Cellular Harmony Search for Optimization Problems by Mohammed Azmi Al-Betar, Ahamad Tajudin Khader, Mohammed A. Awadallah, Mahmmoud Hafsaldin Alawan, Belal Zaqaibeh

“…In cHS, the population is arranged as a two-dimensional toroidal grid, where each individual in the grid is a cell and only interacts with its neighbors. The memory consideration and population update are modified according to cellular EA theory. …”
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Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices by Zidane Ikkoy Ramadhan, Harya Widiputra

“…There are many variants of RNN such as RNN itself, long-short-term memory (LSTM), and gated recurring unit, so it is frequently debatable which algorithm from the RNN family has the most optimal efficiency and computation time. …”
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Effects of Drought Frequency on Growth Performance and Transpiration of Young Black Locust (Robinia pseudoacacia L.) by Dario Mantovani, Maik Veste, Dirk Freese

“…We exposed young trees in lysimeters to different cycles of drought. The drought memory affected the plant growth performance and its drought tolerance: the plants resprouting under drought conditions were more drought tolerant than the well-watered ones. …”
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Short-term prediction of trimaran load based on data driven technology by Haoyun Tang, Rui Zhu, Qian Wan, Deyuan Ren

“…In the research, the monitoring data from a trimaran model test is applied for the training and testing of long short-term memory (LSTM) neural network. The impact analysis on the factors such as input length, neuron number, artificial neural network (ANN) optimizer, and output scope, are taken. …”
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Automated vulnerability discovery method for 5G core network protocol by Peixiang WU, Zhilong ZHANG, Libo CHEN, Yijun WANG, Zhi XUE

“…With the widespread development of fifth-generation (5G) mobile communication technology, concerns regarding 5G network security have also increased.Blackbox fuzzing is a commonly used method for automated vulnerability discovery in software security.However, applying dynamic approaches like fuzzing to discover vulnerabilities in the complex design of 5G core network protocols poses challenges such as low efficiency, poor versatility, and lack of scalability.Therefore, a novel static method to examine the open-source solution of the 5G core network was proposed.Through this method, a series of memory leak security issues caused by improper variable life cycle management were identified, which can lead to denial-of-service attacks on the 5G core network.To summarize these weaknesses, a general vulnerability model and an automated vulnerability discovery method called HoI were presented, which utilized hybrid analysis based on control and data flow.By successfully discovering five zero-day bugs in Open5GS, an open-source solution for the 5G core network, vulnerabilities that cover practical application scenarios of multiple interface protocols in the 5G core network were identified.These vulnerabilities have wide-ranging impact, are highly detrimental, and can be easily exploited.They have been reported to the vendor and assigned four Common Vulnerabilities and Exposures (CVE) numbers, demonstrating the effectiveness of this automated vulnerability discovery method.…”
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A local algorithm to approximate the global clustering of streams generated in ubiquitous sensor networks by Pedro Pereira Rodrigues, João Araújo, João Gama, Luís Lopes

“…L2GClust performs local clustering of the sources based on the moving average of each node’s data over time: the moving average is approximated using memory-less statistics; clustering is based on the furthest-point algorithm applied to the centroids computed by the node’s direct neighbors. …”
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Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach by Tirunagaru V. Sarathkumar, Arup Kumar Goswami, Baseem Khan, Kamel A. Shoush, Sherif S. M. Ghoneim, Ramy N. R. Ghaly

“…On one front, forecasting VPP generation units, including solar photovoltaic, wind power, and combined heat and power, employs a novel Adam Optimizer Long-Short-Term-Memory (AOLSTM) machine learning technique. Conversely, estimating the revenue’s superior frontier is accomplished by integrating energy storage and Monte-Carlo optimization. …”
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Subjective Hesitation in Paul Auster’s Report from the Interior: ‘you think of yourself as anyone, as everyone’ by Nicolas Pierre BOILEAU

“…The fragmented structure suggests that the retrospective narrative, which comes, first needs to complement the author’s memory. These objects also seem to supplement language where it fails to be referential. …”
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Prediction Interval of Interface Regions: Machine Learning Nowcasting Approach by Khaled Alielden, Enrico Camporeale, Marianna B. Korsós, Youra Taroyan

“…In this work, a 1D ensemble system comprised of a Long‐short‐term memory (LSTM) model and a Convolution Neural Network (CNN) model—LCNN is introduced to classify the observed IR time series and give the prediction interval nowcast of its transit time to the observer. …”
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The history of formation and development of the Natio­nal Museum of Roman Art in Mérida (Spain) by K.G. Gorshkova, O.A. Masalova

“…The article is devoted to the history of preservation of the archaeological monuments of the city of Mérida that keep the memory of the former greatness of the capital of the Roman province of Lusitania. …”
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Maturation, Refinement, and Serotonergic Modulation of Cerebellar Cortical Circuits in Normal Development and in Murine Models of Autism by Eriola Hoxha, Pellegrino Lippiello, Bibiana Scelfo, Filippo Tempia, Mirella Ghirardi, Maria Concetta Miniaci

“…In the adult cerebellar cortex, many developmental mechanisms persist but play different roles, such as supporting synaptic plasticity during learning and formation of cerebellar memory traces. A dysfunction at any stage of this process can lead to disorders of cerebellar origin, which include autism spectrum disorders but are not limited to motor deficits. …”
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Enhancing the Process of AES: A Lightweight Cryptography Algorithm AES for Ad-hoc Environments by Alharith A. Abdullah, Çağatay Aydin, Oğuz Ata, Mustafa Alhandhal

“…Ad hoc networks work on battery power, and this kind of networks tend to consume more power and time to process data and memory resources through data encryption, eventually requiring a higher amount of power and more time. …”
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Stage-Based Remaining Useful Life Prediction for Bearings Using GNN and Correlation-Driven Feature Extraction by Guangzhong Huang, Wenping Lei, Xinmin Dong, Dongliang Zou, Shijin Chen, Xing Dong

“…Then, a model combining Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) networks is proposed for bearing degradation stage classification. …”
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Environmental harshness does not affect the propensity for social learning in great tits, Parus major by Emil Isaksson, Julie Morand-Ferron, Alexis Chaine

“…Tests of this hypothesis to date have largely focused on asocial learning and memory, thus failing to account for the spread of information via social means. …”
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Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm by Hui Ning, Qian Li

“…The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. …”
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Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM by Bo Sun, Wenting Hu, Hao Wang, Lei Wang, Chengyang Deng

“…The resulting frequency domain data is then used as input to the convolutional neural network for feature extraction; Then, the weights of channel features and spatial features are assigned to the extracted features by CBAM, and the weighted features are then input into the Long Short-Term Memory (LSTM) network to learn temporal features. …”
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Unveiling inverted D genes and D-D fusions in human antibody repertoires unlocks novel antibody diversity by Ponraj Prabakaran, Abhinav Gupta, Sambasiva P. Rao, Deepak Rajpal, Maria Wendt, Yu Qiu, Partha S. Chowdhury

“…We identified 25 unique inverted D genes (InvDs) in both naive and memory B cells from antibody repertoires of 13 healthy donors. …”
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Exploring the Ability to Perform Activities of Daily Living and Cognitive Status after Hospitalization with COVID-19: A Multiple Case Study by Kate Allen Christensen, Jan Christensen, Signe Janum Eskildsen

“…Eleven patients were included. 75% had a significant increase in motor ability measures, and 27% had a significant increase in process ability measures at follow-up. 67% of follow-up cases showed mild cognitive impairment, where executive functioning and memory were most predominant. Conclusions. The ability to perform ADL was affected at discharge and at three-month follow-up. …”
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Reducing Training Time in Skin Cancer Classification Using Convolutional Neural Network with Mixed Precision Implementation by Raka Ryandra Guntara, Hendriyana, Indira Syawanodya

“…This study investigates the use of the MobileNetV3Large architecture for transfer learning, chosen for its efficiency in low-power and memory-constrained applications. To further enhance performance, black-hat morphological transformation and oversampling techniques were applied to the ISIC 2020 dataset. …”
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Multimodal Emotion Recognition: Emotion Classification Through the Integration of EEG and Facial Expressions by Songul Erdem Guler, Fatma Patlar Akbulut

“…This study aims to advance the knowledge on multimodal emotion recognition by combining electroencephalography (EEG) signals with facial expressions, using advanced models such as Transformer, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results validate the effectiveness of this approach, demonstrating the high accuracy of the Gated Recurrent Unit (GRU) model, which achieved an average of 91.8% classification accuracy on unimodal (EEG-only) data and an average of 97.8% classification accuracy on multimodal (EEG and facial expressions) datasets in the multi-class emotion categories. …”
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