This work presents a new circularly polarized (CP) monopolar microstrip antenna for future smart mobility communication system. The proposed antenna consists of hexagonal quasi-circular patches on the top plane, complemented by asymmetric trapezoid slots on the ground. By integrating rotated asymmetric trapezoid ground slots, the antenna induces a radiation pattern characterized with right-handed CP. The parameters have been optimized by precise design considerations for enhanced performance.
This paper explores the transformative impact of AI-powered digital assistants on business operations and the evolving role of secretarial work. As organizations increasingly integrate artificial intelligence (AI) into their workflows, digital assistants are becoming essential tools that streamline tasks, enhance the efficiency, and support decision-making processes. These AIdriven technologies are not only automating routine administrative functions but also enabling more strategic contributions by secretaries, such as managing complex schedules, data analysis, and personalized communication. The study examines how AI is reshaping the traditional secretarial role, leading to a shift in job responsibilities and skill requirements. It also discusses the potential challenges such as ethical considerations and the need for upskilling that arises with this technological advancement. The findings suggest that AI-powered digital assistants are set to revolutionize the business landscape, offering both opportunities and challenges for the future of secretarial work.
Efficient resource allocation in device-to-device (D2D) communication within 6G networks is crucial for enhancing overall network performance and efficiency. This paper presents a novel Deep Learning (DL) based approach for radio resource allocation (RRA), leveraging distributed artificial intelligence (DAI) using belief-desire-intention extended (BDIx) agents, dynamic feedback allocation, and a Deep Feedback Neural Network (DFBNN). Additionally, Federated Learning (FL) is integrated to enable distributed training across BDIx agents, serving as D2D Relays (D2DR) or D2D Multihop Relays (D2DMHR), ensuring data privacy and reducing communication overhead. The proposed method is thoroughly evaluated against traditional graph-based and game-theoretic algorithms and deep feedforward neural networks (DFNNs). Results demonstrate significant improvements in interference management, data rate, and execution time. By providing scalable, adaptive, and resilient resource allocation, this proposed method meets the stringent requirements of 6 G applications, paving the way for more efficient and reliable network operations.
As COVID-19 cases continue to rise, minimizing physical contact is essential to curb the virus’s spread. IDE LPKIA, an educational institution, currently uses a centralized attendance system based on fingerprint scanning, which increases the physical contact and thus the potential for virus transmission. To address this issue, this research proposes a new attendance system that allows employees to mark their attendance independently using their personal smartphones, eliminating the need for centralized attendance stations. The proposed system integrates facial recognition and location radius technology. Facial recognition is implemented using a convolutional neural network (CNN) to ensure accurate identification, while the Haversine formula is employed to calculate the location radius, ensuring attendance can only be registered within a specific geographic area around the institution. This approach not only reduces physical contact but also prevents attendance fraud, as employees can only check in based on their facial identity and within the defined location radius. This system aims to enhance safety and integrity in attendance tracking amidst the ongoing pandemic.
This study focuses on the market share reduction of an Indonesian Internet provider in spite of rising revenue and client base. More research on this issue would be intriguing, particularly in light of how crucial customer pleasure is to retaining market share. There aren’t many research that particularly examine how brand recognition and image affect consumers’ intentions to stick with fixed broadband packages, particularly in Indonesia. By examining the impact of brand image and brand awareness on continuation intention through customer satisfaction on Internet provider goods in Indonesia, this study seeks to close a gap in the literature. We implemented a quantitative technique by disseminating a structured survey. We employed the SEM-PLS approach to examine the data that we had gathered. According to our research, customer satisfaction is positively and significantly impacted by brand image, customer satisfaction is positively and significantly impacted by continuity intention, and customer satisfaction is positively and significantly impacted by brand awareness, which in turn influences Continuance Intention through customer satisfaction. Conversely, neither directly nor indirectly, brand awareness has no appreciable impact on customer satisfaction or continuation intention. Also, Continuance Intention is not greatly impacted by Brand Image.
The Internet is integral to modern life, with Internet service provider (ISP) offering appealing deals to meet the demand for unlimited data. However, reality often falls short of expectations. While recommendation systems exist, user-centric options are rare. This paper proposes a novel ISP selection methodology using user experience data and a calibrated random forest (CRF) model. Unlike traditional methods that focus on advertised features, this approach emphasizes user-defined criteria such as cost, device connectivity, and technical support experience. By analyzing survey data, the model highlights the critical link between user needs and support quality, enabling users to choose ISPs that prioritize customer service. The model demonstrates promising results with a strong R-squared value and low mean squared error (MSE). This user-centric approach fosters informed decision-making, potentially driving competition and encouraging ISPs to improve service standards, laying a foundation for future developments in ISP selection.
This study explores the integration of artificial intelligence (AI) with human-centered design (HCD) principles in crafting user interface (UI) and user experience (UX) for epromotion platforms within Indonesia’s smart cities. As culinary tourism emerges as a significant driver of local economies, particularly in diverse and culturally rich countries like Indonesia, the need for innovative promotional strategies becomes essential. AI technologies are increasingly being utilized to personalize and enhance user interactions, providing tailored recommendations and engaging experiences for tourists. However, to ensure these AI-driven solutions meet the needs and expectations of users, incorporating HCD in the design process is crucial. This research examines how AI-powered public applications can effectively boost culinary tourism by delivering personalized, seamless, and culturally relevant experiences to users. The study focuses on designing UI/UX that not only leverages AI for functional efficiency but also prioritizes the emotional and cognitive engagement of users, ensuring that technology serves as an enabler rather than a barrier. By analyzing current trends and case studies within Indonesia’s smart cities, the paper provides insights into best practices for integrating AI and HCD in e-promotion strategies. The findings aim to offer valuable guidelines for developers, marketers, and policymakers in enhancing the appeal and effectiveness of digital tools designed to promote culinary tourism, ultimately contributing to the growth of Indonesia’s tourism sector in the smart city context.
This article chooses to use the random forest algorithm to improve the performance of network intrusion detection systems (IDS). The algorithm significantly improves the accuracy, recall and precision of network intrusion detection compared to traditional methods. The required data and experimental results were obtained from the LUFlow dataset by using a more accurate feature extraction method. Eventually, the readability and comprehension of the experimental results were enhanced by visualizing them. Overall, the performance of the network IDS based on the random forest method has been significantly improved. However, there are still some problems in the experiment, such as the lack of comparison with other commonly used intrusion detection methods or algorithms. Similar problems make the experiment lack of comprehensiveness. Therefore, future research should consider introducing more kinds of intrusion detection methods for comparative analysis to further validate and improve the performance of the system. In addition, extending the dataset of the experiments and improving the feature extraction techniques may also bring additional improvements. In summary, although the performance of the random forest-based network IDS has been improved, there is still much room for improvement and research potential.
In recent years, there have been frequent incidents of foreign objects intruding into railway and Airport runways. These objects can include pedestrians, vehicles, animals, and debris. This paper introduces an improved YOLOv5 architecture incorporating FasterNet and attention mechanisms to enhance the detection of foreign objects on railways and Airport runways. This study proposes a new dataset, the aero and rail foreign object detection (AARFOD), which combines two public datasets for detecting foreign objects in aviation and railway systems. The dataset aims to improve the recognition capabilities of foreign object targets. Experimental results on this large dataset have demonstrated significant performance improvements of the proposed model over the baseline YOLOv5 model, reducing computational requirements. Improved YOLO model shows a significant improvement in precision by $1.2 \%$, recall rate by $1.0 \%$, and mAP@. 5 by $0.6 \%$, while mAP@. $5-.95$ remained unchanged. The parameters were reduced by approximately ${2 5. 1 2 \%}$, and GFLOPs were reduced by about $10.63 \%$. In the ablation experiment, it is found that the FasterNet module can significantly reduce the number of parameters of the model, and the reference of the attention mechanism can slow down the performance loss caused by lightweight.
Delicate cloth simulations have long been desired in computer graphics. Various methods were proposed to improve engaged force interactions, collision handling, and numerical integrations. Deep learning has the potential to achieve fast and real-time simulation, but common neural network (NN) structures often demand many parameters to capture cloth dynamics. This paper proposes a physics-embedded learning framework that directly encodes physical features of cloth simulation. The convolutional NN is used to represent spatial correlations of the mass-spring system, after which three branches are designed to learn linear, nonlinear, and time derivate features of cloth physics. The framework can also integrate with other external forces and collision handling through either traditional simulators or sub NNs. The model is tested across different cloth animation cases, without training with new data. Agreement with baselines and predictive realism successfully validate its generalization ability. Inference efficiency of the proposed model also defeats traditional physics simulation. This framework is also designed to easily integrate with other visual refinement techniques like wrinkle carving, which leaves significant chances to incorporate prevailing machine learning techniques in 3D cloth amination.
The developments of artificial intelligence (AI) are growing along with its applications. This growth is so quick that it often surprises even researchers who had hypothesized different times. Within the field of criminal profiling, this is interesting because it can help to recognize errors and biases that are typical of humans [1]. Even though training AI to recognize emotions based on biometric parameters is becoming easier, the subsequent analyses are problematic. In fact, it is difficult to interpret biometric data, which are also influenced by cultural and social factors. In terrorism analysis, for instance, the behaviors that are analyzed are different among the different groups or tribes. Therefore, the influence of social factors goes beyond the analysis of the complex neural responses [2–4]. Another element that plays a role is in the interpretation of emotions for the judicial system, which is represented by ethical and moral factors [5]. Artificial intelligence cannot be used for reconstructing the origin of a crime [6] and only an expert’s opinion can be considered reliable [7]. Only an analysis based on the individual and aspects, and only the knowledge of the psychopathology, together with the scientific analysis of the nonverbal language, can help reconstruct the origin and the dynamics of the crime [10–12]. In conclusion, even though AI offers an important support since it can speed up some processes of the analysis, it currently cannot replace humans when it comes to profiling [13, 14]. In light of the chosen method, the analyses are ongoing, and the initial results indicate a trend toward greater reliability for profiling conducted by a human compared to that performed by AI. This is not due to the AI’s capacity for emotional recognition but rather to the methodology employed by the AI. Humans respond to any sensory stimulation with an emotion, making any inference, reasoning, or behavioral choice closely dependent on the emotion experienced. In contrast, AI recognizes emotions through a process of analysis comparable to purely cognitive processes. Consequently, the capacity for emotional recognition through empathy is lacking. To guarantee the best possible analysis and limit the possibility of moral and ethical issues, it is extremely important for a human to oversee this process. AI can be used to recognize emotions based on biometric alterations, but it should not go further than that. Relying solely on its conclusions would be sterile and incomplete, and from a legal standpoint, could impact the admissibility of the analysis in court.
In this paper, we have considered the coverage problem in wireless sensor network (WSN) on a convex subset of $R^{2}$. Sensors are dropped from the air randomly on some pre-fixed points, which is known as vertices, of region of interest (ROI). We use optimal partition of the ROI, which is actually partition in several regular hexagons. Since sensors are distributed randomly, a sensor may not be placed on the target vertex. For this reason, ROI will not be completely covered by a set of sensors. In practice, few more sensors are deployed on few (randomly chosen) vertices or used actuator (it can carry sensors to the proper vertex) to reduce the uncovered region or area. In one of our previous works, we have developed a strategy as follows: reduce the distance among two adjacent vertices and deployed one sensor on a vertex so that total number of sensors will be same as in existing old method (drop two sensors on some vertices and one sensor on the rest). We have compared the proportion of uncovered region using the commonly used old strategy with our previous one. We have simulated for several values of percentage of extra sensors and observed that our previous strategy is better for low standard deviation (s.d.), but not better for higher s.d. in both two and three dimensions. Inspiring from the above fact, in this paper, we combined above two strategies to find a general one, for deploying sensors in two dimensions. The excess sensors are divides in two parts. One part is used for decrease the side of the regular hexagon and other part is used for using one more sensor on some selected points. We simulate uncovered area and results indicate the optimal choice of these two parts, which change with the standard deviation of randomness.
