Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques by Mohammed Qader Kheder, Aree Ali Mohammed

“…One alternative solution is vehicular cloud computing (VCC). Additionally, an IoT-aided robotic (IoRT) model has been developed with a modern architecture that integrates IoT sensor nodes and cameras to gather real-time traffic data. …”
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Determination of sulphite in wine samples with the aid of three different analytical techniques by Kingsley Sunday, Patrick Omuku, Collins Odidika, Ebuka Emenike, Steve Eshiemogie, Kingsley Iwuozor

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Measuring countries relative efficiencies in using development assistance: a data envelopment analysis approach by Mohamed F. Sakr, Kamal Samy Selim, Sherin Gamaleldin Taha

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Computer-Aided Diagnosis Techniques for Brain Tumor Segmentation and Classification Using MRI by Gadicha A. B., Kale Prachi V., Dalvi G. D., Mohod M. M., Pakhale S. C., Khan S. M.

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A Methodical Review of Iridology-Based Computer-Aided Organ Status Assessment Techniques by Suja Alphonse, Ramachandran Venkatesan, Theena Jemima Jebaseeli

“…The methodologies, data acquisition source, and volume of data used for both training and testing in the pre-diagnosis of human organs utilizing iris patterns are thoroughly studied. …”
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Efficient Data Storage in DNA Sequences Aided by p-gram Huffman Coding by Kun Tu, Dariusz Puchala

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Trajectory and communication scheduling optimization for the rechargeable UAV aided data collection system by Qianwen LI, Jianfeng CHEN, Miao CUI, Guangchi ZHANG

“…A rechargeable unmanned aerial vehicle (UAV) aided wireless sensor network was considered, which consists of multiple ground terminals with a large amount of time-sensitive data to be collected.Due to the limited battery capacity, the UAV cannot collect the data from all terminals through a single flight mission, and it needs to return to the charging pile to replenish its flight energy several times during the whole mission.The optimization of the terminal scheduling, trajectory, flight speed and transmission rate for the UAV was studied to maximize the number of terminals whose data had been collected within the data lifetime limit.Due to the variable coupling and the existence of discrete binary scheduling variables, the considered optimization problem is difficult to solve.To tackle such a difficulty, an efficient algorithm was proposed based on the stochastic optimization and the feature engineering.Specifically, the flight hover communication protocol was introduced to simplify the UAV flight process.And then a terminal scheduling algorithm was innovatively proposed with the influence factor and the stochastic preference, which extracted the features that affect the service time of the UAV, optimized the weights of the features, and further simplified the optimization problem into multiple subproblems.The subproblems were then solved by using the block coordinate descent and successive convex approximation techniques.Simulation results show that the proposed optimization algorithm achieves significant performance gains over several benchmark schemes in the scenarios with different data lifetime requirements and different numbers of ground terminals.…”
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Effective deep learning aided vehicle classification approach using Seismic Data by Sherief Hashima, Mohamed H. Saad, Ahmad B. Ahmad, Takeshi Tsuji, Hamada Rizk

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Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis by Tao Yan, Jianchun Guo, Yuan Zhou, Lixia Zhu, Bo Fang, Jiawei Xiang

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Machine learning-aided hybrid technique for dynamics of rail transit stations classification: a case study by Ahad Amini Pishro, Shiquan Zhang, Alain L’Hostis, Yuetong Liu, Qixiao Hu, Farzad Hejazi, Maryam Shahpasand, Ali Rahman, Abdelbacet Oueslati, Zhengrui Zhang

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MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects by Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Rossella Donghia, Gianluigi Giannelli, Angela Lombardi, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci

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Application of Computer Aided Design (CAD) and Flat Techniques in Teaching Pattern Drafting by Clothing Lecturers in Universities in South East, Nigeria by Eunice Ifenyinwa Ugwu, Margret Ndidiamaka Ezeaku, Blessing Ijeoma Attah, Udochukwu M Emeghebo, Emmanuel Chekwube Eze

“…The purpose of the study was to investigate the application of computer aided design (CAD) and Flat techniques in teaching pattern drafting by clothing lecturers in Universities in South East, Nigeria. …”
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Computer-Aided Drug Design in Research on Chinese Materia Medica: Methods, Applications, Advantages, and Challenges by Ban Chen, Shuangshuang Liu, Huiyin Xia, Xican Li, Yingqing Zhang

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Computational hybrid analysis of drug diffusion in three-dimensional domain with the aid of mass transfer and machine learning techniques by Mohammed Alqarni, Ali Alqarni

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Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy by Abdul Mizwar A Rahim, Ahmad Ridwan, Bambang Pilu Hartato, Firman Asharudin

“…This study aims to enhance the performance of AIDS infection prediction models by integrating feature selection, data balancing, and machine learning classification techniques. …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…The framework can be divided into four major processes: Network big data collection, intelligent evaluation of network big data, AIGC image fine-tuning model construction, and AI-aided design generation. 1) Network big data collection: Obtain datasets related to the required landscape architecture segmentation scenarios through online social platforms for evaluation and fine-tuning model training. 2) Intelligent evaluation of network big data: Analyze and categorize image data, and filter out the scenario images with excellent user evaluation based on text sentiment evaluation and subsidiary information analysis. 3) AIGC image fine-tuning model construction: Utilize the high-quality image dataset obtained in the previous stage to conduct fine-tuning model training based on a mature pre-trained general model, and inject relevant knowledge and experience from the sub-scenarios of landscape architecture in a cost-effective manner, thereby enhancing the model’s generative capabilities. 4) AI-aided design generation: Employ the fine-tuning model obtained through training to assist in generating scenario images according to the needs of design practice, and based on the intensity of control over the generated content, divide the aided scenario generation into “weakly controlled” and “strongly controlled” aided design scenarios. …”
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Optimising ensemble streamflow predictions with bias correction and data assimilation techniques by M. Tanguy, M. Tanguy, M. Eastman, M. Eastman, A. Chevuturi, E. Magee, E. Cooper, R. H. B. Johnson, K. Facer-Childs, J. Hannaford, J. Hannaford

“…<p>This study evaluates the efficacy of bias correction (BC) and data assimilation (DA) techniques in refining hydrological model predictions. …”
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Analysis of core abilities and supporting techniques in joint all-domain command and control by SHAN Hongchang, WU Lin

“…In order to systematize these core abilities in JADC2, the U.S military develops supporting techniques such as advanced system structure, operation cloud, military internet of things, military 5G communication, big data and supporting systems from the U.S military multiservice. …”
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Efficacy of Bracket Adhesive Remnant Removal by a Fluorescence-Aided Identification Technique with a UV Light Handpiece: In Vitro Study by Sabrina Elise Moecke, Pedro Caio Alves Barros, Adrielle Caroline Moreira Andrade, Alessandra Bühler Borges, César Rogério Pucci, Carlos Rocha Gomes Torres

“…This study aims to analyze the fluorescence-aided identification technique efficacy on adhesive remnant removal from the enamel surface after orthodontic bracket debonding. …”
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Data Mining–Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach by Mohamed Eldawansy, Hazem El Bakry, Samaa M Shohieb

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