Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning by Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman

“…While this approach attains excellent accuracy across varied conditions, it requires more computational time due to its greater number of trainable parameters. …”
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Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems by Wang Jian, Amin Ul Haq, Noman Afzal, Shakir Khan, Hadeel Alsolai, Sultan M. Alanazi, Abu Taha Zamani

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Binary and Multi-Class Classification of Colorectal Polyps Using CRP-ViT: A Comparative Study Between CNNs and QNNs by Jothiraj Selvaraj, Fadhiyah Almutairi, Shabnam M. Aslam, Snekhalatha Umapathy

“…In addition to the key metrics, computational parameters were compared, where CRP_QNN-ViT excelled in computational time. …”
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A Rational Threshold Signature Model and Protocol Based on Different Permissions by Bojun Wang, Cheng Cai, Quan Zhou

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Investigation of Inlet Guide Vane Efficiency in Centrifugal Compressors for the Turbo Refrigeration Machines by Danilishin A.M., Kozhukhov Y. V., Fateeva E.S., Aksenov A.A.

“…The article investigates the influence of geometric parameters of inlet guide vanes (IGVs), including blade profiling and design features, on their operational efficiency. …”
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Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer Learning by Keyu Yan, Wenming Zheng, Tong Zhang, Yuan Zong, Chuangao Tang, Cheng Lu, Zhen Cui

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UAV Target Segmentation Based on Depse Unet++ Modeling by Zhaoqi Hou, Yiqing Gu, Zhen Zheng, Yueqiang Li, Haojie Li

“…By introducing Squeeze-and-Excitation, the model’s ability to discriminate camouflaged targets in high-similarity backgrounds is improved; by incorporating a depth-separated convolutional design, the parameters and computational requirements for embedded device applications are significantly reduced; and employing Dropout technique to prevent overfitting with limited sample sizes, thus boosting the model’s adaptability and generalization across environments. …”
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An Overview on RAM Memories in QCA Technology by Javad Chaharlang, Mohammad Mosleh

“…Nowadays, this technology is a good alternative for CMOS technology due to features such as high speed, low occupied area and low power consumption. …”
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Multi task detection method for operating status of belt conveyor based on DR-YOLOM by Yongan LI, Tengjie CHEN, Hongwei WANG, Zhihao ZHANG

“…Secondly, as the model requires target recognition and segmentation of different types of tasks, an efficient layer aggregation network (RepGFPN) with skip layer connection structure is adopted to optimize the feature fusion part, greatly improving the detection accuracy of the model for different detection tasks while controlling the number of model parameters and inference speed; Finally, to address the detection tasks of three different label shapes, the Inner CIOU loss function is introduced to compensate for the weak generalization ability of the CIoU loss function in different detection tasks. …”
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Designing of a Simulation Tool for the Performance Analysis of Hybrid Data Center Networks (DCNs) by Muhib Ahmad, Farrukh Zeeshan Khan, Zeshan Iqbal, Muneer Ahmad, Ihsan Ali, Sultan S. Alshamrani, Muhammad Talha, Muhammad Ahsan Raza

“…Data center (DC) technology changes the mode of computing. Traditional DCs consist of a single layer and only have Ethernet connections among switches. …”
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Improved RT-DETR Framework for Railway Obstacle Detection by Peng Li, Yanhui Peng, Su-Mei Wang, Cheng Zhong

“…Building upon the RT-DETR framework, this study proposes a Multiscale Separable Deformable (MSD) module that integrates depthwise convolution with deformable convolution to enhance feature extraction capabilities while reducing computational load. …”
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Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure by Feng Gu, Andrew J. Meyer, Filip Ježek, Shuangdi Zhang, Tonimarie Catalan, Alexandria Miller, Noah A. Schenk, Victoria E. Sturgess, Domingo Uceda, Rui Li, Emily Wittrup, Xinwei Hua, Brian E. Carlson, Yi-Da Tang, Farhan Raza, Kayvan Najarian, Scott L. Hummel, Daniel A. Beard

“…Unsupervised machine learning applied to digital twin-derived features identified interpretable phenogroups and mechanistic drivers of cardiovascular death risk. …”
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LiteTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories by Wenshuai Liu, Qingzheng Liu, Wenyong Quan, Junli Wang, Xiaomin Yao, Qiang Liu, Yuxiang Tian

“…This model employed RepViT as a lightweight backbone network instead of the original RTDETR backbone, considerably reducing both the number of parameters in and computational complexity of the model. …”
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Audio copy-move forgery detection with decreasing convolutional kernel neural network and spectrogram fusion by Canghong Shi, Xin Qiu, Min Wu, Xianhua Niu, Xiaojie Li, Sani M. Abdullahi

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Security monitoring via sound analysis and voice identification with artificial intelligence by Balabanova Ivelina, Sidorova Kristina, Georgiev Georgi

“…Subsequently, steps were taken to reduce the informative features when searching for similar levels of accuracy in order to limit the necessary computational procedures in neural training, but maintain the threshold of successful user authentication. …”
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A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng, Xiaoyan Chen

“…Experiments on our dataset show that LightSal-RTDETR achieves a mAP@50 of 81%, improving by 1.9% over the original model while reducing parameters by 28.1% and computational cost by 26.5%. …”
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INTEGRATING ACOUSTIC MODULATION AND LISTENER DEMOGRAPHICS FOR ENHANCED PODCAST EMOTIONAL RESONANCE by Jun Ji, Kotchaphan Youngmee, Khachakrit Liamthaisong, Narisara Brikshavana

“…The research contradicts conventional views in previous studies by including an overall framework for the improvement of auditory features in podcasts. The underlying framework combines analytical methods, computational modelling, and machine learning in order to systematically enhance audio quality and consequently increase listener engagement.  …”
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Temperature-Change-Based Thermal Tomography by Yong Xu, Xiangyu Wei, Ge Wang

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Detecting All-to-One Backdoor Attacks in Black-Box DNNs via Differential Robustness to Noise by Hao Fu, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami

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Constructions of Low-Hit-Zone Frequency-Hopping Sequence Set With Strictly Optimal Periodic Partial Hamming Correlation by Chun Xie, Changyuan Wang

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