Article
Agricultural Engineering
Chao-Tung Yang, Endah Kristiani, Yoong Kit Leong, Jo-Shu Chang
Summary: This paper examines and summarizes the literature related to artificial intelligence (AI) in the bioprocessing field, aiming to explore the potential of machine learning algorithms in revolutionizing bioengineering. By employing natural language processing (NLP), papers from 2013 to 2022 with specific keywords of bioprocessing using AI were collected and analyzed. The results show that in the past five years, 50% of the publications focused on topics such as hybrid models, artificial neural networks (ANN), biopharmaceutical manufacturing, and biorefinery. The summarization and analysis indicate that implementing AI can improve the design and process engineering strategies in bioprocessing fields.
BIORESOURCE TECHNOLOGY
(2023)
Article
Neurosciences
Honglin Xiong, Hongmin Chen, Li Xu, Hong Liu, Lumin Fan, Qifeng Tang, Hsunfang Cho
Summary: Artificial intelligence based on data elements is widely used in healthcare informatics, particularly in health big data. The generation and collection of large quantities of clinical data from electronic medical records and wearable technologies have become easier and faster. With the help of AI technologies and open-source big data platforms, the cost of acquiring and processing health big data can be significantly reduced. This presents new opportunities for discovering relationships among living habits, sports, inheritances, diseases, symptoms, and drugs. Machine learning methods have been extensively applied in health big data, and recent progress has been made in automatic diagnosis.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Engineering, Electrical & Electronic
John Edwards
Summary: Researchers in various fields are increasingly using artificial intelligence and machine learning to develop tools and systems that can predict, adapt, and optimize system performance, helping them achieve breakthroughs in different areas.
IEEE SIGNAL PROCESSING MAGAZINE
(2021)
Review
Medicine, General & Internal
Jimmy S. Chen, Sally L. Baxter
Summary: Advances in technology have led to increased data availability in ophthalmology, but current applications of artificial intelligence (AI) in the field mainly focus on image-based deep learning. However, there is a vast amount of underutilized text data in electronic health records (EHR). Natural language processing (NLP), a type of AI, can be used to develop automated algorithms for analyzing and utilizing this text data. This review summarizes current applications of NLP in ophthalmology and explores potential future applications.
FRONTIERS IN MEDICINE
(2022)
Review
Surgery
Tyler J. Loftus, Maria S. Altieri, Jeremy A. Balch, Kenneth L. Abbott, Jeff Choi, Jayson S. Marwaha, Daniel A. Hashimoto, Gabriel A. Brat, Yannis Raftopoulos, Heather L. Evans, Gretchen P. Jackson, Danielle S. Walsh, Christopher J. Tignanelli
Summary: This study summarizes the state-of-the-art artificial intelligence-enabled decision support in surgery and quantifies deficiencies in scientific rigor and reporting. The results show that these models are limited by reliance on internal validation, small sample sizes, and failure to report confidence intervals and clinical implementation.
Article
Radiology, Nuclear Medicine & Medical Imaging
Christian J. Park, Paul H. Yi, Hussain Al Yousif, Kenneth C. Wang
Summary: The purpose of this study was to evaluate the feasibility of using Google Translate to translate the RadLex lexicon from English to German and vice versa. The study found that the concordance rate for translations by Google Translate was 5.4% for English to German and 6.9% for German to English. Human review of the translations showed that most of the non-concordant translations were understandable. Combining string matching and human review improved the overall success rate of Google Translate.
JOURNAL OF DIGITAL IMAGING
(2022)
Article
Computer Science, Artificial Intelligence
Mosleh Hmoud Al-Adhaileh, Theyazn H. H. Aldhyani
Summary: This study examines the use of artificial intelligence and neural networks to predict crop yields in Saudi Arabia. The findings indicate that temperature, insecticides, and rainfall have significant impacts on crop yields, and these parameters contribute similarly to the accuracy of the predictive model.
PEERJ COMPUTER SCIENCE
(2022)
Article
Engineering, Multidisciplinary
Rezoanul Hafiz Chandan, Nusrat Sharmin, Muhaimin Bin Munir, Abdur Razzak, Tanvir Ahamad Naim, Tasneem Mubashshira, Mokhlesur Rahman
Summary: The evaluation system of small arms firing plays an important role in the military. This paper introduces an AI-based system that can automatically evaluate shooting standards and improve accuracy and precision.
DEFENCE TECHNOLOGY
(2023)
Review
Biochemistry & Molecular Biology
Yunbi Xu, Xingping Zhang, Huihui Li, Hongjian Zheng, Jianan Zhang, Michael S. Olsen, Rajeev K. Varshney, Boddupalli M. Prasanna, Qian Qian
Summary: The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and molecular marker genotypes. However, plant performance is determined by the combined effects of genotype, envirotype, and genotype by environment interaction. Integration of multidimensional information profiles, including spatiotemporal omics, provides predictive breeding with both tremendous opportunities and challenges.
Review
Engineering, Mechanical
Nian Yin, Zhiguo Xing, Ke He, Zhinan Zhang
Summary: Tribology research focuses on friction, wear, and lubrication between interacting surfaces. It has gone through stages of empirical, theoretical, and computational approaches. With the development of information technology, the field of tribology has introduced the concept of tribo-informatics. This paper reviews the application of tribo-informatics methods in tribology research, aiming to provide guidance for efficient and scientific research in this field.
Review
Clinical Neurology
Yuzhe Liu, Yuan Luo, Andrew M. Naidech
Summary: Significant advances in medical data accumulation, computational techniques, and management have been made in the last decade. Big data and computational methods can address gaps in patient selection, complications prediction, and outcome understanding. Automated neuroimaging analysis can help triage patients, and data-intensive techniques enable accurate risk calculations for timely prediction of adverse events.
Review
Medicine, General & Internal
Ljiljana Trtica Majnaric, Frantisek Babic, Shane O'Sullivan, Andreas Holzinger
Summary: Multimorbidity, the coexistence of two or more chronic diseases in a person, presents unique care needs that current healthcare systems struggle to address due to their focus on single diseases. To improve patient care in these cases, a radical change in medical research and treatment approaches is required, with a shift towards interactive research supported by artificial intelligence and big data analytics.
JOURNAL OF CLINICAL MEDICINE
(2021)
Editorial Material
Environmental Studies
Sarah Barns
Summary: This commentary discusses how routine urban behaviors are now being replicated computationally, emphasizing the significant role of big data in the city. The emerging urban systems of learned intelligence are described as both radical and routine, with attention also being drawn to the generative design principles of data-driven models of urban behavior.
Article
Agronomy
Jian Gao, Wenzhi Zeng, Zhipeng Ren, Chang Ao, Guoqing Lei, Thomas Gaiser, Amit Kumar Srivastava
Summary: In this study, an innovative model integrating machine learning and swarm intelligence search algorithms was proposed to overcome the limitations of traditional fertilization decision methods. By applying ML algorithms such as random forest, extreme random tree, and extreme gradient boosting, and coupling them with the cuckoo search algorithm, an optimal fertilization strategy was discovered. The model achieved high yield simulation accuracy and significantly increased the average yields of maize, rice, and soybean crops in the study area.
Article
Agriculture, Multidisciplinary
Stefan Fenz, Thomas Neubauer, Juergen Kurt Friedel, Marie-Luise Wohlmuth
Summary: Crop rotation planning is the process of determining the types and order of plants in agricultural areas to improve soil quality, crop yield, and pest/weed resistance. This study trained a reinforcement learning agent using literature and NDVI measurements to generate realistic and practical crop rotation sequences based on crop-specific attributes.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Green & Sustainable Science & Technology
Ishita Afreen Ahmed, Swapan Talukdar, Mohd Waseem Naikoo, Shahfahad, Ayesha Parvez, Swades Pal, Shakeel Ahmed, Atiqur Rahman, Abu Reza Md Towfiqul Islam, Amir H. Mosavi
Summary: This study aimed to identify the most suitable soil-water conservation areas in Guwahati through a coupling coordination mechanism. Principal component analysis and revised universal soil loss equation were used to determine the suitability models for current and future scenarios. The findings of this study are significant for environmental protection and land-water resource management in urban watersheds.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Interdisciplinary Applications
Yousef Abbaspour-Gilandeh, Mohammadreza Abbaspour-Gilandeh, Hassan A. Babaie, Gholamhossein Shahgoli
Summary: Developing accurate models to predict soil bulk density is important due to the difficulty in determining it. In this study, we conducted experiments to determine the factors affecting soil bulk density in three different soil textures. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict and model soil bulk density using independent parameters such as cone index, moisture content, and electrical conductivity. The ANN model developed using the Bayesian tuning algorithm with an R-2 value of 0.93 was found to be the most suitable model. The ANFIS model also showed high accuracy with a coefficient of determination of 0.988.
EARTH SCIENCE INFORMATICS
(2023)
Article
Forestry
Hengameh Mirhashemi, Mehdi Heydari, Omid Karami, Kourosh Ahmadi, Amir Mosavi
Summary: The present study models the effect of climate change on the distribution of Persian oak (Quercus brantii Lindl.) in the Zagros forests of Iran. Using the machine learning method of Bayesian additive regression tree (BART), the study finds that the suitable habitat of Persian oak will decrease by 75.06% by 2070 under both climate change scenarios. This study provides insights into the current condition and future projections of the local forests for proper management and protection of endangered ecosystems.
Article
Forestry
Saeideh Karimi, Mehdi Heydari, Javad Mirzaei, Omid Karami, Brandon Heung, Amir Mosavi
Summary: Wildfire has a significant impact on plant phenology and can be monitored using time series satellite data to identify the growing season. This study investigated the use of remote sensing data and land surface phenology parameters to evaluate the impacts of fire in semi-arid oak forests of Iran. The results showed that the fire had a negative effect on land surface phenology, but there were signs of forest restoration after two years.
Article
Geosciences, Multidisciplinary
Saeid Janizadeh, Sayed M. Bateni, Changhyun Jun, Jungho Im, Hao-Thing Pai, Shahab S. Band, Amir Mosavi
Summary: In this study, various models including the generalized linear model (GLM) and four ensemble methods were used to predict forest fire hazard in Chalus Rood watershed, Iran. Data from 108 historical forest fire events were collected and used for analysis. The models were trained and tested using 70% and 30% of the data, respectively. 14 environmental, climatic, and vegetation variables were used as inputs, and the efficiency of the models was evaluated using ROC curve parameters. Results showed that the ensemble methods improved the performance of the GLM model, with the Bayesian algorithm being the most efficient.
GEOMATICS NATURAL HAZARDS & RISK
(2023)
Article
Environmental Sciences
Akram Seifi, Soudabeh Golestani Kermani, Amir Mosavi, Fatemeh Soroush
Summary: The main objective of this study was to investigate the impact of input parameter uncertainty on the output of the WinSRFR model. The generalized likelihood uncertainty estimation (GLUE) framework was used to evaluate the model uncertainty. The results showed that parameter uncertainty had a significant influence on the model outputs, especially for soil infiltration and roughness coefficients. It is recommended to use accurate field methods and equipment, as well as proper measurements of soil infiltration.
Article
Green & Sustainable Science & Technology
Mehdi Hosseinzadeh, Mazhar Hussain Malik, Masoumeh Safkhani, Nasour Bagheri, Quynh Hoang Le, Lilia Tightiz, Amir H. Mosavi
Summary: An authentication protocol for secure data transmission in an IoT subsystem is proposed, but it has non-ideal security properties, vulnerability to insider attackers, and lacks perfect forward secrecy. The protocol is redesigned to withstand these attacks with only a 15.5% increase in computational cost.
Article
Engineering, Chemical
Mohammad Kaveh, Malgorzata Nowacka, Esmail Khalife, Kamal Imanian, Yousef Abbaspour-Gilandeh, Maryam Sabouri, Safoura Zadhossein
Summary: This study aimed to examine the effect of ultrasonic (US) pretreatment and microwave-hot air drying (MW-HA) on the drying time, specific energy, qualitative properties, and bioactive compound properties of hawthorn fruit. The results showed that the use of US and MW-HA air drying reduced the drying time and obtained high-quality dried products. However, compared to fresh samples, the antioxidant activity, total phenolic content, and total flavonoid content were decreased with the use of US and MW-HA air drying.
Article
Food Science & Technology
Fatemeh Joudi-Sarighayeh, Yousef Abbaspour-Gilandeh, Mohammad Kaveh, Mariusz Szymanek, Ryszard Kulig
Summary: In this research, a convective/infrared (CV/IR) dryer was used to dry pumpkin slices. The influence of air temperature, air velocity, and IR power on the drying process were assessed. The optimal drying conditions were determined to be a temperature of 70 degrees C, air velocity of 0.69 m/s, and IR power of 750 W.
Article
Environmental Sciences
Elika Safaie Ghamsary, Mehrdad Karimimoshaver, Armin Akhavan, Zahra Afzali Goruh, Farshid Aram, Amir Mosavi
Summary: This study compared land use and accessibility as two main factors affecting the selection of appropriate locations for pocket parks. The results indicated that the effects of location greatly outweighed the effects of accessibility. It was also found that different types of commercial land use were more closely associated with people's attendance. These findings will assist urban planners and authorities in making better decisions regarding pocket park locations.
ENVIRONMENTAL AND SUSTAINABILITY INDICATORS
(2023)
Article
Engineering, Chemical
Mohammad Kaveh, Necati Cetin, Esmail Khalife, Yousef Abbaspour-Gilandeh, Maryam Sabouri, Faroogh Sharifian
Summary: This study used machine learning approaches to estimate moisture content and moisture ratio of apricot in different dryers, and calculated specific energy consumption and effective moisture diffusivity. The results showed that the hybrid dryer achieved the best drying performance and lowest energy consumption, while the convective dryer performed the worst under suboptimal conditions. The RF technique showed excellent correlation in moisture content estimation, while the MLP had high accuracy in moisture ratio estimation and drying method discrimination.
JOURNAL OF FOOD PROCESS ENGINEERING
(2023)
Article
Computer Science, Information Systems
Muhammad Sheeraz, Muhammad Arsalan Paracha, Mansoor Ul Haque, Muhammad Hanif Durad, Syed Muhammad Mohsin, Shahab S. Band, Amir Mosavi
Summary: The internet's advancements and benefits have made it essential for organizations, but security threats are on the rise. Monitoring security and utilizing SIEM and SOAR systems as part of a SOC are crucial for organizations to protect their IT infrastructure and make informed decisions.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Muhammad Sajid Farooq, Sagheer Abbas, Kiran Sultan, Muhammad Adnan Atta-ur-Rahman, Muhammad Adnan Khan, Amir Mosavi
Summary: The growth in data generation and use of computer network devices has enhanced internet infrastructure, leading to complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems are crucial in monitoring network traffic for malicious activities. In this study, a fused machine learning technique is proposed for detecting intrusion in heterogeneous networks and protecting against malicious attacks, achieving a validation accuracy of 95.18% and a miss rate of 4.82% in intrusion detection.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Information Systems
Muhammad Nadeem, Ali Arshad, Saman Riaz, Syeda Wajiha Zahra, Shahab S. Band, Amir Mosavi
Summary: Many organizations focus on protecting cloud servers from external attacks, but the majority of risks come from internal sources. While there are algorithms in place to safeguard against attacks, hackers constantly find ways to bypass these security measures. Cloud cryptography provides the best data protection algorithm for secure data exchange between authentic users.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Information Systems
Muhammad Nadeem, Ali Arshad, Saman Riaz, SyedaWajiha Zahra, Muhammad Rashid, Shahab S. Band, Amir Mosavi
Summary: Cloud computing is an attractive and cost-saving model that offers online services to end-users, allowing them to access data from any node. However, cloud security is a major concern due to various malware attacks from internal and external sources. This paper proposes a tool that uses Cloudflare and K-nearest neighbors (KNN) classification to prevent spamming attacks on cloud servers. Cloudflare blocks attacker's IP addresses, while KNN classifiers identify the location of spammers. The article also discusses various prevention techniques, compares with other studies, and draws conclusions based on different results.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
