bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural and vibrational properties of COFe2O4 ferrite at high pressure
Authors: Nguyen N. T. , Lis O. N. , Rutkauskas A. V. , Lukin E. V. , Kozlenko D. P. , Zhaludkevich A. L. , Kichanov S. E. , Truong-Tho N. , Dang N. T.
Published: November 2024

The crystal structure and vibrational spectra of CoFe2O4 ferrite were studied using X-ray diffraction and Raman spectroscopy over a pressure range of 0–35[Formula: see text]GPa. A structural phase transition from the cubic [Formula: see text] phase to the post-spinel orthorhombic Bbmm phase occurs at a pressure of approximately 23[Formula: see text]GPa through a two-phase region. Pressure-induced changes in the structural parameters, lattice distortion, and vibrational modes of the studied ferrite were investigated in detail. Lattice parameters, bond lengths, compressibility, and bulk modulus for both the cubic and orthorhombic phases of CoFe2O4 were determined.
Period Function for a Family of Piecewise Planar Hamiltonian Systems
Authors: Rezende Alex C. , Santos Mirianne A. S. , Torregrosa Joan
Published: November 2024

In this paper, we analyze the monotonicity and the number of critical periods of the period function associated with a family of piecewise planar continuous potential systems. In particular, we describe the bifurcation diagram of the period function, determining the regions of the parameter space where it is monotonically increasing or decreasing and where it has at most one simple critical period. In order to prove the main result, we rewrite such a function in terms of the two period functions of the uncoupled planar Hamiltonian systems matched by the separation line.
A Model-Based Evaluation Metric for Question Answering Systems
Authors: Bakır Dilan , Aktas Mehmet S. , Yıldız Beytullah
Published: November 2024

The paper addresses the limitations of traditional evaluation metrics for Question Answering (QA) systems that primarily focus on syntax and n-gram similarity. We propose a novel model-based evaluation metric, MQA-metric, and create a human-judgment-based dataset, squad-qametric and marco-qametric, to validate our approach. The research aims to solve several key problems: the objectivity in dataset labeling, the effectiveness of metrics when there is no syntax similarity, the impact of answer length on metric performance, and the influence of real answer quality on metric results. To tackle these challenges, we designed an interface for dataset labeling and conducted extensive experiments with human reviewers. Our analysis shows that the MQA-metric outperforms traditional metrics like BLEU, ROUGE and METEOR. Unlike existing metrics, MQA-metric leverages semantic comprehension through large language models (LLMs), enabling it to capture contextual nuances and synonymous expressions more effectively. This approach sets a standard for evaluating QA systems by prioritizing semantic accuracy over surface-level similarities. The proposed metric correlates better with human judgment, making it a more reliable tool for evaluating QA systems. Our contributions include the development of a robust evaluation workflow, creation of high-quality datasets, and an extensive comparison with existing evaluation methods. The results indicate that our model-based approach provides a significant improvement in assessing the quality of QA systems, which is crucial for their practical application and trustworthiness.
A PROBABILISTIC PHENOTYPE DYNAMICAL MODEL FOR SYMPATRIC SPECIATION: SOME PROPERTIES AND NUMERICAL RESULTS
Authors: BAZZANI ARMANDO , D’AMBROSIO RAFFAELE , FREGUGLIA PAOLO , VENTURINO EZIO
Published: November 2024

Sympatric speciation is an important phenomenon in Evolution: a population being able to express two different phenotypes gives rise to a new species without a physical separation from the original population. In this paper, we describe a model that describes the sympatric speciation process using a population dynamics approach. Our aim is to point out some dynamical features that could be observed in a population where a sympatric speciation process is going on. Some interesting stochastic effects are discussed by means of numerical simulations.
UNVEILING THE TENSILE AND FLEXURAL BEHAVIORS OF INFLORESCENCE/SISAL FIBER-FORTIFIED HYBRID EPOXY COMPOSITES
Authors: VAITHEESWARAN G. , MARIMUTHU K. , SRINIVASAN V. P. , SELVARAJAN L.
Published: November 2024

The enactment of natural fibers as a replacement for conventional synthetic fiber has aroused the interest of academicians and researchers to look out for newer materials. Surface modification of inflorescence fiber with 5% wt/vol of aqueous solution eliminated the functional elements present. An increase in crystal size of 17.84% was observed between untreated and alkali-treated inflorescence fibers. FTIR characterization revealed an increase in stress transfer capacity by the elimination of hemicelluloses, lignin, pectin and other amorphous substances. The addition of inflorescence fiber up to 20 wt.% with 10 wt.% of sisal fiber and 70 wt.% of epoxies solicited towards utter tensile strength of 48.42[Formula: see text]MPa and flexural strength of 72.69[Formula: see text]MPa. Around 51.98% increase in tensile strength was recorded between S0 (neat epoxy) and S4 (10% sisal and 20% inflorescence fiber) composites. Similarly, a 39.18% increase in flexural strength was estimated between S0 (neat epoxy) and S4 (10% sisal and 20% inflorescence fiber) composites. SEM analysis reported the formation of rough surfaces and cavities on the inflorescence fiber surface owing to NaOH modification of fibers. Fiber pullouts were also recorded in 25 wt.% reinforced inflorescences/sisal fiber fortified epoxy composites which attributed to a sudden decrease in tensile and flexural properties.
TEMPERATURE FIELD OPTIMIZATION AND TRIBOLOGICAL PERFORMANCE FOR BATCH PREPARATION OF DIAMOND-COATED MECHANICAL SEAL RINGS BY HFCVD METHOD
Authors: ZHU HAOJIE , LIU DONGDONG , SHEN RUI , SUN FANGHONG
Published: November 2024

Diamond coatings are deposited on the end surfaces of mechanical seal rings by the hot filament chemical vapor deposition (HFCVD) method. To improve the homogeneity of batch preparation of HFCVD diamond-coated seal rings, the temperature distributions on the end surfaces of seal rings are simulated and optimized using the finite volume method (FVM). The influence mechanism of various setup parameters on coating deposition is demonstrated systematically with the orthogonal experimental method, including the filament diameter d, the filament temperature [Formula: see text], the separation between two adjacent filaments D and the filament height H. The optimal HFCVD setting parameters for batch preparation of diamond-coated seal rings are proposed based on the simulation results of orthogonal experiments. Afterwards, mass deposition experiments of seal rings are conducted using the optimal setting parameters, and the diamond coatings deposited on the end surfaces of seal rings are characterized. Furthermore, mechanical seal operation bench tests are carried out to evaluate the abrasion resistance of diamond-coated seal rings. The results indicate that the optimized setting parameters can produce a homogeneous temperature field, and diamond coatings with uniform thickness and excellent wear resistance can be obtained in the batch preparation of diamond-coated seal rings.
INVESTIGATION ON THE MACHINABILITY OF TITANIUM GRADE 23 USING TEXTURED CUTTING TOOLS WITH CHIP BREAKER
Authors: ROY RIMA SINGHA , MAITY KALIPADA
Published: November 2024

In this paper, an experimental investigation of the machinability of Titanium grade 23 has been carried out using hybrid micro-textures on the cutting tool. The different types of micro-textures have been fabricated on the rake surface as well as on the flank surface of the cutting tool. The performance of textured tools with the presence of a chip breaker has been investigated. The selected patterns were vertical grooved rake face (VT-R), diagonal grooved rake face (DT-R), vertical grooved flank face (VT-F) and hybrid textured tools namely, vertical rake and flank (V+V), diagonal rake and vertical flank (D+V). A comparative study has been carried out to acknowledge the performance of textured tools while dry turning Titanium Grade 23 alloy. The machinability has been made to acknowledge the performance of textured tools with the variation of machining time in case of dry turning operation. The machinability criteria are investigated based on cutting forces, cutting temperature and tool wear mechanisms involved while machining. The performance of the cutting tool with DT-R and VT-F micro-texture is observed to be better in comparison to others. Innovative applications of micro-textured tools are for dry green machining without any environmental hazards and proper chip-flow control.
TRIBOLOGICAL BEHAVIOUR STUDY OF SiO2-TiO2-COATED INCONEL 617 ALLOY FABRICATED BY TIG WELDING
Authors: KUMAAR S. SENTHIL , KORRA NANDA NAIK , DEVAKUMARAN K. , KUMAR K. GANESH
Published: November 2024

In this study, the tribological behavior of Tungsten Inert Gas (TIG) welded Inconel 617 alloy has been studied using the Pin-on-disc tribometer. TIG welding was performed after imparting surface coatings to the Inconel 617 alloy which was coated with a blend of Silicon dioxide (SiO2) and Titanium dioxide (TiO2) in different proportions. Three samples were prepared by varying the coating composition and weld current. The Coefficient of Friction (COF), wear depth ([Formula: see text]m) and hardness of the samples were estimated. Microstructure analysis and X-ray diffraction (XRD) analysis were also carried out on the samples. The wear depth of the Inconel 617 substrate, as well as weld bead samples 1, 2, and 3, were 2055.1, 415.39, 463.17, and 560.68 [Formula: see text]m respectively. The COF for the Inconel 617 substrate and weld bead samples 1, 2, and 3 were determined as 0.384, 0.491, 0.471, and 0.455 respectively. By analyzing the results of the wear test, it was observed that ‘sample 1’ welded at the lowest heat input of 4.32 kJ/mm, exhibited superior tribological characteristics including reduced wear (415.39 [Formula: see text]m) and an increased COF (0.491) compared to the other samples. The enhanced tribological performance could be attributed to factors such as the presence of carbides like TiC and notably the higher hardness (284.12[Formula: see text]HV) exhibited by ‘sample 1’.
Lie symmetries, conservation laws, optimal system and power series solutions of time fractional modified KdV–Zakharov–Kuznetsov equation
Authors: Yu Jicheng , Feng Yuqiang
Published: November 2024

In this paper, Lie symmetry analysis method is applied to [Formula: see text]-dimensional time fractional modified KdV–Zakharov–Kuznetsov equation. All Lie symmetries and the corresponding conserved vectors for the equation are obtained. The one-dimensional optimal system is utilized to reduce the aimed equation with Riemann–Liouville fractional derivative to a low dimensional fractional partial differential equation with Erdélyi–Kober fractional derivative. Then the power series solution of the reduced equation is given. Moreover, some other low dimensional reduced fractional differential equations with Riemann–Liouville fractional derivative are obtained and can be solved by different methods in the literatures herein.
Super-Poisson Renewal Process Characterized by Waiting Time Distributions with the Locally Thickened Tail
Authors: Dubkov Alexander , Kargovsky Aleksey , Chichigina Olga
Published: November 2024

Super-Poisson renewal processes with different distributions of waiting times intervals are investigated. A distribution with a locally thickened tail is proposed. The method for generation of this process is suggested. It is shown that this distribution can be used to solve the search problem.
Two-States Brownian Particle in a Harmonic Potential
Authors: Carollo Giovanni Battista , Gonnella Giuseppe , Moretti Daniela , Suma Antonio , Baldovin Fulvio , Orlandini Enzo
Published: November 2024

In this paper, we study the behavior of a Brownian particle in the overdamped regime in the presence of a harmonic potential, assuming its diffusion coefficient to randomly jump between two distinct values. In particular, we characterize the probability distribution of the particle position and provide detailed expressions for the mean square displacement and the kurtosis. We highlight non-Gaussian behavior even within the long-term limit carried over with an excess of probability both in the central part and in the distribution tails. Moreover, when one of the two diffusion coefficients assumes the value zero, we provide evidence that the probability distribution develops a cusp. Most of our results are analytical, and corroborated by numerical simulations.
Some Examples of Silted Algebras of Dynkin Type
Authors: Xing Ruoyun
Published: November 2024

This paper studies silted algebras, namely, endomorphism algebras of 2-term silting complexes, over path algebras of Dynkin quivers. We describe an algorithm to produce all basic 2-term silting complexes over the path algebra of a Dynkin quiver, and use this algorithm to compute some examples.
Construction of Cospectral Cayley Digraphs over Dihedral Group D 6p
Authors: Yang Jing , Tang Lang , Liu Weijun , Feng Lihua
Published: November 2024

Let [Formula: see text] be a finite group and [Formula: see text], where [Formula: see text] denotes the identity element of [Formula: see text]. The Cayley digraph [Formula: see text] of [Formula: see text] over the set [Formula: see text] is the graph with vertex set [Formula: see text] and edge set [Formula: see text]. Two graphs are called cospectral if their adjacency matrices have the same spectrum. We construct a large family of cospectral non-isomorphic Cayley digraphs over the dihedral group of order [Formula: see text] for [Formula: see text].
Finite Autocapable Groups with the 2-Groups of Maximal Class
Authors: Bai Pengfei , Guo Xiuyun , Wang Jiao
Published: November 2024

A group [Formula: see text] is said to be autocapable if there exists a group [Formula: see text] such that [Formula: see text] is isomorphic to the absolute central factor group [Formula: see text] of [Formula: see text]. In this paper, we first prove that if [Formula: see text] is a characteristic subgroup of an autocapable group [Formula: see text], then [Formula: see text] is neither the generalized quaternion group nor the semi-dihedral group. Next, we give the classification of finite groups [Formula: see text] if [Formula: see text] is a 2-group of maximal class.
Small cancellation groups are bi-exact
Authors: Oyakawa Koichi
Published: November 2024

In this paper, we prove that finitely generated (not necessarily finitely presented) [Formula: see text]-groups are bi-exact. This is a new class of bi-exact groups.
On the SD number and the ID number of generalized hypercube networks
Authors: Yang Jinxia , Tang Fang , Liang Zhipeng
Published: November 2024

Generalized hypercube is an interconnection network topology that exhibits excellent performance and symmetry; it facilitates the expansion of capacity and the upgrading of resources to accommodate larger demands; the domination parameters serve as a crucial foundation for the analysis and quantification of the dependability of interconnection networks. In this paper, we explore the structure and properties of generalized hypercube networks to establish the bounds of signed domination numbers and Italian domination numbers. We also present a vertex set partitioning approach to investigate these parameters for some small dimensional generalized hypercube networks and hypercube networks. Furthermore, a Vizing-type conjecture was proposed that satisfies the signed domination number and Italian domination number of generalized hypercube networks.
Dynamics of Darcy–Forchheimer flow of Casson nanofluidic model with Newtonian heating: Nonlinear input–output neural networks
Authors: Shah Zahoor , Raja Muhammad Asif Zahoor , Shoaib Muhammad , Shahzad Faisal
Published: November 2024

This research work presents the numerical solution of the Darcy–Forchheimer flow of Casson nanofluidic model (DFFCNFM) on stretching sheets with Newtonian heating by utilizing a combination of nonlinear input–output neural networks with backpropagation of Levenberg–Marquardt computational approach. The presented study investigates the impact of electroosmosis forces on the boundary layer of the Casson nanofluid, focusing on viscous and Joule dissipations. A dataset for DFFCNFM is generated for the different events with backward differentiation formula (BDF) by varying Casson fluid parameter ([Formula: see text], permeability parameter (Da), electric parameter ([Formula: see text], Reynolds number (Re) relative to stretching velocity, magnetic field ([Formula: see text], Eckert number (Ec), and Prandtl number (Pr). The artificial intelligence-inspired technique via nonlinear input–output neural networks with backpropagation of Levenberg–Marquardt is utilized from the generated dataset for DFFCNFM to find the approximate solutions. The satisfactory performance levels, as indicated by the mean square error (MSE), have been attained consistently with magnitude around 10[Formula: see text]–10[Formula: see text] for all scenarios of DFFCNFM. The precision and performance validation is effectively established by the negligible MSE, close proximity to the unit value of regression metric, and the distribution of instances in error-histograms.
Chapter 9. Albertine Rift Stratigraphy 1: Sedimentary Architecture
Authors: Nicholas Christopher J.
Published: November 2024

A persistent problem that has hampered a general understanding of Albertine Rift early continental rift basins has been the lack of a high-resolution stratigraphic framework within which to correlate sediments across their onshore outcrop. Crucial to resolving this problem is resolving how the three-dimensional stacking architecture of sediments is controlled by a combination of evolving rift basin structure and the glacial climatic cyclicity experienced at the Equatorial Tropics of East Africa during deposition. Rift basin structural styles and subsidence rates are modelled and their merits discussed in light of the field evidence gathered from around the Albertine Rift. After establishing the mechanism of structural development in the basins, the impact of glacially-driven climatic oscillations on sedimentary lithofacies is also incorporated to produce a combined glacial climatic cyclicity model (GCCM) for Albertine Rift basin evolution. This superimposes lake level transgressive – regressive cyclicity upon a tectonic regime of fault propagation and depocentre retreat away from rift margins. The model predicts the resulting stratigraphic architecture that should develop in outcrop areas and is supported by field evidence of not only lithofacies characteristic of arid (glacial) and wet (interglacial) climatic conditions but also cycles of 3-D terrace development, channel incision and sediment backfilling.
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Authors: Luo Sichun , He Bowei , Zhao Haohan , Shao Wei , Qi Yanlin , Huang Yinya , Zhou Aojun , Yao Yuxuan , Li Zongpeng , Xiao Yuanzhang , Zhan Mingjie , Song Linqi
Published: November 2024

Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has employed specialized
prompts
to leverage the in-context learning capabilities of LLMs for recommendation purposes. More recent studies have utilized instruction tuning techniques to align LLMs with human preferences, promising more effective recommendations. However, existing methods suffer from several limitations. The full potential of LLMs is not fully elicited due to low-quality tuning data and the overlooked integration of conventional recommender signals. Furthermore, LLMs may generate inconsistent responses for different ranking tasks in the recommendation, potentially leading to unreliable results.

In this paper, we introduce RecRanker, tailored for instruction tuning LLMs to serve as the
Ranker
for top-
k
Rec
ommendations. Specifically, we introduce importance-aware sampling, clustering-based sampling, and penalty for repetitive sampling for sampling high-quality, representative, and diverse training data. To enhance the prompt, we introduce a position shifting strategy to mitigate position bias and augment the prompt with auxiliary information from conventional recommendation models, thereby enriching the contextual understanding of the LLM. Subsequently, we utilize the sampled data to assemble an instruction-tuning dataset with the augmented prompts comprising three distinct ranking tasks: pointwise, pairwise, and listwise rankings. We further propose a hybrid ranking method to enhance the model performance by ensembling these ranking tasks. Our empirical evaluations demonstrate the effectiveness of our proposed
RecRanker
in both direct and sequential recommendation scenarios.

1
New insights into the impact of bed rest on lumbopelvic muscles: a computer-vision model approach to measure fat fraction changes
Authors: Wesselink Evert O. , Hides Julie , Elliott James M. , Hoggarth Mark , Weber Kenneth A. , Salomoni Sauro E. , Tran Vienna , Lindsay Kirsty , Hughes Luke , Weber Tobias , Scott Jonathan , Hodges Paul W. , Caplan Nick , De Martino Enrico
Published: November 2024

This study reveals novel insights into fat fraction changes in lumbopelvic muscles after 60 days of head-down bed rest and 13 days of reconditioning. Lipids increased in the deep regions of the lumbar multifidus (LM) and lumbar erector spinae (LES), particularly at lower vertebral levels, and persisted after reconditioning. These findings highlight the need to target deep fascicles of LM and LES in future countermeasures to mitigate microgravity-induced deconditioning and optimize spine health.
Pathological Image Segmentation Method Based on Multiscale and Dual Attention
Authors: Wu Jia , Niu Yuxia , Ling Ziqiang , Zhu Jun , Gou Fangfang
Published: November 2024

Medical images play a significant part in biomedical diagnosis, but they have a significant feature. The medical images, influenced by factors such as imaging equipment limitations, local volume effect, and others, inevitably exhibit issues like noise, blurred edges, and inconsistent signal strength. These imperfections pose significant challenges and create obstacles for doctors during their diagnostic processes. To address these issues, we present a pathology image segmentation technique based on the multiscale dual attention mechanism (MSDAUnet), which consists of three primary components. Firstly, an image denoising and enhancement module is constructed by using dynamic residual attention and color histogram to remove image noise and improve image clarity. Then, we propose a dual attention module (DAM), which extracts messages from both channel and spatial dimensions, obtains key features, and makes the edge of the lesion area clearer. Finally, capturing multiscale information in the process of image segmentation addresses the issue of uneven signal strength to a certain extent. Each module is combined for automatic pathological image segmentation. Compared with the traditional and typical U‐Net model, MSDAUnet has a better segmentation performance. On the dataset provided by the Research Center for Artificial Intelligence of Monash University, the IOU index is as high as 72.7%, which is nearly 7% higher than that of U‐Net, and the DSC index is 84.9%, which is also about 7% higher than that of U‐Net.
A Comparative Study of the Pendulum Equation Using Two Analytical Methods
Authors: Sharif Nazmul , Yeasmin Ismot Ara
Published: November 2024

This paper presents a comprehensive comparison between the modified harmonic balance method (MHBM) and He’s frequency formulation (HFF) for solving the nonlinear dynamics of an excited pendulum constrained by a crank‐shaft‐slider mechanism (CSSM). The pendulum’s motion, influenced by the CSSM, introduces strong nonlinearity, making the analysis challenging using conventional methods. The harmonic balance method (HBM), a widely used technique for approximating solutions to nonlinear differential equations, often becomes cumbersome due to its complex nature, particularly when higher‐order approximations are necessary. This complexity leads to the emergence of a set of intricate nonlinear complex equations that are not easy to solve. To overcome the limitations of HBM, a modified form of the HBM is adopted in this study. The performance of the MHBM is thoroughly evaluated by comparing the results with those obtained using HFF. Additionally, the numerical solutions are obtained using the Runge–Kutta fourth‐order method, serving as a benchmark to assess the accuracy of both analytical approaches. Remarkably, the first approximation provided by the MHBM exhibits superior accuracy compared to the solutions obtained from the HFF method and other existing analytical methods. The results highlight the potential of MHBM as a reliable and accurate method for solving nonlinear differential equations in various engineering applications, particularly in systems exhibiting strong nonlinearity.
The crucial role of artificial intelligence in addressing climate change
Authors: Andrade-Arenas Laberiano , Hernández Celis Domingo , Yactayo-Arias Cesar
Published: November 2024

Addressing climate change is one of the fundamental priorities at a global level, given its significant impact on both the environment and society. This systematic literature review explores the role of artificial intelligence (AI) in addressing climate change. It identified applications, contributions to predicting extreme events, techniques used, ethical challenges, and associated biases. The rapid systematic literature review (RSL) was conducted using databases such as Scopus, Dimensions, directory of open access journals (DOAJ), and IEEE Xplore. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was used to ensure the completeness and transparency of the analysis. 40 articles were selected that were published between 2018 and 2023 and addressed AI in climate change. The findings show that AI is being used to predict and mitigate extreme climate events, estimate the greenhouse effect, and predict temperatures. In addition, innovative techniques such as hybrid machine learning models, convolutional neural networks, artificial neural networks, support vector machines, and logistic regression. In conclusion, AI offers a promising approach to addressing climate change, with transformative potential in predicting and mitigating its effects. However, continuous ethical considerations are required to guarantee its conscientious and efficient utilization.
Convolutional neural network modelling for autistic individualized education chatbot
Authors: Hamzah Raseeda , Jamil Nursuriati , Ahmad Nor Diana , Syed Zainal Ariffin Syed Mohd Zahid
Published: November 2024

The traditional education system for autistic kids needs integration with computer technology that embraces artificial intelligence to help school instructors and management. An application that enables the teacher to retrieve information from a trusted source is essential since the information is only sometimes available on time. Thus, developing a chatbot application that utilizes natural language processing can enhance the management of autistic schools and will help individualized education for autistic students. This research uses a deep learning model that utilizes a convolutional neural network to develop a chatbot as a teaching assist tool for teachers. The results show that the chatbot has achieved ˜0.03% loss when trained with different epoch numbers. In terms of usability, the chatbot achieves mean system usability scores of 80.48 ± 13.03. This may open opportunities for more effective individualized education for students with special needs and increase the potential to improve inclusive education for disabled students. It is useful to include future actions that enable the simplification of the use of this chatbot tool in a wide range of contexts. To close the education gap for children with disabilities, chatbots could help people with communication disabilities and could also significantly enhance the rate of communication.
Advancing integrity and privacy in cloud storage: challenges, current solutions, and future directions
Authors: Shrinivasa Shrinivasa , Beturpalya Muddaraju Chandrakala , Prashanth Patil Annapurna
Published: November 2024

The rapid expansion of cloud computing has steered in an era where cloud storage is increasingly prevalent, offering significant advantages in terms of reducing local storage burden. However, this technological shift has also introduced complex security challenges, including data integrity and privacy concerns. In response to these challenges, various data integrity auditing (DIA) protocols have been developed, aiming to enable efficient and secure verification of data stored in cloud environments. This survey paper provides a comprehensive analysis of existing DIA mechanisms, focusing on methods like homomorphic linear authentication, dynamic hash tables, and watermarking techniques for integrity and privacy preservation. It critically evaluates these methods in terms of their advantages, limitations, and the unique challenges they face in practical applications, such as scalability, efficiency in multi-owner contexts, and real-time auditing. Furthermore, the paper identifies key research gaps, including the need for optimizing largescale data handling, balancing watermarking imperceptibility with embedding capacity, and developing comprehensive solutions for decentralized public auditing. The survey serves as a critical resource for researchers to understand the current background of cloud data integrity auditing and the future directions in this evolving field.
Predicting water resistance and pitching angle during take-off: an artificial neural network approach
Authors: Fajar Muhammad , Atmaja Sigit Tri , Pinindriya Sinung Tirtha , Soemaryanto Arifin Rasyadi , Hidayat Kurnia
Published: November 2024

This research addresses the challenges faced by seaplanes and amphibious aircraft during takeoff and landing on water, emphasizing the limitations and costs associated with traditional towing tank tests and computational fluid dynamics (CFD) simulations. The study proposes an innovative approach that employs artificial neural networks (ANN) to predict water resistance and pitching angle during amphibious aircraft take-off, minimizing the reliance on expensive towing tank tests. The ANN models are developed and optimized using Bayesian optimization, showcasing improved accuracy in predicting water resistance and pitching angle. The research demonstrates the potential of machine learning, specifically ANNs, to significantly reduce the need for costly experimental tests, providing an efficient alternative for designing amphibious aircraft. The results indicate high accuracy in predicting water resistance and pitching angle, offering substantial time and resource savings during the experimental phase. However, the study highlights the need for model adaptation for different designs and test variations to enhance overall applicability.
A Fletcher-Reeves conjugate gradient algorithm-based neuromodel for smart grid stability analysis
Authors: Ojo Adedayo Olukayode , Eyitayo Aiyedun Olatilewa , Onibonoje Moses Oluwafemi , Gbadamosi Saheed Lekan
Published: November 2024

Interest in smart grid systems is growing around the globe as they are getting increasingly popular for their efficiency and cost reduction at both ends of the energy spectrum. This study, therefore, proposes a neuro model designed and optimized with the Fletcher-Reeves conjugate gradient algorithm for analyzing the stability of smart grids. The performance results achieved with this algorithm was compared with those obtained when the same network was trained with other algorithms. Our results show that the proposed model outperforms existing techniques in terms of accuracy, efficiency, and speed. This study contributes to the development of intelligent solutions for smart grid stability analysis, which can enhance the reliability and sustainability of power systems.
Improving the performance of the fuzzy-internet of things joint system by using an efficient service deployment algorithm
Authors: Razzaq Ali Ahmed , Rao Kunjam Nageswara
Published: November 2024

The aim of this paper to present research proposes a quality of service (QoS)-fog service placement algorithm, which is in the context of providing better performance when there are requests for high-priority (delay-sensitive) services. Our proposed algorithm attempts to maintain the implementation of delay-sensitive services within a fog environment. Its performance was evaluated by comparing it with two placement strategies (cloud only, edge-ward), using the MobFogSim simulator. The results showed that the proposal achieved better performance than the two mentioned strategies, from two perspectives (according to the scenarios). The first is at the service level, where the algorithm was able to achieve a lower average response time and the other is at the system level, as it was able to reduce the total energy consumption by adopting a mechanism to save energy when there is a low network load.
Artificial intelligence and machine learning adoption in the financial sector: a holistic review
Authors: Sayari Karima , Jannathl Firdouse Mohamed Kasim , Al Abri Fathiya
Published: November 2024

The evolution of new technologies has spurred a growing body of literature exploring their application and impact on the financial sector, particularly the integration of artificial intelligence (AI). This paper delves into the rapid adoption of AI and machine learning within the financial sector, highlighting their potential to enhance financial stability and productivity. By reviewing research from 2018 to 2023, the study categorizes AI applications in finance into three main areas: cybersecurity, customer services, and financial management. Furthermore, the research identifies and classifies various threats posed to the integrity and stability of the financial system by AI, along with associated challenges for policy and regulatory frameworks. It also addresses the risks and obstacles inherent in deploying AI within financial markets and banking sectors, offering recommended strategies to mitigate these limitations. Despite the recognized advantages, the comprehensive understanding of AI's benefits and drawbacks remains incomplete due to its evolving nature and varied applications in banking. Clear policies governing AI usage are imperative to safeguard financial consumers and promote a fair and transparent financial market. These guidelines should prioritize human decision-making and foster an unbiased approach to policymaking, ultimately fostering innovation within the industry.
Imitation of the human upper limb by convolutional neural networks
Authors: Useche Murillo Paula , Jimenez Moreno Robinson , Martinez Baquero Javier Eduardo
Published: November 2024

The paper outlines the development of an algorithm focused on imitating movements of a human arm and replicating strokes generated by the user's hand within a working environment. The algorithm was crafted to discern the position of either the user's left or right arm, tracking each section (fingers, wrist, elbow, and shoulder) through a detection and tracking system. These movements are then replicated onto a virtual arm, simulating the actions of a cutting tool, generating strokes as it moves. Convolutional neural networks (CNNs) were employed to detect and classify each arm section, while geometric analysis determined the rotation angles of each joint, facilitating the virtual robot's motion. The stroke replication program achieved an 84.2% accuracy in stroke execution, gauged by the closure of the polygon, distance between initial and final drawing points, and generated noise, which was under 10%, with a 99% probability of drawing a closed polygon. A Fast region-based convolutional neural network (Fast R-CNN) network detected each arm section with 60.2% accuracy, producing detection boxes with precision ranging from 17% to 59%. Any recognition shortcomings were addressed through mathematical estimation of missing points and noise filters, resulting in a 90.4% imitation rate of human upper limb movement.
Microarray gene expression classification: dwarf mongoose optimization with deep learning
Authors: Balaraman Shyamala Gowri , Nair Anu H. , Kumar Sanal
Published: November 2024

The deoxyribonucleic acid (DNA) microarray model holds significant promise for revealing expression data from thousands of genes. It serves as a valuable tool for investigating gene expressions in diverse biological research fields. This study explores advancements in gene selection for cancer detection through artificial intelligence, with a focus on the challenge of extracting pertinent information from vast databases. The application of deep learning architecture in detecting chronic diseases and aiding medical decision-making has proven effective across various domains. Therefore, this study designs an enhanced microarray gene expression classification by utilizing a dwarf mongoose optimization with deep learning (MGEXC-DMODL) approach. The MGEXC-DMODL approach intends to classify the microarray gene expression (MGE). For this, the MGEXC-DMODL technique initially applies the wiener filtering (WF) technique to eradicate the noise. In addition, the MGEXC-DMODL technique employs a deep residual shrinkage network (DRSN) to learn feature vectors. Meanwhile, the convolutional autoencoder (CAE) model was executed for identifying and classifying the MGE data. Furthermore, the dwarf mongoose optimization (DMO)-based hyperparameter tuning is performed to enhance the detection outcomes of the CAE model. The investigational evaluation of the MGEXC-DMODL model is validated using a benchmark database. The comprehensive comparison outcome highlighted the betterment of the MGEXC-DMODL model over recent approaches. 
Comparison of faster region-based convolutional network for algorithms for grape leaves classification
Authors: Sarosa Moechammad , Ma'rifah Puteri Nurul , Kusumawardani Mila , Al Riza Dimas Firmanda
Published: November 2024

The shapes of leaves distinguish the Indonesian grape variants. The grape leaves might look the same at first glance, but there are differences in leaf shapes and characteristics when observed closely. This research uses a deep learning method combined with the faster region-based convolutional neural network (R-CNN) algorithm with the Inception network architecture, ResNet V2, ResNet-152, ResNet-101, and ResNet-50, and uses COCO weights trained to classify five grape varieties through leaf images. The study collected 500 images to be used as an independent dataset. The results show that network improvements can effectively improve operating efficiency. There are also limitations to training scores because the F1 score value tends to stabilize or decrease at a certain point. In the Inception ResNet V2 architecture, with the highest average F1 score of 92%, the average computing time for training and testing is longer than other network architectures. This suggests that the algorithm can classify types of grapes based on their leaves.
Neural networks based-simple estimated model for greenhouse gas emission from irrigated paddy fields
Authors: Arif Chusnul , Purwanto Yohanes Aris , Rudiyanto Rudiyanto , Mizoguchi Masaru
Published: November 2024

The current study aims to develop a simple model for estimating greenhouse gas emissions originating from paddy fields, utilizing backpropagation neural networks. The model integrated three input parameters: soil moisture, soil temperature, and soil electrical conductivity (EC), while generating estimations for two output parameters: methane (CH4) and nitrous oxide (N2O) emissions. The model was put into practice across three different irrigation systems, i.e., continuous flooded (FL), wet (WT), and dry (DR) regimes. For model training and validation, the input parameters were measured by a single 5-TE sensor. Concurrently, CH4 and N2O emissions were determined utilizing a closed chamber, and gas samples were subjected to laboratory analysis. Findings unveiled that the developed model accurately estimated CH4 and N2O emissions, demonstrating commendable coefficient of determination (R2) values ranging from 0.60 to 0.97 for validation process. Notably, the WT irrigation system exhibited the highest precision, boasting R2 values of 0.97 for CH4 and 0.73 for N2O estimation, respectively. Conversely, the FL irrigation system has the lowest accuracy with R2 values of 0.66 and 0.60. Despite variances in accuracy across irrigation systems, the overall performance remained deemed acceptable, warranting the model's applicability for estimating greenhouse gas emissions under diverse irrigation scenarios.
Comparative analysis of genetic algorithms for automated test case generation to support software quality
Authors: Hadiningrum Tiara Rahmania , Rochimah Siti
Published: November 2024

Software testing is crucial for enhancing software quality, but designing test cases is a labor-intensive, resource-intensive, and time-consuming process. Additionally, test case designers often introduce subjectivity when creating test cases manually. To address these challenges, this paper compares three different approaches for automatically generating program branch coverage test cases: the parallel data generation algorithm (PDGA), a standard genetic algorithm (SGA), and a random test generation method. By leveraging genetic algorithms and parallel data generation techniques, these automated approaches aim to reduce the manual effort, resources, and potential biases involved in test case design, while improving the efficiency and effectiveness of achieving comprehensive branch coverage during software testing. The experimental results, conducted using five datasets with programs written in PHP, demonstrate that PDGA outperforms both SGA and random methods across various tested programs, achieving higher maximum and average coverage. Specifically, PDGA achieved an average coverage of 100% in the "calculator" program, highlighting its superior stability and efficiency. While SGA also shows good performance, it is not as optimal as PDGA, and the random method shows the lowest performance among the three. These findings underscore the potential of genetic algorithms, particularly PDGA, to enhance the coverage and quality of software testing, thereby significantly improving system reliability. 
Machine learning-assisted decision support in industrial manufacturing: a case study on injection molding machine selection
Authors: Tayalati Faouzi , Idiri Soulaimane , Boukrouh Ikhlass , Azmani Abdellah , Azman Monir
Published: November 2024

Selecting the right injection molding machine for new products remains a challenging task that significantly influences the profitability and flexibility of companies. The conventional approach involves performing theoretical calculations for clamping force, conducting mechanical validations of the mold, and carrying out real trials for new parts. This approach is time-consuming, costly, and requires a high level of expertise to ensure the optimal machine choice. This study explores the use of machine learning (ML) methods for efficient machine selection based on product, material, and mold criteria. Six supervised learning techniques were tested on a dataset comprising 70 plastic parts and five machines. Evaluation metrics like F1-score, recall, precision, and accuracy were used to compare models. The results indicate that ML can provide guidance for predicting machine selection, with a preference for the random forest (RF), decision tree (DT), and support vector machine (SVM) models. The most favorable outcome is demonstrated by the RF model, displaying an accuracy of 93%. In this manner, these findings may be helpful for injection molding businesses that are considering the significance of using classification algorithms in their manufacturing process. 
Explainable machine learning models applied to predicting customer churn for e-commerce
Authors: Boukrouh Ikhlass , Azmani Abdellah
Published: November 2024

Precise identification of customer churn is crucial for e-commerce companies due to the high costs associated with acquiring new customers. In this sector, where revenues are affected by customer churn, the challenge is intensified by the diversity of product choices offered on various marketplaces. Customers can easily switch from one platform to another, emphasizing the need for accurate churn classification to anticipate revenue fluctuations in
 e-commerce. In this context, this study proposes seven machine learning classification models to predict customer churn, including decision tree (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), k-nearest neighbors (K-NN), and artificial neural network (ANN). The performances of the models were evaluated using confusion matrix, accuracy, precision, recall, and F1-score. The results indicated that the ANN model achieves the highest accuracy at 92.09%, closely followed by RF at 91.21%. In contrast, the NB model performed the least favorably with an accuracy of 75.04%. Two explainable artificial intelligence (XAI) methods, shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), were used to explain the models. SHAP provided global explanations for both ANN and RF models through Kernel SHAP and Tree SHAP. LIME, offering local explanations, was applied only to the ANN model which gave better accuracy.
An efficient load-balancing in machine learning-based DC-DC conversion using renewable energy resources
Authors: Shankara Kavitha Hosakote , Hosakote Shankara Kavitha , Srikantaswamy Mallikarjunaswamy , Nagaraju Sharmila , Nagaraju Sharmila
Published: November 2024

This paper introduces the machine learning-based DC-DC conversion algorithm (ML-DC2A), a pioneering machine learning (ML) approach designed to enhance load-balancing in DC-DC conversion systems powered by renewable energy sources. Traditional control strategies, such as pulse-width modulation (PWM), maximum power point tracking (MPPT), and basic voltage and current controls, are foundational yet often fall short in adapting to the rapid fluctuation’s characteristic of renewable energy supply. The ML-DC2A optimizes crucial performance indicators including conversion efficiency, reliability, adaptability to energy supply variability, and response time to changing loads. By leveraging predictive analytics and adaptive algorithms, it dynamically manages the conversion process, offering superior performance over traditional techniques. A notable drawback of conventional methods is their inability to anticipate and adjust to real-time changes in energy availability and demand, leading to inefficiencies and potential system instability. The proposed ML-DC2A addresses these challenges by incorporating a sophisticated ML framework that predicts future energy scenarios and adaptively adjusts system parameters to maintain optimal performance. Initial results highlight the transformative potential of integrating ML into renewable energy conversion systems, promising significantly enhanced efficiency and system resilience, thus marking a significant step forward in sustainable energy management.