Artificial intelligence is now applied in many fields beyond computer science. In healthcare, it enables early disease detection and improves patient outcomes. This study develops a model that uses AI to find abnormal patterns in cough sounds. A cough is a key symptom of asthma and other respiratory diseases. Previous research has focused on raw audio signals of coughs. In contrast, we analyze spectrogram images derived from these sounds to improve accuracy. We designed a new convolutional neural network (CNN) for this purpose and the recommended CNN is termed as TwoConvNeXt. To showcase the classification performance of the recommended TwoConvNeXt model, a cough sound dataset has been utilized and the recommended TwoConvNeXt achieved 99.66% classification test accuracy. 
These results illustrate that the presented TwoConvNeXt CNN architecture can be useful in both research and clinical settings. This CNN model can be utilized for other image classification problems. It may aid in the early diagnosis of respiratory conditions. Future work will expand the dataset and test the model on larger, more diverse samples.

With the proliferation of advanced driver assistance systems and continued advances in autonomous vehicle technology, there is a need for accurate, real-time methods of identifying and interpreting traffic signs. The importance of traffic sign detection can't be overstated, as it plays a pivotal role in improving road safety and traffic management. This proposed work suggests a unique real-time traffic sign detection and recognition approach using the YOLOv8 algorithm. Utilizing the integrated webcams of personal computers and laptops, we capture live traffic scenes and train our model using a meticulously curated dataset from Roboflow. Through extensive training, our YOLOv8 version achieves an excellent accuracy rate of 94% compared to YOLOV7 at 90.1% and YOLOv5 at 81.3%, ensuring reliable detection and recognition across various environmental conditions. Additionally, this proposed work introduces an auditory alert feature that notifies the driver with a voice alert upon detecting traffic signs, enhancing driver awareness and safety. Through rigorous experimentation and evaluation, we validate the effectiveness of our approach, highlighting the importance of utilizing available hardware resources to deploy traffic sign detection systems with minimal infrastructure requirements. Our findings underscore the robustness of YOLOv8 in handling challenging traffic sign recognition tasks, paving the way for widespread adoption of intelligent transportation technologies and fostering the introduction of safer and more efficient road networks. In this paper, we compare the unique model of YOLO with YOLOv5, YOLOv7, and YOLOv8, and find that YOLOv8 outperforms its predecessors, YOLOv7 and YOLOv5, in traffic sign detection with an excellent overall mean average precision of 0.945. Notably, it demonstrates advanced precision and recall, especially in essential sign classes like "No overtaking" and "Stop," making it the favored preference for accurate and dependable traffic sign detection tasks.

This study thoroughly looks at how the prices and activity of players of popular indie games on Steam changed after COVID-19. It uses data from SteamDB, which has lots of info about game availability, sales, prices, activity of players, followers, positive and negative reviews on Steam and Twitch viewers. The goal is to deeply analyze how indie game makers and publishers set their prices and reactions of players to games which release date start from period before, during and after COVID-19. The focus is on how they changed their pricing models due to big shifts in market demand and consumer behavior because of the pandemic and how players reacted to these price changes in the context of wage cuts and layoffs. Reactions of players can be tracked not only by the statistics of the maximum or average online in the game, but also by the number of positive and negative reviews, because in difficult times it was important for players to correctly distribute their available funds and not to become disappointed in game and not to let other players become disappointed
By studying these changes, the aim is to find out how the indie game industry responded to tough times and new chances in the digital entertainment world. Since the study is being conducted in the post-COVID-19 period, it is also aimed at helping developers choose the right strategy when pricing their new Indie games or changing the prices of their existing Steam Indie games.
The main objects of research are indie games because this genre is one of the most popular in SteamDB, and to create such games requires less costs, therefore their price is acceptable for the average player.

This project addresses the growing issue of fake reviews by developing models capable of detecting them across different platforms. By merging five distinct datasets, a comprehensive dataset was created, and various features were added to improve accuracy. The study compared traditional supervised models like Logistic Regression and SVM with deep learning models. Notably, simpler supervised models consistently outperformed deep learning approaches in identifying fake reviews. The findings highlight the importance of choosing the right model and feature engineering approach, with results showing that additional features don’t always improve model performance.

The increasing urgency for sustainable practices has motivated this research to explore the barriers hindering the adoption of digital technologies in circular systems. As industries seek to leverage IoT for enhanced efficiency and sustainability, understanding these barriers is crucial for effective implementation. This study employs a comprehensive, multi–dimensional approach, integrating insights from a literature review and expert interviews with industry professionals. Key findings reveal that technological complexity and high initial costs are the most significant barriers, highlighting the need for targeted strategies to address these challenges. Additional barriers include regulatory compliance issues and unclear return on investment, which further complicate the adoption process. The study's conclusion emphasizes that overcoming these barriers is essential for facilitating the successful integration of digital technologies in circular economies. Furthermore, the research identifies the necessity for future investigations into the interactions between these barriers and the effectiveness of various interventions. The novelty of this study lies in its holistic examination of the multifaceted barriers, combining qualitative insights with a structured analytical framework. This approach not only contributes to the existing literature on Digitization but also offers practical implications for stakeholders aiming to enhance sustainability and efficiency in their operations. By addressing the identified challenges, organizations can pave the way for a more circular and resilient future, ultimately driving innovation and growth in the rapidly evolving digital landscape.

The use of cloud computing, particularly virtualized infrastructure, offers scalable resources, reduced hardware needs, and energy savings. In Ethiopian public hospitals, the lack of integrated healthcare systems and a national data repository, combined with existing systems deficiencies and inefficient traditional data centers, contribute to energy inefficiency, carbon emissions, and performance issues. Thus, evaluating the energy efficiency and performance of a cloud-based model with various workloads and algorithms is essential for its successful implementation in healthcare systems and digital health solutions. The study experimentally evaluates a cloud-based model's energy efficiency and performance for smart healthcare systems, employing descriptive and experimental designs to simulate cloud infrastructure. Simulations are conducted on diverse workloads in CloudSim using power-aware (PA) algorithms (along with VmAllocationPolicy and VmSelectionPolicy), and dynamic voltage frequency scaling (DVFS). Results reveal that the number of VMs and their migrations significantly impact energy consumption, with some algorithms achieving notable energy savings. Lr/Lrr-based algorithms are particularly energy-efficient, with LrMc and LrrMc saving 29.36% more energy than IqrMu at 55 VMs, and LrrRs saving 30.20% more at 1,765 VMs. DVFS adjusts energy consumption based on the number of VMs, while non-power-aware (NPA) consumes maximum energy based on hosts, regardless of the number of VMs. VM migrations, energy consumption, and average SLAV are positively correlated, while SLA is negatively correlated with these factors. In PlanetLab, energy consumption and average SLAV show a strong positive correlation (0.956) at Workload6, while SLA at Workload2 and average SLAV at Workload1 show a weak negative correlation (-0.055). Excessive migrations can disrupt the system's stability/performance and cause SLA violations. Task completion time is influenced by VM processing power and cloudlet length, being inversely proportional to VM processing power and directly proportional to cloudlet length. Overall, the findings suggest that cloud virtualization and energy-efficient algorithms can enhance healthcare systems performance, patient care, and operational sustainability.

This paper introduces a novel approach for forecasting the price trends of agricultural commodities to address the issue of price volatility faced by both farmers and consumers. The accurate forecasting of food prices is particularly crucial in emerging nations such as India where food security is a top priority. To achieve this goal, the paper presents an ensemble learning-based approach for predicting the agricultural commodity price (ACP) trend. Using dataset namely rainfall and wholesale pricing index (WPI), the study compares the performance of various individual and ensemble regression models. The findings of this work demonstrated that the novel competitive ensemble regression (CER) approach outperforms traditional individual regression models in predicting price fluctuations trend accurately. This approach has the high potential and more precise prediction to afford farmers and dealers, also make the model suitable for the financial industries.