bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization Applications as Quantum Performance Benchmarks
Authors: Lubinski Thomas , Coffrin Carleton , McGeoch Catherine , Sathe Pratik , Apanavicius Joshua , Bernal Neira David , Quantum Economic Development Consortium(QED-C) Collaboration
Published: July 2024

Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm and Quantum Annealing can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-cut problems using gate-model quantum devices and a quantum annealing device. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through compelling visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. As an illustration, we explore the factors that influence quantum computing system throughput, using results obtained through execution on various quantum simulators and quantum hardware systems.
Utilizing classical programming principles in the Intel Quantum SDK: implementation of quantum lattice Boltzmann method
Authors: Shinde Tejas , Budinski Ljubomir , Niemimäki Ossi , Lahtinen Valtteri , Liebelt Helena , Li Rui
Published: July 2024

We explore the use of classical programming techniques in implementing the quantum lattice Boltzmann method in the Intel Quantum SDK—a software tool for quantum circuit creation and execution on Intel quantum hardware. As hardware access is limited, we use the state vector simulator provided by the SDK. The novelty of this work lies in leveraging classical techniques for the implementation of quantum algorithms. We emphasize the refinement of algorithm implementation and devise strategies to enhance quantum circuits for better control over problem variables. To this end, we adopt classical principles such as modularization, which allows for systematic and controlled execution of complex algorithms. Furthermore, we discuss how the same implementation could be expanded from state vector simulations to execution on quantum hardware with minor adjustments in these configurations.
Enabling Technologies and Techniques for Floor Identification
Authors: Ashraf Imran , Zikria Yousaf Bin , Garg Sahil , Hur Soojung , Park Yongwan , Guizani Mohsen
Published: July 2024

Location information has initiated a multitude of applications such as location-based services, health care, emergency response and rescue operations, and assets tracking. A plethora of techniques and technologies have been presented to ensure enhanced location accuracy, both horizontal and vertical. Despite many surveys covering horizontal localization technologies, the literature lacks a comprehensive survey incorporating up-to-date vertical localization approaches. This article provides a detailed survey of different vertical localization techniques such as path loss models, time of arrival, received signal strength, reference signal received power, fingerprinting utilized by WiFi, radio-frequency identification (RFID), global system for mobile communications (GSM), long-term evolution (LTE), barometer, inertial measurement unit (IMU) sensors, and geomagnetic field. The article primarily aims at human localization in indoor environments using smartphones in essence. Besides the localization accuracy, the presented approaches are evaluated in terms of cost, infrastructure dependence, deployment complexity, and sensitivity. We highlight the pros and cons of these approaches and outline future research directions to enhance the accuracy to meet the future needs of floor identification standards set by the Federal Communications Commission.
Bruton’s tyrosine kinase (BTK) inhibitors alter blood glucose and insulin in obese mice but reduce inflammation independent of BTK
Authors: Chan Darryl Y. , Barra Nicole G. , Fang Han , Rodrigues e-Lacerda Rodrigo , Schertzer Jonathan D.
Published: July 2024

Bruton’s tyrosine kinase (BTK) is involved in immune function. It was thought that BTK inhibitors improve characteristics of obesity-related metabolic disease by lowering metabolic inflammation. However, tyrosine kinase inhibitors are permissive, and it was not known if different BTK inhibitors alter host metabolism or immunity through actions on BTK. We found that two BTK inhibitors had divergent effects on blood glucose and insulin via BTK, but inhibition of metabolic inflammation occurred independently of BTK in obese mice.
Mesenchymal Stem Cell–Conditioned Media Modulate HUVEC Response to H2O2: Impact on Gene Expression and Potential for Atherosclerosis Intervention
Authors: Alqasoumi Abdulmajeed , Alsharidah Mansour , Mahmood Amer , Elsafadi Mona , Al Rugaie Osamah , Mohany Khalid M. , Al-Regaiey Khalid A. , Alyahya Khaleel I. , Alanteet Alaa A. , Algarzae Norah K. , AlGhibiwi Hanan K. , AlHomaidi Adel , Abumaree Mohammad
Published: July 2024

Background: We studied the potential of human bone marrow–derived mesenchymal stem cell conditioned media (hBMSC CM) in protecting endothelial cell properties (viability, proliferation, and migrations) from the deleterious effects produced by the inflammatory environment of H2O2. Additionally, we investigated their impact on the endothelial cells’ gene expression of some inflammatory‐related genes, namely, TGF‐β1, FOS, ATF3, RAF‐1, and SMAD3.

Methods: Human umbilical vein endothelial cells (HUVECs) were cultured individually under three conditions: alone, with varying concentrations of H2O2, or with varying concentrations of H2O2 and hBMSC CM. HUVEC adhesion, proliferation, and migration were evaluated using the xCELLigence system. The HUVECs’ gene expressions were evaluated by real‐time polymerase chain reaction (RT–PCR).

Results: Generally, we observed enhanced HUVEC viability, proliferation, and migration when cultured in media supplemented with H2O2 and hBMSC CM. Furthermore, the CM modulated the expressions of the studied inflammatory‐related genes in HUVECs, promoting a more robust cellular response.

Conclusion: This study has illuminated the protective role of hBMSC CM in mitigating the damaging effects of H2O2 on endothelial cell function. Our data demonstrate that hBMSC CM enhances the viability, proliferation, and migration of HUVECs even under oxidative stress conditions. Additionally, the conditioned medium was found to modulate the gene expression of pivotal markers related to inflammation, suggesting a favorable influence on cellular response mechanisms.
Application of Covariance Matrix Adaptation‐Evolutionary Strategy for Modified Constrained Optimal Power Flow Problem Incorporating Valve Point and Emission Effect
Authors: Parthasarathy Hari Krishna Achuthan , Saranathan Madhusudan , S. Tamilselvi , N. Karuppiah , Balachandran Praveen Kumar , C. Dhanamjayulu , Khan Baseem , A. Thamilmaran
Published: July 2024

A prevailing problem in power and energy subsystems is the smooth operation of electric energy systems. This work presents recent, efficient, and reliable evolutionary algorithm for solving the optimal power flow (OPF) analysis. All various practical complex equality and inequality constraints, namely, bus voltages, real powers of the generator buses, tap settings of the transformers and the reactive power generations, shunt compensation, and emission, are considered for the real‐world scenario. Primary feature in a gas power plant that raises a lot of computational shortcomings with nonlinear structure in fuel cost is valve point effect. The existing research works have not factored the valve point effect and lack the accuracy in the fuel cost minimization and do not reflect the various practical complexities such as valve point and emission effects in the OPF problem formulation. This paper, for the first time, introduces modified OPF problem formulation incorporating valve point effect and applies covariance matrix adaptation‐evolution strategy (CMA‐ES) for solving the modified OPF problem. The algorithm is scrutinised and tested on a modified IEEE‐30‐bus platform for various OPF objectives such as cost minimization, transmission loss, and total voltage deviation, subjected to practical constraints. Load flow analysis has been carried out using the Newton–Raphson method. This work aims to lay the foundation in such a way that it can be applicable in a real‐world scenario for any number of buses.
Phenotype‐Genotype Discordance and a Case of a Disorder of Sexual Differentiation
Authors: Snipes Madeline , Stokes Stephanie , Vidalin Amy , Moore Lee D. , Schlabritz-Lutsevich Natalia , Maher James
Published: July 2024

Discordance between the genetic sex and phenotype seen on ultrasound can identify disorders of sexual development (DSD) that previously escaped detection until puberty. We describe a 46, XY disorder of sexual differentiation caused by a rare mutation in the SF1 gene (OMIM]184757, (NR5A1). The mutation (NR5A1)‐c.205C > G (p. Arg69Gly) was discovered after a phenotype‐genotype discrepancy was encountered during prenatal care. The baby with 46, XY DSD has female external genitalia but evidence of Y chromosome‐related regression of Müllerian structures and the absence of palpable gonads. We discussed the literature on phenotype‐genotype discrepancy and the importance of care coordination between the antenatal and postnatal teams to ensure a timely diagnosis of DSD.
Association between Long-term Energy Balance–Related Factors and Survival in Colorectal Cancer Overall and by Metabolic Warburg-Subtypes
Authors: Offermans Kelly , Jenniskens Josien C.A. , Simons Colinda C.J.M. , Samarska Iryna , Fazzi Gregorio E. , Smits Kim M. , Schouten Leo J. , Weijenberg Matty P. , Grabsch Heike I. , van den Brandt Piet A.
Published: July 2024

Background:

Long-term energy balance–related factors (i.e., lifestyle and physiologic factors that influence the equilibrium between energy intake and energy expenditure over an extended period) such as body mass index (BMI) are linked to colorectal cancer risk, but their impact on colorectal cancer survival is unclear. We explored associations between these long-term energy balance–related factors and survival and examined potential differences across metabolic Warburg-subtypes.

Methods:

Associations between long-term energy balance–related factors and survival in the total series of patients with colorectal cancer (n = 2,347) obtained from the prospective Netherlands Cohort Study, as well as according to Warburg-subtype (Warburg-low: n = 652, Warburg-moderate: n = 802, Warburg-high: n = 797), were investigated using Cox regression analysis.

Results:

Among the long-term energy balance–related factors studied, only increasing prediagnostic BMI was associated with a borderline significant poorer overall survival in patients with colorectal cancer [HR5kg/m2, 1.07; 95% confidence interval (CI), 0.99–1.15]. Stratified analyses showed that prediagnostic weight gain (HR5kg, 1.04; 95% CI, 0.99–1.09) and potentially increased height (HR5cm, 1.04; 95% CI, 0.98–1.11) were associated with poor overall survival only in patients with Warburg-high colorectal cancer. Increasing prediagnostic BMI was associated with poor survival only in patients with Warburg-moderate colorectal cancer (colorectal cancer–specific: HR5kg/m2, 1.12; 95% CI, 0.96–1.32; overall: HR5kg/m2, 1.20; 95% CI, 1.05–1.36). No consistent patterns were observed across energy restriction proxies.

Conclusions:

Maintaining a healthy prediagnostic BMI may be beneficial for colorectal cancer survival. Moreover, associations between prediagnostic BMI, weight change, early-life energy restriction, height, and colorectal cancer survival differed according to Warburg-subtypes.

Impact:

Understanding the biologic pathways involved in associations between energy balance–related factors and colorectal cancer survival could help refine prevention strategies in the future.
Targeting AKR1B10 by Drug Repurposing with Epalrestat Overcomes Chemoresistance in Non–Small Cell Lung Cancer Patient-Derived Tumor Organoids
Authors: Suvilesh Kanve N. , Manjunath Yariswamy , Nussbaum Yulia I. , Gadelkarim Mohamed , Raju Murugesan , Srivastava Akhil , Li Guangfu , Warren Wesley C. , Shyu Chi-Ren , Gao Feng , Ciorba Matthew A. , Mitchem Jonathan B. , Rachagani Satyanarayana , Kaifi Jussuf T.
Published: July 2024

Purpose:

Systemic treatments given to patients with non–small cell lung cancer (NSCLC) are often ineffective due to drug resistance. In the present study, we investigated patient-derived tumor organoids (PDTO) and matched tumor tissues from surgically treated patients with NSCLC to identify drug repurposing targets to overcome resistance toward standard-of-care platinum-based doublet chemotherapy.

Experimental Design:

PDTOs were established from 10 prospectively enrolled patients with non-metastatic NSCLC from resected tumors. PDTOs were compared with matched tumor tissues by histopathology/immunohistochemistry, whole exome sequencing, and transcriptome sequencing. PDTO growths and drug responses were determined by measuring 3D tumoroid volumes, cell viability, and proliferation/apoptosis. Differential gene expression analysis identified drug-repurposing targets. Validations were performed with internal/external data sets of patients with NSCLC. NSCLC cell lines were used for aldo-keto reductase 1B10 (AKR1B10) knockdown studies and xenograft models to determine the intratumoral bioavailability of epalrestat.

Results:

PDTOs retained histomorphology and pathological biomarker expression, mutational/transcriptomic signatures, and cellular heterogeneity of the matched tumor tissues. Five (50%) PDTOs were chemoresistant toward carboplatin/paclitaxel. Chemoresistant PDTOs and matched tumor tissues demonstrated overexpression of AKR1B10. Epalrestat, an orally available AKR1B10 inhibitor in clinical use for diabetic polyneuropathy, was repurposed to overcome chemoresistance of PDTOs. In vivo efficacy of epalrestat to overcome drug resistance corresponded to intratumoral epalrestat levels.

Conclusions:

PDTOs are efficient preclinical models recapitulating the tumor characteristics and are suitable for drug testing. AKR1B10 can be targeted by repurposing epalrestat to overcome chemoresistance in NSCLC. Epalrestat has the potential to advance to clinical trials in patients with drug-resistant NSCLC due to favorable toxicity, pharmacological profile, and bioavailability.
Precision Oncology and Systemic Targeted Therapy in Pseudomyxoma Peritonei
Authors: Martínez-Quintanilla Jordi , Cabot Débora , Sabia Doménico , Arqués Oriol ,   for the PMPnet Group, Vergés Jordi , Chicote Irene , Bijelic Lana ,   for the PMPnet Group, Cabellos Laia , Alcántara Anna M. , Ramos Isabel , Barrios Pedro , Crusellas Oriol , Palacio Lina M. ,   for the PMPnet Group, Cámara Juan A. , Barriuso Jorge ,   for the PMPnet Group, Jiménez Juan J. , Muñoz-Torres Pau , Nonell Lara , Flores Raquel , Médico Enzo , Guaglio Marcello ,   for the PMPnet Group, Ros Javier , Élez Elena , Tabernero Josep , Aziz Omer , Deraco Marcello , Palmer Héctor G. ,   for the PMPnet Group,   PMPnet Group, Giovanna Chiorino , Francesco Mazzarotto , Manuela Gariboldi , Luca Varinelli , Tommaso Cavalleri , Bipasha Chakrabarty , Raghavendar Nagaraju , Patrick Caswell , Milly McAllister
Published: July 2024

Purpose:

Pseudomyxoma peritonei (PMP) is a rare and poorly understood malignant condition characterized by the accumulation of intra-abdominal mucin produced from peritoneal metastases. Currently, cytoreductive surgery remains the mainstay of treatment but disease recurrence and death after relapse frequently occur in patients with PMP. New therapeutic strategies are therefore urgently needed for these patients.

Experimental Design:

A total of 120 PMP samples from 50 patients were processed to generate a collection of 50 patient-derived organoid (PDO) and xenograft (PDX) models. Whole exome sequencing, immunohistochemistry analyses, and in vitro and in vivo drug efficacy studies were performed.

Results:

In this study, we have generated a collection of PMP preclinical models and identified druggable targets, including BRAFV600E, KRASG12C, and KRASG12D, that could also be detected in intra-abdominal mucin biopsies of patients with PMP using droplet digital PCR. Preclinical models preserved the histopathological markers from the original patient sample. The BRAFV600E inhibitor encorafenib reduced cell viability of BRAFV600E PMP-PDO models. Proof-of-concept in vivo experiments showed that a systemic treatment with encorafenib significantly reduced tumor growth and prolonged survival in subcutaneous and orthotopic BRAFV600E-PMP-PDX mouse models.

Conclusions:

Our study demonstrates for the first time that systemic targeted therapies can effectively control PMP tumors. BRAF signaling pathway inhibition represents a new therapeutic opportunity for patients with BRAFV600E PMP who have a poor prognosis. Importantly, our present data and collection of preclinical models pave the way for evaluating the efficacy of other systemic targeted therapies toward extending the promise of precision oncology to patients with PMP.
Supplementary Figure S1 from Origins of Second Malignancies in Children and Mutational Footprint of Chemotherapy in Normal Tissues
Authors: Sánchez-Guixé Mònica , Muiños Ferran , Pinheiro-Santin Morena , González-Huici Víctor , Rodriguez-Hernandez Carlos J. , Avgustinova Alexandra , Lavarino Cinzia , González-Pérez Abel , Mora Jaume , López-Bigas Núria
Published: July 2024

Histopathology (H&E) analysis of tumor samples from cases 1, 2, 3 and 4.
Figure 6 from Exploration of Immune-Modulatory Effects of Amivantamab in Combination with Pembrolizumab in Lung and Head and Neck Squamous Cell Carcinoma
Authors: Lim Sun M. , Kang Seong-san , Kim Dong K. , Lee Soo-Hwan , Synn Chun-Bong , Baek Sujeong , Yang Seung M. , Han Yu J. , Kim Mi H. , Han Heekyung , Na Kwangmin , Kim Young T. , Yun Mi R. , Kim Jae H. , Byeon Youngseon , Kim Young S. , Lee Jii B. , Hong Min H. , Curtin Joshua C. , Patel Bharvin , Bergiers Isabelle , Pyo Kyoung-Ho , Cho Byoung C.
Published: July 2024

Upregulation of EGFR and MET in HNSCC PDX (YHIM-3003) tumor induced increased expression of immune checkpoints regulators in the EGFRHIGH/METHIGH subcluster (EMHIGH). A, Volcano plot of top 50 genes in EGFRHIGH/METHIGH against EGFRLOW/METLOW tumor subcluster analyzed by log2 fold change (FC) against P-values. Red dots indicating transcripts with significantly increased fold changes including MET, PD-L1, and MET-regulated genes. B, Expression of STAT-4/PD-L1 (MET, STAT4, CD274), MET-regulated (BACE2, STK40, PRSS23, DPYD, CAV1, S100A4, PYGL), and MET-related immune checkpoints (HAVCR2, CD276) generally increased in the EGFRHIGH/METHIGH tumor subcluster compared to the EGFRLOW/METLOW subcluster. C, Expression of MET-related markers in different treatment groups.
Figure 3 from Exploration of Immune-Modulatory Effects of Amivantamab in Combination with Pembrolizumab in Lung and Head and Neck Squamous Cell Carcinoma
Authors: Lim Sun M. , Kang Seong-san , Kim Dong K. , Lee Soo-Hwan , Synn Chun-Bong , Baek Sujeong , Yang Seung M. , Han Yu J. , Kim Mi H. , Han Heekyung , Na Kwangmin , Kim Young T. , Yun Mi R. , Kim Jae H. , Byeon Youngseon , Kim Young S. , Lee Jii B. , Hong Min H. , Curtin Joshua C. , Patel Bharvin , Bergiers Isabelle , Pyo Kyoung-Ho , Cho Byoung C.
Published: July 2024

Flow cytometry analysis of memory subsets of T cells in the different treatment groups of humanized HNSCC (YHIM-3003) and LUSC (YHIM-2010) PDX tumor of internally sacrificed mice (n = 5 in each treatment group). A, Heatmap of memory T cell subsets (central memory, effector memory, and effector T cells) and activation markers in the tumor samples of HNSCC PDX. B, Heatmap of memory T-cell subsets (central memory, effector memory, and effector T cells) and activation markers in the tumor samples of YHIM-2010. C, Factors that combination of amivantamab and pembrolizumab positively affected in each humanized PDX model and both models shared enhancement of CD8+ T central memory subset by combination therapy. D, Tumor reactive (CEA-stained) CD8 T cells in HNSCC PDX tumor were abundant in the combination treatment group and were significantly higher in proportion compared to the control group (8.28 ± 2.67 and 3.02 ± 0.75, respectively, P < 0.05).
Figure 2 from Exploration of Immune-Modulatory Effects of Amivantamab in Combination with Pembrolizumab in Lung and Head and Neck Squamous Cell Carcinoma
Authors: Lim Sun M. , Kang Seong-san , Kim Dong K. , Lee Soo-Hwan , Synn Chun-Bong , Baek Sujeong , Yang Seung M. , Han Yu J. , Kim Mi H. , Han Heekyung , Na Kwangmin , Kim Young T. , Yun Mi R. , Kim Jae H. , Byeon Youngseon , Kim Young S. , Lee Jii B. , Hong Min H. , Curtin Joshua C. , Patel Bharvin , Bergiers Isabelle , Pyo Kyoung-Ho , Cho Byoung C.
Published: July 2024

Multiplex IHC showing T cell subpopulations in the tumor microenvironment of HNSCC PDX (YHIM-3003) tumor after combination treatment of amivantamab and pembrolizumab. A, Whole slide images of tumor scan showing segmentation of tissues and cells. Helper CD4 and cytotoxic CD8 T cells were marked with FoxP3 (regulatory T cells) and GZMB (active cytotoxic T cells). Cancer cells were stained with Pan-CK. B, Stained whole slide images were quantified and presented in bar plots showing GZMB+ CD8 T cells and regulatory T cells in the tumor microenvironment, tumor nest and stroma in different treatment groups. Proportion of GZMB+ CD8 T cells in the total TME and the tumor nest was significantly increased in the combination group (P < 0.05). Each bar in the bar plot represents five mice that were internally sacrificed.
The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors
Authors: Dravillas Caroline E. , Coleman Samuel S. , Hoyd Rebecca , Caryotakis Griffin , Denko Louis , Chan Carlos H.F. , Churchman Michelle L. , Denko Nicholas , Dodd Rebecca D. , Eljilany Islam , Hardikar Sheetal , Husain Marium , Ikeguchi Alexandra P. , Jin Ning , Ma Qin , McCarter Martin D. , Osman Afaf E.G. , Robinson Lary A. , Singer Eric A. , Tinoco Gabriel , Ulrich Cornelia M. , Zakharia Yousef , Spakowicz Daniel , Tarhini Ahmad A. , Tan Aik Choon ,   for the exORIEN Consortium
Published: July 2024

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and its potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICI). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA sequencing (RNA-seq) was conducted on the formalin-fixed, paraffin-embedded and fresh frozen tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival ≥24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The age of the 71 patients with metastatic melanoma ranged from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy-responsive versus nonresponsive tumors. Responders showed significant enrichment of bacteriophages in the phylum Uroviricota, and nonresponders showed enrichment of several bacteria, including Campylobacter jejuni. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.

Significance:

We analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.
Review of image processing and artificial intelligence methodologies for apple leaf disease diagnosis
Authors: Tabassum Husna , Theerthagiri Prasannavenkatesan
Published: July 2024

Apple leaf disease (ALD) potentially affects the apple tree's health by reducing fruit yield and its capability to grow healthy. The prime purpose of the proposed study is to review and assess the strengths and weaknesses associated with the frequently exercised methods of ALD diagnosis using image processing and artificial intelligence (AI). Although these are widely adopted in recent studies, the core notion is to find the pros and cons associated with the practical viability. A desk research methodology is undertaken to carry out proposed review work where a database of recent scientific manuscripts is collected and studied very closely. The existing approaches are reviewed concerning identified problems, adopted solutions, advantages, and limitations. Finally, the paper contributes towards offering insight into potential research gap which will guide the upcoming researchers to make wise decisions for planning their models. The results acquired from this review work show that generalized challenges of ALD are not addressed, less emphasis on illumination variability, reduced target to minimize complexity, lesser evidence towards real-time processing, no evidence towards interpretability, limitation of available dataset, and tradeoff-between image processing and AI.
Hyperspectral image classification with spectral-spatial feature integration and ensemble learning
Authors: N Bhavatarini , Prashant Jyothi Aracot
Published: July 2024

Hyperspectral imaging (HSI) has emerged as a robust remote sensing and medical imaging tool. However, HSI classification remains a challenging problem due to the high-dimensional data and the need for efficient feature selection and enhancement techniques. The proposed work addresses the problem of spatial feature extraction in spectral-spatial HSI classification tasks. This paper introduces an innovative model addressing the intricacies of spatial feature extraction in spectral-spatial HSI classification tasks, employing a fusion of spectral and spatial features through an adaptive kernel-based Gaussian filtering mechanism to elevate the quality of HSI data and augment classification performance. The classification is executed using three distinct classifiers, whose decisions are harmoniously integrated within an ensemble learning framework to optimize outcomes. The effectiveness of the proposed system is meticulously evaluated across three diverse datasets, Indian Pine, Pavia, and Salinas. This study also compares the model's efficiency against the existing similar work presented in the literature. The results show that the proposed work outperforms existing methods with constantly showing 99% accuracy and kappa score for each dataset, demonstrating its potential applications in various domains such as remote sensing and medical imaging.
Seismic trend analysis: a data mining approach for pattern prediction
Authors: Andrade Arenas Laberiano , Yactayo-Arias Cesar
Published: July 2024

In the global context, seismic movements represent a constant for the population due to geophysical variability and other factors that make them possible, carrying with them the risk of losing innocent lives. The main purpose of our research is to apply data mining techniques to prevent seismic events of any magnitude to anticipate and mitigate future events. In the development of the research, we applied knowledge discovery database methodology. The clustering analysis results revealed the following: cluster 0 encompassed 45 items, with average magnitude of 0.230, representing 15.5% of the total events. Cluster 1 comprised 56 items with average magnitude of 0.156, equivalent to 19.2% of the total. Cluster 2, the largest, consisted of 94 items with average magnitude of 0.156, constituting 32.3% of the total seismic events. Cluster 3 was composed of 54 items, with average magnitude of 0.155, representing 18.3% of the total. Lastly, cluster 4 included 42 items, with average magnitude of 0.155, representing 14.5% of the total. In conclusion, cluster 3 emerged as the most significant, with 94 events and average magnitude of 0.141, equivalent to 32.3% of the total seismic events. This discovery underscores the need to utilize data mining techniques for earthquake prediction, enabling proactive measures against potential events, which are frequent in various geographic areas.
Stand-off concealed firearm detection using motion tracking and convolutional neural networks
Authors: Muriithi Henry Muchiri , Lukandu Ateya Ismail , Wanyembi Gregory
Published: July 2024

The standoff detection of concealed firearms is crutial in managing public security in public spaces. Currently employed standoff concealed weapon detection techniques employ electromagnetic wave imaging which has been found to be extremely slow and may require expensive hardware and may not be applicable in public open spaces. Inorder to maintain safety in open spaces, artificial intelligence enabled video surveillance systems have been widely adopted. This poses an opportunity to explore video surveillance cameras as concealed weapon detectors. A review of existing video surveillance based automated weapon detection approaches discovered that the focus was on the detection of unconcealed firearms leaving a gap in the detection of concealed firearms. This study addresses the aforementioned gap by providing a standoff concealed firearm detection approach on video based on skeletal-based human motion tracking and convolutional neural networks. The motion of armed and unarmed persons was tracked using a depth camera and further classified using convolutional neural networks model. The developed model reported 100% accuracy, precision and recall scores. These results outperformed results obtained from traditional machine learning models therefore highlighting the superior capability of the proposed approach for concealed firearm detection on video to complement the efforts of human video surveillance operators.
An innovative approach for detecting buildings and construction anomalies in Zenata City
Authors: Ait Moulay Maryem , Salbi Adil , Bouganssa Issam , Masmoudi Mohamed-Salim , Lasfar Abdelali
Published: July 2024

Rapid urban development in Morocco has led to increased construction activities and significant environmental concerns. Recently Zenata city has undergone significant urban development, marking a crucial step in its trajectory toward a modern smart city. As a part of this growth, our research incorporates an innovative method within the You Only Look Once version 8 (YOLOv8) model, representing a significant advance over conventional methods. The YOLO algorithm has been updated with new features and improvements that infuse our work with a dash of innovation. YOLOv8 integration improves construction and irregular construction detection accuracy beyond what is possible with traditional applications. We trained our algorithm using orthophoto captured by DJI MATRICE 300 RTK drone split into georeferenced tiles and annotated using LabelImg software. Through this process, we were able to create a solid 742 image dataset for training, testing, and validation purposes related to construction. Utilizing drone imagery and the YOLOv8 object detection algorithm, buildings and construction irregularities are detected with high accuracy after 300 training epochs on Kaggle's GPU P100. Insights for early detection and effective building site management are provided by this all-encompassing strategy, which supports Zenata City's sustainable urban growth.

 
Anamoly based intrusion detection using ensemble machine learning and block-chain
Authors: Mekala Srinivasa Rao , Nazma Shaik , Nava Chaitanya Kumbhagiri , Ambica Thota
Published: July 2024

A major issue facing the quickly evolving technological world is the surge in security concerns, particularly for critical Internet-of-Things (IoT) applications like health care and the military. Early security attack detection is crucial for safeguarding important resources. Our research focuses on developing an anomaly-based intrusion detection system (IDS) using machine learning (ML) models. With the use of voting strategies, Bagging Ensemble, Boosting Ensemble, and Random Forest, we created a robust and long-lasting IDS. The F1 score is a crucial metric for measuring accurate predictions at the class level and serves as the focus of these ML systems. Maintaining a high F1 score in critical applications highlights the constant need for development. Make use of the latest CICIoT2023 data-set employ Hyper-ledger Fabric to create a private channel in order to bolster the security of our IDS through the usage of block-chain technology. We use block-chain's immutable record and cryptographic techniques to establish a decentralized, tamper-proof environment. Consequently, our proposed approach provides an efficient intrusion detection system that significantly enhances resource protection and alerting the user in prior with intruder information   incritical regions for Internet of Things security applications.
Automated detection of kidney masses lesions using a deep learning approach
Authors: ALMahadin Ghayth , Abu Owida Hamza , Al Nabulsi Jamal , Turab Nidal , Al Hawamdeh Nour
Published: July 2024

Deep learning has emerged as a potent tool for various tasks, such as image classification. However, in the medical domain, there exists a scarcity of data, which poses a challenge in obtaining a well-balanced and high-quality dataset. Commonly seen issues in the realm of renal health include conditions such as kidney stones, cysts, and tumors. This study is centered on the examination of deep learning models for the purpose of classifying renal computed tomography (CT)-scan pictures. State-of-the-art classification models, such as convolutional neural network (CNN) approaches, are employed to boost model performance and improve accuracy. The algorithm is comprised of six convolutional layers that progressively increase in complexity. Every layer in the network utilizes a uniform 3x3 kernel size and applies the rectified linear unit (ReLU) activation function. This is followed by a max-pooling layer that downsamples the feature maps using a 2x2 pool size. Following this, a flatten layer was implemented in order to preprocess the data for the fully linked layers. The consistent utilization of uniform kernel sizes and activation functions throughout all layers of the model facilitated the smooth extraction of complex features, thereby enhancing the model’s ability to accurately identify different kidney conditions. As a result, we achieved a high accuracy rate of 99.8%, precision is 99.8%, and F1 score of approximately 99.7%.
A new system for underwater vehicle balancing control based on weightless neural network and fuzzy logic methods
Authors: Zarkasi Ahmad , Satria Hadipurnawan , Primanita Anggina , Abdurahman Abdurahman , Afifah Nurul , Sutarno Sutarno
Published: July 2024

The utilization of humans to be in the water for short time, resulting in limited area underwater that can be explored, so the information obtained is very limited, plus the influence of irregular water movements, changes in waves, and changes in water pressure, indirectly also constitutes obstacle to this problem. One of the best solutions is to develop underwater vessel that can travel either autonomously or by giving control of movement and navigation systems. New system for underwater vehicle balance control through weightless neural network (WNN) and fuzzy logic methods was proposed in this study. The aim was to simplify complicated data source on stability system using WNN algorithm and determine depth level of autonomous underwater vehicle (AUV) through fuzzy logic method. Moreover, speed control of underwater vehicle was determined using fuzzy rule-based design and inference. The tests were conducted by showing convergence performance of system in the form of AUV simulator. The results showed that proposed system could produce real-time motion balance performance, faster execution time, and good level of accuracy. This study was expected to produce real-time motion balance system with better performance, faster execution time, and good level of accuracy which could be subsequently used to design simple, cheap, and efficient hardware prototype.
Computer vision that can ‘see’ in the dark
Authors: Goh Shi Yong , Wong Yan Chiew  , Ahmad Radzi Syafeeza , Sarban Singh Ranjit Singh
Published: July 2024

Insufficient lighting environment has raised challenges for night shift workers’ safety monitoring. Thus, we have developed a computer vision-based algorithm recognizing 11 actions based on action recognition in dark (ARID) dataset. A hybrid model of integrating convolutional neural network (CNN) into YOLOv7 has been proposed. YOLOv7 is an algorithm designed for real-time object detection in image or video, for fast and accurate detection in applications such as autonomous vehicles and surveillance systems. In this work, video in dark environment has first been enhanced using CNN algorithm before feeding into YOLOv7 network for activity recognition. Adaptive gamma intensity correction (GIC) has been integrated to further improving the overall result. The proposed model has been evaluated over different enhancement modes. The proposed model is able to handle dark video frames with 74.95% Top-1 accuracy with fast processing speed of 93.99 ms/frame on a 4 GB RTX 3050 graphical processing unit (GPU) and 17.59 ms/frame on 16 GB Tesla T4 GPU. The base size of the proposed model is tiny, only 74.8 MB, but with 36.54 M of total parameters indicating that it has more capacity to learn more meaningful information with limited hardware resources.
Machine learning-based decision-making approach for predicting defects detection: a case study
Authors: Barzizza Elena , Biasetton Nicolo , Ceccato Riccardo , Molena Alberto
Published: July 2024

In today’s highly competitive global market, industries must produce faultless products to achieve profitability. Machine learning (ML) algorithms provide a possible method to improve quality standards by enabling the prediction of the outcome of quality control processes. This article presents a real case study based on ML algorithms suggested to develop a knowledge-based intelligent supervisory system to predict defect products in the fashion industry. Defect detection is formulated as a binary classification problem, and several ML algorithms have been compared to determine the most suitable one on the available data. The random forest (RF), LightGBM, and C5.0 algorithms exhibit comparable high-end performances on the pre-processed dataset made available by the company. Nevertheless, since the aim of the analysed industry is to reduce the rate of false negative observations (i.e., the proportion of defected-free products wrongly classified), the best method results is RF, as it minimizes this metric.
Efficient autonomous navigation for mobile robots using machine learning
Authors: Waga Abderrahim , Ba-ichou Ayoub , Benhlima Said , Bekri Ali , Abdouni Jawad
Published: July 2024

The ability to navigate autonomously from the start to its final goal is the crucial key to mobile robots. To ensure complete navigation, it is mandatory to do heavy programming since this task is composed of several subtasks such as path planning, localization, and obstacle avoidance. This paper simplifies this heavy process by making the robot more intelligent. The robot will acquire the navigation policy from an expert in navigation using machine learning. We used the expert A*, which is characterized by generating an optimal trajectory. In the context of robotics, learning from demonstration (LFD) will allow robots, in general, to acquire new skills by imitating the behavior of an expert. The expert will navigate in different environments, and our robot will try to learn its navigation strategy by linking states and suitable actions taken. We find that our robot acquires the navigation policy given by A* very well. Several tests were simulated with environments of different complexity and obstacle distributions to evaluate the flexibility and efficiency of the proposed strategies. The experimental results demonstrate the reliability and effectiveness of the proposed method.
An ensemble features aware machine learning model for detection and staging of dyslexia
Authors: Mulakaluri Sailaja , Gowdra Shivappa Girisha
Published: July 2024

Dyslexia is a specific learning disorder (SLD) which may affect young child's cognitive skills, text comprehension, reading-writing and also problemsolving abilities. To diagnose and identify dyslexia, the testing scale tool has been proposed using artificial intelligence technique. The proposed tool allows the student who is suspected to have dyslexia to take up quiz and perform certain task based on the type of learning impairments. After completion of the test, resultant data is provided as input to the proposed ensemble feature aware machine-learning (EFAM) XGBoost (XGB) model. Based on the student assessment score and time taken by children, the EFAMXGB algorithm predicts dyslexia. The proposed EFAM-XGB is used to develop an integrated and user-friendly tool that is highly accurate in identifying reading disorders even with presence of realistic imbalanced dataset and suggest the most appropriate instructional activities to parents and teachers. The EFAM-XGB-based dyslexia detection method achieves very good accuracy of 98.7% for dyslexia dataset; thus, attain better performance in comparison with existing machine learning (ML)-based methodologies.
Smart agriculture model in detecting oil palm plantation diseases using a convolution neural network
Authors: Gunawan Gunawan , Zarlis Muhammad , Sihombing Poltak , Sutarman Sutarman
Published: July 2024

Planning models for sustainable crop care in the context of smart agriculture are complex issues as they involve many factors such as productivity, quality, growth sustainability, workforce use, and information technology use. In this study, we will create an optimized model using a convolution neural network (CNN) that can classify and monitor plant diseases. Part of the plant care system is to be aware of plant diseases and to be able to deal with them immediately. This study aims to acquire a new smart farming model for integrated crop care. The results of this research are findings in the form of a CNN model for classifying plant diseases detected from the leaves of the plants studied in oil palm. Testing using Google Colab obtains 100% accuracy and 99% accuracy using a teachable machine. The contributions of this paper create a new model in the field of informatics, especially in the field of intelligent agriculture based on information technology.
Computer aided detection for vertebral deformities diagnosis based on deep learning
Authors: OUNASSER Nabila , Rhanoui Maryem , Mikram Mounia , El Asri Bouchra
Published: July 2024

The diagnosis of spinal deformities is one of the most frequent daily clinical routine. X-ray images are used to diagnose several pathologies in order to reduce harmful radiations of the patient. Spinal deformities are diagnosed essentially from vertebral shapes, orientations, and positions, so their detection and segmentation are major steps required for diagnosis. Deep learning could be applied for automatic diagnosis to detect scoliosis and its variants with a favourable performance. In this study, based on 609 spinal anterior-posterior x-ray images obtained from the public SpineWeb, we examine generative ad- versarial network (GAN) based architectures and convolutional neural network (CNN) based architectures models that are capable of automatically detecting anomalies in radiograph and achieve expert-level performances in various fields providing a solid comparative study. Most of the implemented models are apt to automatically distinguish limits between vertebrae so determining their shape with a very good visual performance. The GAN-based architecture estimates the required vertebral landmarks with an accuracy rate of 0.966, signify its capacity for automatic scoliosis assessment in a clinical setting.
Enhanced you only look once approach for automatic phytoplankton identification
Authors: Wisnu Ardhi Ovide Decroly , Retnaningsih Soeprobowati Tri , Adi Kusworo , Prakasa Esa , Rachman Arief
Published: July 2024

Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of ACVIT-YOLO compared to the YOLOv8m model, further demonstrated by the decreased giga floating-point operations (GFLOP), decreased parameter count, and compact dimensions, significantly improves the automation of phytoplankton species identification. This suggests that the ACVIT-YOLO model could produce a better prediction system for identifying phytoplankton with similar accuracy to the original YOLOv8m model but with lower computational power and resource usage.
The incorporation of stacked long short-term memory into intrusion detection systems for botnet attack classification
Authors: Heryanto Ahmad , Stiawan Deris , Hermansyah Adi , Firnando Rici , Pertiwi Hanna , Bin Idris Mohd Yazid , Budiarto Rahmat
Published: July 2024

Botnets are a common cyber-attack method on the internet, causing infrastructure damage, data theft, and malware distribution. The continuous evolution and adaptation to enhanced defense tactics make botnets a strong and difficult threat to combat. In light of this, the study's main objective was to find out how well techniques like principal component analysis (PCA), synthetic minority oversampling technique (SMOTE), and long short-term memory (LSTM) can help find botnet attacks. PCA shows the ability to reduce the feature dimensions in network data, allowing for a more efficient and effective representation of the patterns contained. The SMOTE addresses class imbalances in the dataset, enhancing the model's ability to recognize suspicious activity. Furthermore, LSTM classifies sequential data, understanding complex network patterns and behaviors often used by botnets. The combination of these three methods provided a substantial improvement in detecting suspicious botnet activities. We also evaluated the effectiveness using performance metrics such as accuracy, precision, recall, and F1-score. The results showed an accuracy of 96.77%, precision of 88.95%, recall of 88.58%, and F1-score of 88.64%, indicating that the proposed model was reliable in detecting botnet traffic compared to other deep learning models. Furthermore, LSTM can classify sequential data, understanding complex network patterns and behaviors often used by botnets.