A Novel Deep Hybrid Model for Automatic Femoral Stem Classification in Hip Arthroplasty From Radiographs: MSFT-Net With CBAM and Transformer Modules by Emre Gogus, Atinc Yilmaz, Meric Enercan

“…In cases where prior implant data are unavailable, manual identification is often required, posing significant challenges due to its time-consuming and error-prone nature. To solve this problem, a novel hybrid deep learning architecture that includes a convolutional block attention module and a swin transformer with multi-scale feature fusion from pre-trained architectures DenseNet201, VGG19, and InceptionV3 under the transfer learning paradigm was proposed in this study. …”
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PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image by Khaled Eabne Delowar, Mohammed Borhan Uddin, Md Khaliluzzaman, Riadul Islam Rabbi, Md Jakir Hossen, M. Moazzam Hossen

“…However, the manual examination for identifying the type of polyps can be time-consuming, tedious, and prone to human error. Automatic classification of polyps through colonoscopy images can be more efficient. …”
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A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases by G. Sambasivam, G. Prabu kanna, Munesh Singh Chauhan, Prem Raja, Yogesh Kumar

“…Traditional methods to diagnose these diseases are time-consuming, prone to error, and require expert knowledge, making automated solutions highly preferred. …”
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Evaluation of Natural Image Generation and Reconstruction Capabilities Based on the β-VAE Model by Zhang Honghao

“…Train the model on the CelebA dataset, using three metrics: Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID) for matrix evaluation, to analyze the generation quality and reconstruction capability of each model. …”
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An Improved Methodology to Locate Faults in Onshore Wind Farm Collector Systems by Moisés Davi, Alailton Júnior, Caio Grilo, Talita Cunha, Leonardo Lessa, Mário Oleskovicz, Denis Coury

“…The proposed methodology, which combines the various fault location methods tailored to specific fault types, results in a substantial improvement, achieving an average fault location error of 1.89%, reflecting a 92% reduction in error compared to conventional methods. …”
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Field Obstacle Detection and Location Method Based on Binocular Vision by Yuanyuan Zhang, Kunpeng Tian, Jicheng Huang, Zhenlong Wang, Bin Zhang, Qing Xie

“…In localization accuracy tests, the maximum average error and relative error in the 2–10 m range for the distance between the camera and five types of obstacles were 0.16 m and 2.26%. …”
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Adaptive quadrilateral distance relaying scheme for fault impedance compensation by Patel Ujjaval J., Chothani Nilesh G., Bhatt Praghnesh J.

“…Impedance reach of numerical distance relay is severely affected by Fault Resistance (RF), Fault Inception Angle (FIA), Fault Type (FT), Fault Location (FL), Power Flow Angle (PFA) and series compensation in transmission line. …”
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Conditional cash transfer for safe delivery in India: utilisation and inequalities with reference to NFHS-5 by Subhajeet Singh Sardar, Barun Kumar Majee, Mridul Mandal, Pralay Kundu, Manavendra Mondal, Rambha Kumari, Suman Paul, Subhasis Bhattacharya

“…Abstract Background The implementation of JSY in India completed nearly 25 years since from its inception. Studies on beneficiary of the scheme based on various nationally provided data set indicates its persistence level over socio-economic inequality. …”
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Bone Age Estimation of Chinese Han Adolescents’s and Children’s Elbow Joint X-rays Based on Multiple Deep Convolutional Neural Network Models by LI Dan-yang, ZHOU Hui-ming, WAN Lei, LIU Tai-ang, LI Yuan-zhe, WANG Mao-wen, WANG Ya-hui

“…For regression, VGG16, VGG19, InceptionV2, InceptionV3, ResNet34, ResNet50, ResNet101 and DenseNet121 models were selected for bone age estimation. …”
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Image Inpainting of Portraits Artwork Design and Implementation by Zhang Hongting

“…In the testing phase, two widely used metrics in image evaluation, Mean Squared Error (MSE) and Fréchet Inception Distance (FID), are introduced to assess the performance. …”
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Advanced deep transfer learning techniques for efficient detection of cotton plant diseases by Prashant Johri, SeongKi Kim, Kumud Dixit, Prakhar Sharma, Barkha Kakkar, Yogesh Kumar, Jana Shafi, Muhammad Fazal Ijaz

“…Hence, the significance of this work lies in its potential to mitigate the impact of these diseases, which cause significant damage to the cotton and decrease fibre quality and promote sustainable agricultural practices.MethodsThis paper investigates the role of deep transfer learning techniques such as EfficientNet models, Xception, ResNet models, Inception, VGG, DenseNet, MobileNet, and InceptionResNet for cotton plant disease detection. …”
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Applying transfer learning in CNN model architectures for detecting tomato leaf disease with explainable artificial intelligence by Alexander Takele Mengesha, Melaku Alelign Mengistie

“…Traditional manual inspection is error-prone and impracti cal for large-scale farming. …”
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Blood cancer prediction model based on deep learning technique by Amr I. Shehta, Mona Nasr, Alaa El Din M. El Ghazali

“…This paper focuses on improving blood cancer diagnosis using advanced deep learning techniques like ResNetRS50, RegNetX016, AlexNet, Convnext, EfficientNet, Inception_V3, Xception, and VGG19. Among the models assessed, ResNetRS50 had better accuracy and speed with minimal error rates compared with other state-of-the-arts. …”
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FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model by Yunxiang Li, Hua-Chieh Shao, Xiaoxue Qian, You Zhang

“…The evaluation metrics included Fréchet inception distance (FID), mean absolute error, mean squared error, structural similarity index measure, and Dice similarity coefficient (DICE). …”
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A Data-Driven-Based High Impedance Fault Location Method Considering Traveling Waves in Branched Distribution Networks by Eren Baharozu, Suat Ilhan, Gurkan Soykan

“…The results show that the proposed method is promising, with a high accuracy for determining faulty section and a low error ratio for fault distance calculations.…”
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UNIFIED MULTIMODAL BIOMETRICS FUSION USING DEEP LEARNING FOR SECURING IOT by Prabhjot Kaur, Chander Kaur

“…Our experimental evaluation employs performance metrics like accuracy, “Equal Error Rate”, and “Receiver Operating Characteristic” curves. …”
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Efficacy of Adjuvant Yoga Therapy for Cognition in Schizophrenia: A Systematic Review and Meta-analysis of Randomized Controlled Trials by Daiveek G. Pattanashetty, Shivarama Varambally, Hemant Bhargav

“…Conclusion: The results of the meta-analysis should be interpreted with caution owing to the low power, small sample size, methodological limitations in the included studies, and hence high likelihood of type II error. There is a need for more studies with rigorous methodology involving yoga for cognition in schizophrenia.…”
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Deep learning and wavelet packet transform for fault diagnosis in double circuit transmission lines by Ziad M. Ali, Ehab M. Esmail

“…Simulation results across diverse fault scenarios, varying in location, resistance, and inception angle, demonstrate high accuracy and robustness. …”
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Comparative Model Efficiency Analysis Based on Dissimilar Algorithms for Image Learning and Correction as a Means of Fault-Finding by Joe Benganga, Tshepo Kukuni, Ben Kotze, Lepekola Lenkoe

“…As a result of the opportunities that artificial intelligence presents to different sectors by optimally performing tasks with less error compared to humans or traditional models, the use of AI in artefact detection is being investigated. …”
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Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images by Amit Kumar Chanchal, Shyam Lal, Shilpa Suresh

“…This process is time-consuming, prone to human error, and highly depends on the expertise of a pathologist. …”
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