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Innovative Mechanical Ventilation Control for Enhanced Indoor Air Quality and Energy Efficiency

Energy engineering 2025年第3期122卷 861-883页

作者： Giovanni Miracco Francesco Nicoletti Vittorio Ferraro Dimitrios Kaliakatsos Department of Mechanical Energy and Management Engineering(DIMEG)University of CalabriaCosenza87036Italy Department of Computer ModellingElectronics and Systems Engineering(DIMES)University of CalabriaCosenza87036Italy

Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational *** study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy *** classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely *** each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity *** proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and *** addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy *** demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air *** findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as ***,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor *** study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.

关键词： Indoor air quality mechanical ventilation system innovative control system energy efficiency Energy-Plus simulation

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Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

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computers, Materials & Continua 2024年第4期79卷 117-142页

作者： Ankita Gangwar Vijaypal Singh Dhaka Geeta Rani Shrey Khandelwal Ester Zumpano Eugenio Vocaturo Department of Computer and Communication Engineering Manipal University JaipurJaipurIndia Department of IoT and Intelligent Systems Manipal University JaipurJaipurIndia Department of Computer Engineering ModellingElectronics and Systems(DIMES)University of CalabriaRende(Cosenza)Italy National Research Council Institute of Nanotechnology(NANOTEC)Rende(Cosenza)Italy

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20%of the total *** increase of 3.3%in consumption is predicted from 2024 to *** are also rich in iron,potassium,antioxidant lycopene,vitamins A,C and K which are important for preventing cancer,and maintaining blood pressure and glucose ***,tomatoes are globally important due to their widespread usage and nutritional *** face the high demand for tomatoes,it is mandatory to investigate the causes of crop loss and minimize *** are one of the major causes that adversely affect crop yield and degrade the quality of the tomato *** leads to financial losses and affects the livelihood of ***,automatic disease detection at any stage of the tomato plant is a critical *** learning models introduced in the literature show promising results,but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of ***,most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf ***,the existing techniques lack in recognizing multiple diseases on the same *** address these concerns,we introduce a customized deep learning-based convolution vision transformer *** achieves an accuracy of 93.51%for classifying tomato leaf images with plain as well as complex backgrounds into 13 *** requires a space storage of merely 5.8 MB which is 98.93%,98.33%,and 92.64%less than stateof-the-art visual geometry group,vision transformers,and convolution vision transformermodels,*** training time of 44 min is 51.12%,74.12%,and 57.7%lower than the above-mentioned ***,it can be deployed on(Internet of Things)IoT-enabled devices,drones,or mobile devices to assist farmers in the real-time monitoring of tomato *** p

关键词： Tomato disease transformer deep learning mobile devices

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Unveiling The Role of Large Language Models in Healthcare 4th

Unveiling The Role of Large Language Models in Healthcare

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Ruga, Tommaso DIMES—Department of Computer Engineering Modeling Electronics and Systems University of Calabria Rende Italy CNR-NANOTEC National Research Council Rende Italy

ISBN: (纸本)9789819769919

Artificial intelligence has proven to be a valuable tool in supporting the medical sector. The integration of artificial intelligence and medicine can lead to more timely and accurate diagnoses, analyze responses to medications, and provide personalized treatment suggestions for patients. Additionally, it can streamline administrative processes, allowing medical professionals to focus more on direct patient care. Concerning this last aspect, the role of Large Language Models has become crucial to provide solutions that can mimic the behavior of a human operator and reduce the human-machine distancing. Large language models can respond to free-text queries without being specifically trained in the task in question, causing excitement and concern about their use in healthcare, especially when these responses are related to diagnoses or treatments. The objective of this work is to analyze the literature from a quantitative point of view and thoroughly understand what aspects the research has focused on up to this point, with the perspective of identify the most discussed topics and the current state of research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Patient treatment

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IoT and Artificial Intelligence Integration for a Stormwater Monitoring and Management System 1

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4th International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Piro, Patrizia Palermo, Stefania Anna Tropea, Mauro Saleh, Mohammed M. De Rango, Floriano Department of Civil Engineering University of Calabria Rende87036 Italy DIMES Department University of Calabria Rende87036 Italy

ISBN: (数字)9783031812446

ISBN: (纸本)9783031812439

Stormwater management is a crucial environmental and public health issue, particularly in urban areas, where impervious surfaces limit rainwater infiltration into the soil. Traditional stormwater management systems have limitations in detecting and predicting the behavior of runoff, which can lead to flooding. This study focuses on using the Internet of Things (IoT) and Artificial Intelligence (AI) technologies to optimize stormwater systems. Overall, these systems employ various IoT devices to monitor critical parameters of the stormwater network in real-time. The data collected by these devices are processed and analyzed using AI algorithms such as machine learning and deep learning, which enable the system to detect anomalies, predict potential flooding events, and provide decision-makers with valuable insights into the behavior of surface runoff. Integrating IoT and AI technologies in stormwater man-agement improves efficiency, early issues detection, real-time decision-making, and reduced environmental damage. Thus, this study shows a possible architecture that exploits IoT and AI technologies to build a stormwater management system that can provide real-time and predictive control and monitoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Storm sewers

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Editorial Deep Learning-Empowered Big Data Analytics in Biomedical Applications and Digital Healthcare

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年第4期21卷 516-520页

作者： Zhou, Xiaokang Leung, Carson K. Wang, Kevin I-Kai Fortino, Giancarlo Shiga University Faculty of Data Science Shiga Hikone522-0069 Japan University of Manitoba Department of Computer Science WinnipegR3T 2N2 Canada University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand University of Calabria DIMES Department Rende CS87036 Italy

Deep learning and big data analysis are among the most important research topics in the fields of biomedical applications and digital healthcare. With the fast development of artificial intelligence (AI) and Internets of Things (IoT) technologies, deep learning (DL) for big data analytics - including affective learning, reinforcement learning, and transfer learning - are widely applied to sense, learn, and interact with human health. Examples of biomedical applications include smart biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and eye tracking. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. With an enormous amount of historical data, DL and big data analysis technologies are able to identify potential linkage between features and possible risks, raise important decision for medical diagnosis, and provide precious advice for better healthcare treatment and lifestyle. Although significant progress has been made with AI, DL, and big data analytic technologies for medical and healthcare research, there remain gaps between the computer-aided treatment design and real-world healthcare demands. In addition, there are unexplored areas in the fields of healthcare and biomedical applications with cutting-edge AI and DL technologies. Hence, exploring the possibility of DL and big data analytics in the fields of biomedical applications and digital healthcare is in high demand. © 2004-2012 IEEE.

关键词： Big data

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Disease detection,severity prediction,and crop loss estimation in MaizeCrop using deep learning

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Artificial Intelligence in Agriculture 2022年第1期6卷 276-291页

作者： Nidhi Kundu Geeta Rani Vijaypal Singh Dhaka Kalpit Gupta Siddaiah Chandra Nayaka Eugenio Vocaturo Ester Zumpano Department of Computer and Communication Engineering Manipal University JaipurJaipurIndia ICAR DOS in Biotechnology University of Mysore ManasagangotriMysore 570005India DIMES(Department of Computer Engineering ModelingElectronics and Systems)University of CalabriaItaly Nanotec Italian National Research Council87036 RendeCSItaly

The increasing gap between the demand and productivity of maize crop is a point of concern for the food industry,and ***'susceptibility to diseases such as Turcicum Leaf Blight,and Rust is a major cause for reducing its *** detection,and classification of these diseases,calculation of disease severity,and crop loss estimation is a time-consuming ***,it requires expertise in disease ***,there is a need to find an alternative for automatic disease detection,severity prediction,and crop loss *** promising results of machine learning,and deep learning algorithms in pattern recognition,object detection,and data analysis motivate researchers to employ these techniques for disease detection,classification,and crop loss estimation in maize *** research works available in literature,have proven their potential in automatic disease detection using machine learning,and deep learning ***,there is a lack none of these works a reliable and real-life labelled dataset for training these ***,none of the existing works focus on severity prediction,and crop loss *** authors in this manuscript collect the real-life dataset labelled by plant *** propose a deep learning-based framework for pre-processing of dataset,automatic disease detection,severity prediction,and crop loss *** uses the K-Means clustering algorithm for extracting the region of ***,they employ the customized deep learning model‘MaizeNet’for disease detection,severity prediction,and crop loss *** model reports the highest accuracy of 98.50%.Also,the authors perform the feature visualization using the ***,the proposed model is integrated with a web application to provide a userfriendly *** efficacy of the model in extracting the relevant features,a smaller number of parameters,low training time,high accuracy favors its importance as an assisting tool for plant pathology experts.T

关键词： Disease detection Crop loss Severity Deep learning Maize

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Dataset Balancing Techniques and Supervised Learning Algorithms for Predictive Analysis of Rice and Corn Yields 4th

Dataset Balancing Techniques and Supervised Learning Algori...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Di Mauro, Lorenzo Vocaturo, Eugenio Zumpano, Ester CNR-NANOTEC National Research Council Rende Italy DESF—Department of Economic Statistics and Finance University of Calabria Rende Italy DIMES—Department of Computer Engineering Modeling Electronics and Systems University of Calabria Rende Italy

ISBN: (纸本)9789819769919

Agriculture plays a fundamental role in the global socio-economic context, forming the pillar of communities and economies around the world. This study focuses on the impact of pesticides and fertilizers on the crop yield of rice and maize in India, employing supervised machine learning algorithms. The methodology begins with the understanding and pre-processing of an agricultural yield dataset, with a focus on dataset balancing to handle any imbalances in the target variable categories. Several data mining models are then implemented, and selected based on various performance measures. The final stage involves the prediction of crop yield in relation to pesticide and fertilizer levels, followed by the interpretation of the results. In summary, the work provides a starting point for the analysis of agricultural yield in varied geographic settings with diversity in climate and agricultural practices. The methodologies proposed in this study provide a useful framework for future research on agricultural yield optimization solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

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Predictive Analysis for Early Detection of Breast Cancer Through Artificial Intelligence Algorithms 4th

Predictive Analysis for Early Detection of Breast Cancer T...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Gagliardi, Marco Ruga, Tommaso Vocaturo, Eugenio Zumpano, Ester DESF—Department of Economic Statistics and Finance University of Calabria Rende Italy DIMES—Department of Computer Engineering Modeling Electronics and Systems University of Calabria Rende Italy CNR-NANOTEC National Research Council Rende Italy

ISBN: (纸本)9789819769919

Breast cancer is the most common type of cancer in women worldwide. In Italy, there were 59,700 new diagnoses in 2023. Early diagnosis is crucial for fighting cancer and reducing the mortality rate. Ultrasound imaging is a common technique used in the early diagnosis of breast cancer. The paper provides the radiologist with the mask of the tumor mass mask along with its classification to make the diagnosis more effective. The proposal examines different deep learning models for image classification and uses the selected best model to perform segmentation in order to create an image mask to identify the tumor mass. The BUSI dataset is one of the few publicly available datasets. Experimental studies have shown that classification models can successfully classify breast ultrasound images that segmentation models can produce a good mask. The best-performing model outperforms other models with an accuracy of over 90%, a precision of 92%, a recall of 90%, and an F1-score of 90%. This study shows that deep learning architectures are effective in classifying and segmenting ultrasound breast images and could be used in clinical experiments in the near future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ultrasonic imaging

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Indoor Positioning Error Analysis Using a Cooperative Multi-Technology Simultaneous Localization and Signal Mapping in a Vehicular Environment

Indoor Positioning Error Analysis Using a Cooperative Multi-...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Luigi D’Alfonso Mauro Tropea Giuseppe Fedele Floriano De Rango Department of Computer Engineering Modeling Electronics and Systems (DIMES) University of Calabria Rende Italy

ISBN: (数字)9798350366402

ISBN: (纸本)9798350366419

This paper introduces an error positioning analysis making use of a proposed cooperative multi-technology localization technique that leveraging WiFi, Ultra WideBand (UWB), LIght Detection And Ranging (LIDAR) signals and an Extended Kalman Filter (EKF) is able to guarantee a more precise positioning estimation and a signal map reconstruction. The analysis of the positioning error aims to show how the quality of the used sensors can affect the values estimated by the filter. In particular, we have considered three different classes of WiFi receiver sensitivity for evaluating how the receiver quality can affect the error positioning estimation. Moreover, we have also varied the number of WiFi emitters giving an indication of the goodness of the proposed approach in respect with other techniques. The numerical simulations are carried out considering a system in which sensors with different quality of sensing are taken into account. The results obtained have shown the greater robustness of the proposed strategy compared to others based on multilateration, especially when dealing with low-quality sensors, that is receiver with bad sensitivity, and with different number of WiFi emitters inside the considered area. It emerges that the adopted approach leads to acceptable results even in the presence of unreliable sensor measurements.

关键词： Location awareness Sensitivity Laser radar Estimation Receivers Numerical simulation Robustness Sensors Kalman filters Wireless fidelity

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A focused review of ANN-based models for Predicting Absorption Maxima (λmax) of Dyes

A focused review of ANN-based models for Predicting Absorpti...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Rani, Geeta Tomar, Neeraj Dhaka, Vijaypal Singh Surolia, Praveen K. Vocaturo, Eugenio Zumpano, Ester Manipal University Jaipur Department of Computer and Communication Engineering Jaipur India Manipal University Jaipur Department of Chemistry Jaipur India University of Calabria CNR-NANOTEC Dimes CS Rende Italy

ISBN: (纸本)9798350324457

The rapidly increasing demand for energy and the consequent depletion of non-renewable energy sources pose significant challenges. Seeking alternatives, renewable sources like solar cells come into focus. Nevertheless, their limited efficiency hinders practical application and motivates researchers to develop more efficient solar cells. Through an examination of effectiveness, design viability, and fabrication costs, Dye-Sensitized Solar Cells (DSSC) emerge as superior to other photovoltaic solar cells. In particular the dye component is crucial for how well the DSSC works as it absorbs light from the *** paper investigates the topic related to forecasting the absorption maxima (λmax) of dyes and presents an overview of the proposals in the literature that use neural networks to forecast it. In addition, it discusses the main challenges related to this relevant topic, evidencing the need to address these challenges. © 2023 IEEE.

关键词： artificial neural network DSSC energy solar λmax

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