provide immutable transaction logs, ensuring transparency and accountability in data exchanges. Additionally, AI driven techniques such as natural language processing and semantic reasoning are incorporated to intelligently extract and structure knowledge, improving the accuracy and accessibility of information in decentralized systems. The architecture also integrates dynamic trust validation through decentralized reputation scoring and blockchain based logging, ensuring that trust is continually updated based on real time interactions and participant behavior. This dynamic approach is essential for maintaining trust among stakeholders in the absence of centralized control. The proposed system was validated through simulations, which demonstrated significant improvements in trust consistency, data reliability, and secure knowledge utilization compared to traditional security centric models. The results indicate that the architecture effectively addresses key challenges such as scalability and interoperability, offering a more robust solution for decentralized platforms in sectors like healthcare, finance, and smart cities. These findings highlight potential for secure decentralized knowledge management.
Privacy Aware Intelligent Data Processing Using Differential Privacy and Deep Neural Networks in Smart City Applications
Smart cities are rapidly reshaping urban environments by integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. These innovations aim to enhance urban management, optimize public service delivery, and improve the overall quality of life for urban residents. However, as cities increasingly rely on vast amounts of personal and sensitive data, concerns regarding privacy protection and data security continue to intensify. Conventional privacy preserving techniques, including encryption and anonymization, often reduce the analytical efficiency required for real time smart city applications, creating challenges in balancing data protection with effective decision making. This study proposes a privacy aware intelligent data processing framework that integrates differential privacy mechanisms with deep neural networks (DNNs) to address these issues. The proposed framework is designed to achieve a balance between strong privacy protection and high analytical performance, enabling smart city systems to securely process and analyze large scale datasets. By embedding differential privacy into the data processing pipeline, controlled noise is added to datasets or model outputs to safeguard individual privacy while maintaining analytical reliability. Experimental evaluations using real world datasets from traffic management, energy consumption, and healthcare services show that the framework significantly reduces risks of privacy leakage while preserving a high level of predictive accuracy. Although stronger privacy settings slightly decrease model performance, the overall results demonstrate an effective balance between privacy protection and data utility. Furthermore, the framework supports regulatory compliance with privacy standards and highlights future opportunities for scalable privacy preserving smart city applications.
Intelligent Resource Optimization in Internet of Things Environments Using Evolutionary Algorithms and Deep Reinforcement Learning
The optimization of resource allocation in Internet of Things (IoT) systems remains a major challenge due to the dynamic and highly variable nature of such environments. Traditional resource allocation techniques, including static and heuristic methods, often struggle to adapt to real time changes in resource demands, network conditions, and device behavior, leading to inefficiencies and suboptimal performance. This study investigates the integration of Evolutionary Algorithms (EA) and Deep Reinforcement Learning (DRL) as a hybrid optimization approach to improve resource management in IoT systems. The proposed model combines the global search capability of EA with the adaptive decision making efficiency of DRL, creating a flexible and scalable framework. Through experiments, the performance of the hybrid model was evaluated and compared with conventional resource allocation approaches. The results demonstrate improvements in resource utilization, latency reduction, and system scalability when using the hybrid strategy. The model was also tested in simulated and practical IoT scenarios, including smart city infrastructures and mobile edge computing environments. In these contexts, the hybrid method outperformed static and single algorithm approaches in terms of energy efficiency, throughput, and adaptability to changing conditions. The findings indicate that combining EA and DRL offers a promising solution for optimizing resource allocation in dynamic IoT environments. However, the hybrid approach introduces computational complexity, especially when IoT networks become large and heterogeneous. Therefore, further research is needed to improve computational efficiency, enhance scalability, and support practical deployment of hybrid optimization models in large scale IoT applications.
An Interpretable Machine Learning Framework for Risk Sensitive Decision Support in High Dimensional Financial Information Systems
In recent years, machine learning (ML) has transformed financial decision making systems by improving predictive accuracy, efficiency, and automation. Financial institutions increasingly adopt ML techniques to process large volumes of financial data and generate actionable insights for decision support. However, many traditional ML models operate as “black boxes,” making it difficult for stakeholders to interpret and trust the decisions produced by these systems. This lack of transparency creates significant challenges in critical financial tasks such as risk assessment, credit scoring, and fraud detection. To address this issue, this study proposes an interpretable machine learning framework that integrates advanced ML algorithms with feature attribution techniques to ensure both predictive performance and transparency. The framework combines models such as XGBoost, LightGBM, and CatBoost with interpretability approaches including SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to explain how individual features influence model predictions. The proposed framework is evaluated using real world financial transaction datasets, demonstrating its capability to accurately predict financial risks while simultaneously providing clear explanations of the decision making process. The results show that the framework enhances model interpretability and supports more transparent and accountable financial analysis. Furthermore, its adaptability to dynamic financial environments and its capacity to handle high dimensional data make it a valuable tool for financial institutions seeking to strengthen their risk management strategies. This study emphasizes the importance of balancing predictive accuracy, interpretability, and risk sensitivity in developing reliable decision support systems for the financial sector.
A Context Aware Hybrid Artificial Intelligence Model for Predictive Decision Making in Large Scale Distributed Information Systems
This study presents the development and evaluation of a context-aware hybrid AI model designed to improve predictive decision-making in large-scale distributed information systems. Conventional predictive models often fail to account for dynamic and contextual factors, leading to suboptimal performance in real-world applications. The research aims to address this gap by integrating machine learning techniques with rule-based reasoning, creating a hybrid model that adapts to varying environmental conditions and enhances decision accuracy. The model’s effectiveness is tested using datasets from large-scale distributed systems, such as traffic networks and industrial monitoring systems. The proposed method combines real-time contextual data with historical information, ensuring accurate and timely predictions. Experimental results show that the hybrid model outperforms traditional data-driven approaches, demonstrating higher prediction accuracy, improved adaptability, and robustness in dynamic environments. The study concludes that the context-aware hybrid AI model is a powerful tool for optimizing decision-making in large-scale systems, providing more reliable and relevant predictions. Future research could expand the model’s application to other domains and incorporate additional AI techniques to further enhance its performance and scalability.
Retrieved Augmented Generation (RAG)-Based Academic Chatbot for the Informatics Engineering Study Program, FTI UNISSULA
Fast, accurate, and reliable access to academic information remains a major challenge for Informatics Engineering students at the Faculty of Industrial Technology, Sultan Agung Islamic University (UNISSULA). Educational information found in various official documents is often difficult to find quickly, leading to confusion and dependence on external news that may be inaccurate. This research aims to create and implement an academic chatbot that uses Retrieval-Augmented Generation (RAG) technology to provide contextually relevant academic information based on official documents. The research method includes collecting and pre-processing official academic documents, dividing them into smaller parts, generating vectors using the Sentence-BERT model, and storing the vectors in the FAISS database to enable meaningful information retrieval. The RAG mechanism combines relevant document search results with the ability of a large language model (LLM) to generate accurate answers. Evaluation results show that the chatbot successfully answered all questions with a 100% success rate. Tests using the ROUGE-1 and BLEU-4 metrics showed excellent results in handling questions from the FAQ category, as well as demonstrating fairly good meaning relevance for non-FAQ questions, despite differences in answer inference. These findings indicate that the RAG approach successfully improves the accuracy, reliability, and context of chatbot answers in the academic field, as well as assisting in the development of intelligent academic information systems in higher education environments.
Application of Natural Language Processing for Sentiment Analysis in Digital Application User Feedback
The increasing volume of user feedback on digital applications presents a major challenge for developers and analysts, as manual analysis is time-consuming, subjective, and inefficient. This research aims to automatically identify sentiment patterns within large-scale user feedback using Natural Language Processing (NLP) techniques based on Transformer architecture. The study applies a Transformer-based model, specifically BERT, to classify sentiments into positive, neutral, and negative categories. User feedback data were collected from various digital application platforms, then preprocessed through tokenization, stopword removal, and stemming to ensure text quality and consistency. The fine-tuned Transformer model successfully achieved high accuracy in classifying sentiment patterns, demonstrating its ability to capture nuanced contextual meanings in textual data. The results revealed that positive feedback accounted for 45.2%, neutral for 23.8%, and negative for 31.0% of the total dataset. Compared to manual sentiment analysis, the Transformer-based approach showed greater efficiency, reduced analysis time, and minimized human bias. These findings highlight the transformative potential of deep learning models in automating large-scale text analytics. In conclusion, this research confirms that Transformer-based NLP methods provide a robust and scalable solution for sentiment analysis of user feedback, enabling digital application developers to monitor user satisfaction and improve service quality based on data-driven insights.
Copyright (c) 2025 Journal of Artificial Intelligence and Information Technology
Implementation of Reinforcement Learning in Adaptive Control of Mobile Robotics
This study investigates the application of Reinforcement Learning (RL) algorithms, specifically Q-learning and Deep Q-Network (DQN), for autonomous robot navigation in dynamic and uncertain environments. The main problem addressed is the limitation of traditional rule-based control systems in handling real-time environmental changes, including moving obstacles, varying terrains, and inconsistent sensor conditions. The research aims to evaluate the effectiveness of RL algorithms in generating optimal navigation paths, minimizing collision risks, and enhancing the robot’s adaptability to environmental variations. An experimental simulation-based approach was employed using platforms such as Gazebo, Robot Operating System (ROS), and Python-based simulators. The robot was trained through multiple interaction episodes, with state spaces including position, velocity, and obstacle distance, and a reward function designed to encourage safe, efficient, and goal-oriented navigation. Experimental results demonstrate that DQN significantly outperforms Q-learning, achieving shorter average path lengths (10.2 m vs. 12.5 m), lower collision rates (7% vs. 15%), faster convergence (180 vs. 350 episodes), and higher cumulative rewards (315 vs. 210). DQN’s learning curves are smoother and more stable, while Q-learning exhibits high fluctuations due to limited generalization. These findings confirm that DQN provides more efficient, safe, and adaptive navigation and holds substantial potential for next-generation autonomous robots in complex environments. Further integration with strategies such as curriculum learning and multi-agent coordination can enhance scalability and overall robotic system performance.
AI Ethics and Regulation Evaluation in the Development of Automated Systems
The rapid advancement of artificial intelligence (AI) has revolutionized various sectors, yet its unregulated and ethically ambiguous development poses significant social and legal risks. This study examines the ethical and regulatory dimensions that must be embedded in AI governance to ensure responsible innovation. The research aims to identify key ethical principles, evaluate current regulatory frameworks across jurisdictions, and highlight existing gaps between ethical aspirations and enforceable laws. Using a qualitative approach combining literature review and policy analysis, the study synthesizes findings from academic publications, policy documents, and legal regulations related to AI ethics and governance. The results reveal persistent regulatory fragmentation, limited global standardization, and insufficient institutional mechanisms for accountability and transparency. Comparative analysis among regions such as the European Union, the United States, China, and emerging economies shows diverse approaches to balancing innovation and regulation, each reflecting distinct ethical and political priorities. The synthesis indicates that future AI governance must move from voluntary ethical codes toward legally binding and globally interoperable frameworks. The study concludes that ethical regulation is not a constraint but a strategic enabler of sustainable innovation. Strengthening multi-stakeholder collaboration, harmonizing international standards, and institutionalizing ethics within legal systems are essential steps toward ensuring that AI development promotes human welfare, fairness, and global equity.
Network Intrusion Detection with Deep Neural Networks for Enhanced Cybersecurity
The increasing sophistication of cyberattacks has challenged the effectiveness of traditional signature-based intrusion detection systems, which rely heavily on predefined attack patterns. This study aims to develop and evaluate a Deep Neural Network (DNN)-based approach for network intrusion detection to enhance cybersecurity performance. The proposed model was trained and tested using two benchmark datasets NSL-KDD and CICIDS2017 following a systematic data preprocessing process, including normalization, feature encoding, and data partitioning. The DNN architecture employed multiple hidden layers with ReLU activation and Adam optimization to capture complex, non-linear traffic patterns. Experimental results demonstrated that the DNN model achieved accuracy levels of 98.6% on NSL-KDD and 99.2% on CICIDS2017, with corresponding high precision, recall, and F1-scores. The confusion matrix and ROC curve analysis further confirmed the model’s capability to accurately distinguish between normal and attack traffic, with an AUC value of 0.995, indicating superior classification performance. Comparative evaluation showed that the DNN significantly outperformed traditional signature-based systems by reducing false positives and effectively identifying novel attacks. In conclusion, the findings highlight the DNN’s potential as a robust and adaptive framework for modern network intrusion detection, capable of improving detection accuracy, operational efficiency, and resilience against evolving cyber threats.
5G Network Optimization Using Artificial Intelligence Algorithms to Improve Quality of Service
The rapid deployment of 5G networks presents unique challenges in meeting the growing demand for high-speed, reliable, and low-latency communication services. Traditional network management methods, which rely on static resource allocation and manual configurations, are insufficient to handle the dynamic nature of 5G networks. This paper explores the application of Artificial Intelligence (AI) algorithms, specifically Reinforcement Learning (RL), for optimizing 5G resource allocation and improving Quality of Service (QoS). The study reviews existing research on 5G network optimization, highlighting the limitations of traditional network management techniques and the need for intelligent, adaptive solutions. RL, with its ability to dynamically adjust to changing network conditions, offers a promising approach to address these challenges by optimizing traffic scheduling and resource utilization. The proposed method leverages RL to autonomously allocate resources in real-time based on network conditions, ensuring optimal network performance and enhanced QoS. Simulation results demonstrate significant improvements in latency, throughput, and packet loss when using RL-based scheduling compared to traditional static methods. RL-based optimization not only enhances the adaptability and stability of network performance but also improves resource efficiency by reducing congestion and minimizing packet loss during peak traffic periods. This paper also discusses the advantages of RL in terms of stability and adaptability, emphasizing its potential to outperform static methods in complex, high-demand 5G environments. In conclusion, the integration of AI-driven optimization, particularly RL, in 5G networks offers substantial benefits in terms of resource management and QoS improvement.
Face Recognition System with Deep Learning for Automated Workspace Access
Conventional access control systems, such as access cards and PIN codes, have long been utilized to secure high-security workspaces. However, these traditional methods are increasingly vulnerable to forgery, theft, and unauthorized sharing, posing significant security risks. The limitations of manual systems such as stolen cards and password leaks highlight the need for more secure and efficient alternatives. This study aims to enhance workplace security by implementing a face recognition system, offering a more secure and efficient method of access control that is less susceptible to fraud.Face recognition technology has evolved significantly with advancements in deep learning, particularly Convolutional Neural Networks (CNN) and FaceNet embedding. These techniques allow for more accurate and reliable face recognition, even under challenging conditions like varying lighting, different head poses, and occlusions. The proposed system uses CNN architecture to extract facial features and FaceNet embedding to create facial feature vectors, which are highly discriminative and robust. The dataset used for training includes facial images captured under diverse environmental conditions, with preprocessing techniques applied to ensure effective recognition.The face recognition system achieved a 95% identification success rate, demonstrating its robustness and reliability. In comparison to traditional methods, the face recognition system offers enhanced security, as it is resistant to spoofing attacks and does not require physical tokens, reducing the risk of unauthorized access. Additionally, face recognition is more user-friendly and hygienic compared to fingerprint or iris scanning systems. The proposed system is also cost-effective and easy to implement, particularly in large-scale environments.Future improvements could include real-time monitoring, integration with additional security measures, and exploration of adaptive learning techniques to further enhance the system’s performance and robustness in dynamic environments.
Development of AI-Based Academic Chatbot for Higher Education Student Services
This study focuses on the development of an AI-based academic chatbot designed to enhance the efficiency and responsiveness of university administrative services. The increasing demand for fast, accurate, and accessible academic information has created challenges for conventional service systems that rely heavily on human staff and manual operations. To address this issue, the research aims to design and evaluate a chatbot system that integrates Natural Language Processing (NLP) for understanding user input and Reinforcement Learning (RL) for continuous performance improvement based on feedback. The research employs a Design and Development Research (DDR) approach with system modules comprising text input, NLP engine, decision module with RL, and response output. The chatbot was trained using a dataset of academic queries and evaluated through three main metrics: accuracy, response time, and user satisfaction. The findings indicate that the chatbot achieved an accuracy rate of 85.2%, exceeding the target of 85%, with an average response time of 2.4 seconds—faster than the 3-second benchmark. Additionally, user satisfaction reached 84.3%, demonstrating the system’s effectiveness and usability. The overall results reveal that the combination of NLP and RL provides significant improvements in chatbot responsiveness and adaptability, enabling more human-like and contextually appropriate interactions. This study concludes that AI-driven chatbots can serve as an effective and scalable digital solution for academic service automation, reducing administrative workload and improving student engagement. Future research is recommended to enhance linguistic diversity, integrate multimodal communication, and implement continuous reinforcement learning to achieve more natural and intelligent academic interactions.
Adaptive Music Recommendation System Using Collaborative Filtering and Deep Learning
This research focuses on the development of an adaptive music recommendation system that combines collaborative filtering and deep learning to improve the accuracy and relevance of music suggestions. The primary problem addressed in this study is the limitation of traditional recommendation methods, such as collaborative filtering, which struggle with issues like data sparsity and the cold start problem. The objective of the research is to design a hybrid recommendation model that enhances collaborative filtering by integrating deep learning techniques to capture complex, nonlinear relationships between users and items. The proposed method employs user-item interactions, such as ratings and listens, to create an initial recommendation model using matrix factorization or nearest neighbor techniques to predict unknown preferences. To further refine the recommendations, deep neural networks (DNNs) are utilized, specifically through multi-layer perceptrons (MLPs) or autoencoders, to analyze intricate patterns and temporal dynamics in user behavior. The findings indicate that the hybrid model leads to a 20% increase in recommendation accuracy compared to traditional methods, demonstrating superior performance in predicting user preferences. Additionally, users reported a more personalized experience with fewer irrelevant recommendations, improving overall user satisfaction. The model was trained using the Adam optimizer and appropriate loss functions to ensure optimal performance. In comparison with traditional collaborative filtering, the hybrid system adapts more effectively to changing user preferences, providing more accurate and diverse music suggestions. In conclusion, the proposed adaptive system significantly enhances recommendation accuracy and user engagement. Future work should explore additional deep learning architectures, such as convolutional and recurrent neural networks, and investigate the use of real-time data to further personalize recommendations. The system has great potential for application across various music platforms, offering users highly personalized and relevant music suggestions.
Development of AI-BasReinforcement Learnined Academic Chatbot for Higher Education Student Services
Development of Automatic Object Detection System for Autonomous Vehicles
Autonomous vehicles (AVs) depend on advanced object detection systems to ensure safety and efficiency in navigation. Real-time detection of vehicles, pedestrians, and obstacles under diverse environmental conditions is vital for AV performance. This study proposes a real-time object detection system using the YOLOv5 architecture, a deep learning model recognized for its balance of speed and accuracy. The system aims to deliver precise and efficient detection to support safe navigation and decision-making in AVs. Real-time detection enhances situational awareness, obstacle avoidance, and overall driving safety, making it essential in autonomous systems. The literature review discusses conventional sensor fusion methods, such as integrating LIDAR and cameras, alongside modern machine learning approaches, especially deep learning and Convolutional Neural Networks (CNNs). YOLOv5 stands out for its capability in real-time processing, with proven success in detecting vehicles and pedestrians in dynamic environments. Despite these advantages, challenges like low-light conditions, occlusion, and variable weather still affect detection performance. The proposed system trains and fine-tunes YOLOv5 with diverse datasets to achieve reliable detection outcomes. Experimental results show that YOLOv5 maintains high accuracy and faster processing compared to traditional methods. This demonstrates its potential as a robust real-time detection framework for AVs. Future enhancements could involve incorporating multiple sensors and refining YOLOv5’s adaptability to complex driving scenarios
The Role of Machine Learning in Cybersecurity: Detecting and Preventing Threats
The increasing frequency and sophistication of cyber threats have highlighted the critical need for advanced security measures in cybersecurity. Machine learning (ML) has emerged as a powerful tool in detecting and preventing these threats by analyzing large datasets and identifying patterns indicative of malicious activity. This study aims to explore the role of machine learning in enhancing cybersecurity, focusing on its application in threat detection and prevention. Through a comprehensive review of current machine learning algorithms, such as supervised and unsupervised learning, anomaly detection, and neural networks, this research examines their effectiveness in various cybersecurity contexts. The findings suggest that ML techniques significantly improve the accuracy and efficiency of threat identification, offering real-time protection against a wide range of cyber-attacks, including malware, phishing, and intrusion attempts. Furthermore, the study discusses the potential challenges of integrating ML into existing cybersecurity systems, such as data privacy concerns and the need for continuous model training. The implications of this research emphasize the need for further innovation in ML-driven cybersecurity solutions to safeguard sensitive information and maintain the integrity of digital infrastructures.
Integrating Artificial Intelligence in Business Analytics: Optimizing Performance and Efficiency
In recent years, the integration of Artificial Intelligence (AI) in business analytics has emerged as a powerful tool for optimizing performance and enhancing operational efficiency. This research explores the application of AI in business analytics, focusing on how AI-driven solutions can improve decision-making, streamline processes, and increase overall productivity. The study employs a qualitative approach, analyzing case studies from various industries to assess the impact of AI technologies on business outcomes. The findings suggest that AI integration enables businesses to predict trends, automate tasks, and extract valuable insights from large datasets, leading to improved strategic planning and resource management. Moreover, the use of AI facilitates more accurate forecasting, faster decision-making, and a more responsive approach to market changes. The implications of this research highlight the growing importance of AI in business analytics and its potential to drive innovation and competitive advantage in the modern business landscape.
Ethical Considerations in AI Development: Balancing Innovation and Responsibility
The rapid advancement of artificial intelligence (AI) presents both opportunities and ethical challenges in its development and application. This study examines the critical ethical considerations in AI development, emphasizing the balance between technological innovation and responsible implementation. The research explores key ethical concerns such as bias in algorithms, transparency, data privacy, and accountability. Using a qualitative approach, this study analyzes ethical frameworks and industry best practices to ensure AI development aligns with societal values. The findings highlight the need for robust ethical guidelines, interdisciplinary collaboration, and regulatory policies to mitigate risks while fostering innovation. The study concludes that balancing innovation with ethical responsibility is essential for sustainable AI advancements. The implications of this research provide insights for policymakers, technology developers, and stakeholders to establish ethical AI governance that benefits society while minimizing potential harm.
Natural Language Processing for Smart Assistants: Improving Human-Computer Interaction
Natural Language Processing (NLP) plays a crucial role in enhancing smart assistants, enabling more intuitive and efficient human-computer interaction. This study explores the advancements in NLP technologies and their impact on improving the responsiveness and contextual understanding of smart assistants. The research aims to analyze key NLP techniques, including machine learning-based language models, sentiment analysis, and speech recognition, to optimize user interactions. A systematic evaluation is conducted to assess the effectiveness of these techniques in real-world applications. The findings indicate that integrating deep learning and transformer-based models significantly enhances the accuracy and adaptability of smart assistants. Moreover, improved language comprehension and personalized responses contribute to a more natural and engaging user experience. These advancements have broad implications for various domains, including customer service, healthcare, and education. This study highlights the potential of NLP-driven smart assistants in reshaping human-computer communication, emphasizing the importance of continuous innovation in AI-driven conversational technologies.