Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

“…The data was gathered from the PlantVillage dataset and the pre-processing was conducted based on image resizing, color enhancement, and data augmentation. These outputs were then processed in the segmentation stage where the U-Net architecture was applied. …”
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Correlation between Post-Thaw Spermatozoa Quality of the Endangered Javan Banteng with OPN Gene Expression by Vincentia Trisna Yoelinda, Raden Iis Arifiantini, Dedy Duryadi Solihin, Muhammad Agil, Dedi Rahmat Setiadi, Tulus Maulana, Bambang Purwantara, Yohana Tri Hastuti, Jansen Manansang, Dondin Sajuthi

“…However, the male Javan banteng reproductive capability must be considered as it influences the targeted outputs. Studying the potential biomarker for fertility such as osteopontin gene expression is also expected to help predict male fertility. …”
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Understanding repeated non-attendance in health services: a pilot analysis of administrative data and full study protocol for a national retrospective cohort by Alex McConnachie, Philip Wilson, Ross McQueenie, David A Ellis, Andrea E Williamson

“…Results will be disseminated via a combination of academic outputs (papers, conferences), social media and through collaborative public health/policy fora.…”
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Research on low carbon dispatch technology for wind and solar grid connection in the Gobi Desert and barren land areas by Yanhong Ma, QingQuan Lv, Jianmei Zhang, Chunxiang Yang, Qiang Zhou

“…Simulation tests indicate that the proposed framework effectively diminishes wind and solar curtailment rates, bolsters the grid’s consumption capability for wind and solar power in the Gobi Desert and barren regions, and decreases both operational expenses and carbon outputs. Moreover, this study delves into the influence of carbon pricing on metrics such as wind and solar consumption rates, overall system costs, daily carbon emissions, and daily thermal power generation. …”
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Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning by Mithila Akter Mim, M. R. Khatun, Muhammad Minoar Hossain, Wahidur Rahman, Arslan Munir

“…Additionally, to interpret the model outputs and explore influencing factors, we used explainable AI (XAI) techniques like SHAP and LIME. …”
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The Burden of HIV Infection among Pregnant Women Attending Antenatal Care in Jimma University Specialized Hospital in Ethiopia: A Retrospective Observational Study by Taye Kebede, Michael Dayu, Abiot Girma

“…A chi-square test was employed to assess an association between variables. Analyses outputs were summarized and presented in tables and figures. …”
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Assessing Seismic Vulnerability and Pollution Risks of Wastewater Treatment Plants by Spyridon K. Golfinopoulos, Ploutarchos N. Kerpelis, Dimitrios E. Alexakis

“…Geographical Information Systems (GISs) were employed to visualize the spatial distribution of the seismic vulnerability of WWTPs. The study outputs include eight (8) maps depicting the spatial distribution of seismic vulnerability, both with and considering soil–water pollution, by calculating the existence of seismic hazards and identifying potentially affected regions. …”
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FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs by Mustafa Tasci, Ayhan Istanbullu, Vedat Tumen, Selahattin Kosunalp

“…The FPGA-QNN framework comes up with 12 accelerators based on multi-layer perceptron (MLP) and LeNet CNN models, each of which is associated with a specific combination of quantization and folding. The outputs from the performance evaluations on Xilinx PYNQ Z1 development board proved the superiority of FPGA-QNN in terms of resource utilization and energy efficiency in comparison to several recent approaches. …”
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Blockchain oracles for decentralized agricultural insurance using trusted IoT data by Manoj T, Krishnamoorthi Makkithaya, Narendra V. G., Vijaya Murari T

“…Presently, agricultural insurance products rely heavily on centralized systems that lack transparency and traceability, leading to suboptimal risk assessment and delays in payouts. To address these concerns the fintech industry has started to embrace a popular decentralized technology called blockchain. …”
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An EFQM-Fuzzy Network Data Envelopment Analysis Model for Efficiency Assessment in Organizations by Alireza Khosravi, Mohammad Fallah, Seyyed Esmaeil Najafi

“…Given that the inputs and outputs of a real system are not always definite and accurate and that some data can only be expressed in vague verbal and subjective terms, the use of fuzzy sets in modeling is inevitable (Ali et al., 2019). …”
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Accurate quantification of the algebraic, multiplicative algebraic, and simultaneous iterative reconstruction techniques in ionosphere rebuilding based on the TIEGCM assessment by Ao Qi, HaiBing Ruan

“…Similar to common practice, electron density distributions from outputs of the International Reference Ionosphere (IRI) model are used as the iterative initial value in CIT applications. …”
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Research on the robustness of convolutional neural networks in image recognition by Dian LIN, Li PAN, Ping YI

“…Convolutional neural network is one of the key technologies in the application of image recognition and processing in artificial intelligence.Its wide application makes researches on its robustness more and more important.Previous researches on robustness of neural networks were too sweeping and most of them focused on adversarial robustness, which causes difficulty in further study in the mechanism of neural network robustness.The related researches of neuroscience were introduced and the concept of visual robustness was put forward.By studying the similarity and difference between neural network models and human visual system, the internal mechanism and faults of neural network robustness were revealed.The researches of neural network robustness in recent years were reviewed, and the reasons for the lack of robustness of neural network models were analyzed.The lack of robustness of neural networks is reflected in their sensitivity to small perturbations.The reason is that neural networks tend to learn high-frequency information for calculation and inference, which is difficult for humans to recognize.High-frequency information is easily affected by perturbations, and eventually causes mistakes of models.Previous researches on robustness mostly focused on mathematical properties of models, and were limited in the natural faults of neural networks.Visual robustness extends the traditional concept of robustness.The traditional concept of robustness measures the discrimination ability of models for distorted image examples.Distorted examples and clean examples can get correct outputs through robust models.Visual robustness measures the consistency between models and humans in discrimination ability.Visual robustness combines the research methods and achievements of neuroscience and psychology with artificial intelligence.The development of neuroscience in the field of vision were reviewed, and the application of research methods of cognitive psychology in neural network robustness were discussed.Human visual system has advantages in learning and abstract ability, whill neural network models have better performance in calculation speed and memory.The difference between the physiological structure of human brain and the logical structure of neural network models is the key factor leading to the problem of robustness of neural networks.The research of visual robustness requires deeper understanding of human visual system.Revealing the differences in cognitive mechanism between human visual system and neural network models and effectively improving the algorithm are the development trends of neural network robustness and even artificial intelligence.…”
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Improving WRF-Chem PM2.5 predictions by combining data assimilation and deep-learning-based bias correction by Xingxing Ma, Hongnian Liu, Zhen Peng

“…In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM2.5 (particulate matter with an aerodynamic equivalent diameter of ≤ 2.5 μm) predictions. …”
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Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN by Nadikatla Chandrasekhar, Sujatha Canavoy Narahari, Sreedhar Kollem, Samineni Peddakrishna, Archana Penchala, Babji Prasad Chapa

“…However, existing approaches for HD diagnosis suffered from low accuracy and external noise, and most relied on either Electrocardiogram (ECG) or Phonocardiogram (PCG) signals. Different outputs might sometimes be obtained from each signal, creating misclassified outcomes. …”
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A Co-Evolutionary Genetic Algorithm Approach to Optimizing Deep Learning for Brain Tumor Classification by Abdelmgeid A. Ali, Mohamed T. Hammad, Hassan S. Hassan

“…Key obstacles include the need for expert interpretation of deep learning model outputs and the difficulty of developing highly accurate categorization systems. …”
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A copula-based multivariate flood frequency analysis under climate change effects by Marzieh Khajehali, Hamid R. Safavi, Mohammad Reza Nikoo, Mohammad Reza Najafi, Reza Alizadeh-Sh

“…The analysis considers two emission scenarios, including SSP2-4.5 and SSP5-8.5 for far-future (2070–2100), mid-term future (2040–2070), and historical (1982–2012) periods. Downscaled GCM outputs are utilized as predictors of the machine learning model to simulate daily streamflow. …”
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A Framework of Recommendation System for Unmanned Aerial Vehicle Autonomous Maneuver Decision by Qinzhi Hao, Tengyu Jing, Yao Sun, Zhuolin Yang, Jiali Zhang, Jiapeng Wang, Wei Wang

“…We developed an integrated learning recommendation system capable of simulating varied mission scenarios, facilitating the acquisition of optimal strategies from a blend of expert human input and algorithmic outputs. The system supports extensive simulation capabilities, including various control modes (manual, autonomous, and hybrid) and both continuous and discrete maneuver actions. …”
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Learning-induced sensory plasticity of mouse olfactory epithelium by A. V. Romashchenko, Р. Е. Kireeva, M. В. Sharapova, Т. A. Zapara, A. S. Ratushnyak

“…Traditionally, studies of the neurobiology of learning and memory focus on the circuitry that interfaces between sensory inputs and behavioral outputs, such as the amygdala and cerebellum. However, evidence is accumulating that some forms of learning can in fact drive stimulus­specifc changes very early in sensory systems, including not only primary sensory cortices but also precortical structures and even the peripheral sensory organs themselves. …”
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Novel electroretinography devices to detect hydroxychloroquine retinopathy: study protocol for a diagnostic accuracy and feasibility study by James Galloway, Chan Ning Lee, Omar A Mahroo, Timothy L Jackson, George Murphy, Hatem A Wafa

“…Secondary outcomes include the effect of dilation on device outputs, analysis of discriminatory waveforms, device acceptability and recruitment rate. …”
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An assessment of the capacity and responsiveness of a national system to address the threat of invasive species: a systems approach by F. Williams, K. L. Constantine, A. A. Ali, T. W. Karanja, S. Kibet, E. K. Lingeera, G. Muthike, I. Rwomushana, J. Godwin, R. Day

“…Method The first step was to define an invasive species system and its component parts including the functions, expected outputs and contextual factors. A range of indicators and participatory tools were developed to measure system performance. …”
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