THE FACTS AND MIRAGE OF INTEGRATING ARTIFICIAL INTELLIGENCE INTO THE JUDICIAL SYSTEM OF NIGERIA by DR ENYINNAYA N. OBOH

“…It is manifest that the integration of AI in the complex Nigeria judicial system is feasible with identifiable benefits ranging from easy and quick access to justice, accurate legal research, efficiency and accuracy in legal processes and reduction of backlog cases while creating ample time for legal professionals to handle more strategic legal tasks. …”
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A New Hard Problem for Post-Quantum Cryptography: Q-Problem Primitives by Mostefa Kara, Mohammad Hammoudeh, Sultan Alamri

“…This shift is motivated by the structural vulnerabilities that quantum algorithms may exploit in traditional formulations. …”
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Chaotic Adaptive Butterfly Mating Optimization and Its Applications in Synthesis and Structure Optimization of Antenna Arrays by Bin Li, Chao Liu, Huaning Wu, Yifeng Zhao, Yinghui Dong

“…The proposed algorithm is then used to understand any linear array problems in terms of the sidelobe reduction. …”
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Diagnostics and Prognostics of Boilers in Power Plant Based on Data-Driven and Machine Learning by Achmad Widodo, Toni Prahasto, Mochamad Soleh, Herry Nugraha

“…The proposed method utilizes machine learning techniques through support vector machine (SVM) and random forest algorithm (RFA) for anomaly detection and similarity-based method of dynamic time warping (DTW) for RUL prediction. …”
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COMBINED ANALYSIS OF LINKAGE AND WHOLE EXOME SEQUENCING REVEALS CIC AS A CANDIDATE GENE FOR ISOLATED DYSTONIA by Barış Salman, Emrah Yücesan, Bedia Samancı, Başar Bilgiç, Haşmet Ayhan Hanağası, İbrahim Hakan Gürvit, Uğur Özbek, Sibel Aylin Uğur İşeri

“…An in-house pipeline compiled for WES analysis along with in-depth in silico prediction algorithms were used to assess the associated data produced in this study. …”
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“VisualFields Easy”: an iPad Application as a Simple Tool for Detecting Visual Field Defects by Angelane S. Santos, MD, Evelyn S. Morabe, MD

“…Purpose/Objective: This study aims to determine the reliability of the “VisualFields Easy” application in detecting visual field loss among ophthalmology patients; and to determine the sensitivity, specificity, positive predictive and negative predictive values of this examination using the Humphrey Visual Field Analyzer as the gold standard. …”
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Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…DT produced moderately reliable results, with an accuracy of 90.58%, while XGBoost was the least effective, achieving just 75.42% accuracy and failing to predict high-risk subsidence zones accurately.Susceptibility mapping: The RF algorithm highlighted central and eastern parts of Isfahan–Borkhar as the most vulnerable to subsidence, driven primarily by excessive groundwater withdrawal and geological factors. …”
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A Framework for High-Spatiotemporal-Resolution Soil Moisture Retrieval in China Using Multi-Source Remote Sensing Data by Zhuangzhuang Feng, Xingming Zheng, Xiaofeng Li, Chunmei Wang, Jinfeng Song, Lei Li, Tianhao Guo, Jia Zheng

“…This indicates that multi-source data can provide complementary information for SM monitoring. (2) Compared to XGBoost and LSTM, RFR and EL demonstrate superior overall performance and are the preferred models for SM prediction. Their R² for the training and test sets exceed 0.969 and 0.743, respectively, and their ubRMSE are below 0.022 and 0.063 m³/m³, respectively. (3) The SM prediction accuracy is highest for the scenario of optical + SAR + auxiliary data, followed by SAR + auxiliary data, and finally optical + auxiliary data. (4) With an increasing Normalized Difference Vegetation Index (NDVI) and SM values, the trained models exhibit a general decrease in prediction performance and accuracy. (5) In 2021 and 2022, without considering cloud cover, the IF theoretically achieved an SM revisit time of 1–3 days across 95.01% and 96.53% of China’s area, respectively. …”
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Shared gene signatures and molecular mechanisms link ankylosing spondylitis and rheumatoid arthritis by Boli Qin, Xiaopeng Qin, Jie Ma, Chenxing Zhou, Tianyou Chen, Jichong Zhu, Chengqian Huang, Shaofeng Wu, Rongqing He, Songze Wu, Sitan Feng, Jiarui Chen, Jiang Xue, Wendi Wei, Tengxiang Long, Quan Pan, Kechang He, Zhendong Qin, Tiejun Zhou, Jiayan Jiang, Xinli Zhan, Chong Liu

“…The CBC data of 23,289 patients were collected, and six machine learning algorithms were applied to develop disease prediction models for AS and RA. …”
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Digital twin-driven method for determining wind force coefficients of a bridge deck section by Hao-Yang Li, You-Lin Xu, Hai-Li Liao, Lin Huang, Qi Wang

“…Given that limitations and uncertainties exist in both methods, this study proposes a digital twin-driven method for providing more accurate predictions of wind force coefficients of a streamlined bridge deck. …”
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Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network by Azizollah Khormali, Soroush Ahmadi, Aleksandr Nikolaevich Aleksandrov

“…A comparison of the predicted values of rock permeability under scaling conditions with experimental data showed that the proposed ANN model makes it possible to predict formation damage due to scaling with high accuracy. …”
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Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter by Amira Wali Omer, Taha Hussein Ali

“…In this study, proposes a Hampel filter-modified algorithm to dynamically detect and mitigate extreme values, enhancing parameter estimation and predictive performance. …”
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Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects by Jingyi Liu, Jun Sun, Yasong Wang, Xin Liu, Yingjie Zhang, Haijun Fu

“…In the future, efforts should be made to enhance the implementation of non-destructive testing technology in the fruit industry through technology integration, optimization algorithm, cost reduction, and expansion of industrial chain application, so as to help the premium growth of the fruit industry.…”
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Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease by Daryll Philip, Rebecca Hodgkiss, Swarnima Kollampallath Radhakrishnan, Akshat Sinha, Animesh Acharjee

“…These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. …”
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Optimising test intervals for individuals with type 2 diabetes: A machine learning approach. by Sasja Maria Pedersen, Nicolai Damslund, Trine Kjær, Kim Rose Olsen

“…A training set comprising 80% of the data is used to predict optimal test intervals, with 20% reserved for testing. …”
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A Multi-Modal Attentive Framework That Can Interpret Text (MMAT) by Vijay Kumari, Sarthak Gupta, Yashvardhan Sharma, Lavika Goel

“…Deep learning algorithms have demonstrated exceptional performance on various computer vision and natural language processing tasks. …”
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Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions by Giovanna Nicora, Samuele Pe, Gabriele Santangelo, Lucia Billeci, Irene Giovanna Aprile, Marco Germanotta, Riccardo Bellazzi, Enea Parimbelli, Silvana Quaglini

“…ML is reported as highly effective in predicting movement intentions, assessing clinical outcomes, and detecting compensatory movements, providing insights into the future of personalized rehabilitation interventions. …”
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Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas by Safiah Almarri, Hur Al Safwan, Shahd Al Qisoom, Soufien Gdaim, Abdelkrim Zitouni

“…The system also integrates artificial intelligence (AI) algorithms (multiclass logistic regression) to process sensor data and predict fire risk levels with 99.97% accuracy, enabling proactive wildfire mitigation. …”
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Assessing CO2 separation performances of IL/ZIF-8 composites using molecular features of ILs by Hasan Can Gulbalkan, Alper Uzun, Seda Keskin

“…The accurate prediction power of these ML models was shown by comparing their estimates with the experimental and simulation data. …”
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An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes by Y. Yu, S. Bai, J. Wang, K. Tang, S. Bao, L. Chen, R. Chen

“…An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. …”
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