bluuebunny/crossref_metadata_2025 huggingface dataset:
Data from The ω-3 Polyunsaturated Fatty Acid Docosahexaenoic Acid Enhances NK-Cell Antitumor Effector Functions
Authors: Wu Shuting , Peng Hongyan , Li Songyang , Huang Lanlan , Wang Xiangyu , Li Yana , Liu Yongjie , Xiong Peiwen , Yang Qinglan , Tian Kunpeng , Wu Weiru , Pu Rongxi , Lu Xiulan , Xiao Zhenghui , Yang Jian , Zhong Zhaoyang , Gao Yuan , Deng Yafei , Deng Youcai
Published: June 2024

Abstract
ω-3 polyunsaturated fatty acids (PUFA) are known to directly repress tumor development and progression. In this study, we explored whether docosahexaenoic acid (DHA), a type of ω-3 PUFA, had an immunomodulatory role in inhibiting tumor growth in immunocompetent mice. The number of natural killer (NK) cells but not the number of T or B cells was decreased by DHA supplementation in various tissues under physiologic conditions. Although the frequency and number of NK cells were comparable, IFNγ production by NK cells in both the spleen and lung was increased in DHA-supplemented mice in the mouse B16F10 melanoma tumor model. Single-cell RNA sequencing revealed that DHA promoted effector function and oxidative phosphorylation in NK cells but had no obvious effects on other immune cells. Using Rag2−/− mice and NK-cell depletion by PK136 antibody injection, we demonstrated that the suppression of B16F10 melanoma tumor growth in the lung by DHA supplementation was dependent mainly on NK cells. In vitro experiments showed that DHA directly enhanced IFNγ production, CD107a expression, and mitochondrial oxidative phosphorylation (OXPHOS) activity and slightly increased proliferator-activated receptor gamma coactivator-1α (PGC-1α) protein expression in NK cells. The PGC-1α inhibitor SR-18292 in vitro and NK cell–specific knockout of PGC-1α in mice reversed the antitumor effects of DHA. In summary, our findings broaden the current knowledge on how DHA supplementation protects against cancer growth from the perspective of immunomodulation by upregulating PGC-1α signaling–mediated mitochondrial OXPHOS activity in NK cells.
Data from Intratumoral TREX1 Induction Promotes Immune Evasion by Limiting Type I IFN
Authors: Toufektchan Eléonore , Dananberg Alexandra , Striepen Josefine , Hickling James H. , Shim Abraham , Chen Yanyang , Nichols Ashley , Duran Paez Mercedes A. , Mohr Lisa , Bakhoum Samuel F. , Maciejowski John
Published: June 2024

Abstract
Chromosomal instability is a hallmark of human cancer that is associated with aggressive disease characteristics. Chromosome mis-segregations help fuel natural selection, but they risk provoking a cGAS-STING immune response through the accumulation of cytosolic DNA. The mechanisms of how tumors benefit from chromosomal instability while mitigating associated risks, such as enhanced immune surveillance, are poorly understood. Here, we identify cGAS-STING–dependent upregulation of the nuclease TREX1 as an adaptive, negative feedback mechanism that promotes immune evasion through digestion of cytosolic DNA. TREX1 loss diminishes tumor growth, prolongs survival of host animals, increases tumor immune infiltration, and potentiates response to immune checkpoint blockade selectively in tumors capable of mounting a type I IFN response downstream of STING. Together, these data demonstrate that TREX1 induction shields chromosomally unstable tumors from immune surveillance by dampening type I IFN production and suggest that TREX1 inhibitors might be used to selectively target tumors that have retained the inherent ability to mount an IFN response downstream of STING.

See related article by Lim et al., p. 663
Data from The Exonuclease TREX1 Constitutes an Innate Immune Checkpoint Limiting cGAS/STING-Mediated Antitumor Immunity
Authors: Lim Junghyun , Rodriguez Ryan , Williams Katherine , Silva John , Gutierrez Alan G. , Tyler Paul , Baharom Faezzah , Sun Tao , Lin Eva , Martin Scott , Kayser Brandon D. , Johnston Robert J. , Mellman Ira , Delamarre Lélia , West Nathaniel R. , Müller Sören , Qu Yan , Heger Klaus
Published: June 2024

Abstract
The DNA exonuclease three-prime repair exonuclease 1 (TREX1) is critical for preventing autoimmunity in mice and humans by degrading endogenous cytosolic DNA, which otherwise triggers activation of the innate cGAS/STING pathway leading to the production of type I IFNs. As tumor cells are prone to aberrant cytosolic DNA accumulation, we hypothesized that they are critically dependent on TREX1 activity to limit their immunogenicity. Here, we show that in tumor cells, TREX1 restricts spontaneous activation of the cGAS/STING pathway, and the subsequent induction of a type I IFN response. As a result, TREX1 deficiency compromised in vivo tumor growth in mice. This delay in tumor growth depended on a functional immune system, systemic type I IFN signaling, and tumor-intrinsic cGAS expression. Mechanistically, we show that tumor TREX1 loss drove activation of CD8+ T cells and NK cells, prevented CD8+ T-cell exhaustion, and remodeled an immunosuppressive myeloid compartment. Consequently, TREX1 deficiency combined with T-cell–directed immune checkpoint blockade. Collectively, we conclude that TREX1 is essential to limit tumor immunogenicity, and that targeting this innate immune checkpoint remodels the tumor microenvironment and enhances antitumor immunity by itself and in combination with T-cell–targeted therapies.

See related article by Toufektchan et al., p. 673
Data from Engineering CD3/CD137 Dual Specificity into a DLL3-Targeted T-Cell Engager Enhances T-Cell Infiltration and Efficacy against Small-Cell Lung Cancer
Authors: Mikami Hirofumi , Feng Shu , Matsuda Yutaka , Ishii Shinya , Naoi Sotaro , Azuma Yumiko , Nagano Hiroaki , Asanuma Kentaro , Kayukawa Yoko , Tsunenari Toshiaki , Kamikawaji Shogo , Iwabuchi Ryutaro , Shinozuka Junko , Yamazaki Masaki , Kuroi Haruka , Ho Samantha Shu Wen , Gan Siok Wan , Chichili Priyanka , Pang Chai Ling , Yeo Chiew Ying , Shimizu Shun , Hironiwa Naoka , Kinoshita Yasuko , Shimizu Yuichiro , Sakamoto Akihisa , Muraoka Masaru , Takahashi Noriyuki , Kawa Tatsuya , Shiraiwa Hirotake , Mimoto Futa , Kashima Kenji , Kamata-Sakurai Mika , Ishikawa Shumpei , Aburatani Hiroyuki , Kitazawa Takehisa , Igawa Tomoyuki
Published: June 2024

Abstract
Small-cell lung cancer (SCLC) is an aggressive cancer for which immune checkpoint inhibitors (ICI) have had only limited success. Bispecific T-cell engagers are promising therapeutic alternatives for ICI-resistant tumors, but not all patients with SCLC are responsive. Herein, to integrate CD137 costimulatory function into a T-cell engager format and thereby augment therapeutic efficacy, we generated a CD3/CD137 dual-specific Fab and engineered a DLL3-targeted trispecific antibody (DLL3 trispecific). The CD3/CD137 dual-specific Fab was generated to competitively bind to CD3 and CD137 to prevent DLL3-independent cross-linking of CD3 and CD137, which could lead to systemic T-cell activation. We demonstrated that DLL3 trispecific induced better tumor growth control and a marked increase in the number of intratumoral T cells compared with a conventional DLL3-targeted bispecific T-cell engager. These findings suggest that DLL3 trispecific can exert potent efficacy by inducing concurrent CD137 costimulation and provide a promising therapeutic option for SCLC.
FIGURE 7 from B7-H3–Targeting Chimeric Antigen Receptors Epstein-Barr Virus–specific T Cells Provides a Tumor Agnostic Off-The-Shelf Therapy Against B7-H3–positive Solid Tumors
Authors: Yeo Siok Ping , Kua Lindsay , Tan Jin Wei , Lim Joanna Kristyn , Wong Fiona HS , Santos May Delos , Poh Chek Meng , Goh Angeline XH , Koh Xin Yu , Zhou Xiaohua , Rajarethinam Ravisankar , Chen Qingfeng , Her Zhisheng , Horak Ivan D. , Low Lionel , Tan Kar Wai
Published: June 2024

In vivo targeting of allogeneic myeloid cells by B7H3.CAR EBVSTs is linked to a lower risk of CRS. A, IVIS monitoring was performed to track tumor progression. Bioluminescence images of tumor burden in mice injected intravenously with NALM-6 (left) or HT-29 (right) at days −1, 2, and 5 posttreatment. B, Average tumor burden represented in mice of each treatment group injected with NALM-6 (top) or HT-29 (bottom). Body weight measurements of NALM-6 (C) or HT-29-injected (D) mice posttreatment. E, Serum levels of human cytokines and chemokines at 3 days posttreatment as quantified by multiplex bead immunoassay. F, Total counts of dendritic cells (top), monocytes (middle), and neutrophils (bottom) in blood, bone marrow, spleen, and liver of each treatment groups quantified by flow cytometry. G, B7-H3 expression on dendritic cells (top), monocytes (middle), and neutrophils (bottom) in blood, bone marrow, spleen, and liver after treatment, quantified by flow cytometry. Data comprise of up to 6–9 mice in each treatment arm. For B–D, groups were compared using two-way ANOVA with Tukey test for multiple comparisons test. Comparison and P values between untreated versus UT T, untreated versus B7H3.CAR EBVSTs and UT T versus B7H3.CAR EBVSTs groups is represented in black, blue, and red, respectively. For E–G, groups were compared using one-way ANOVA with Tukey test for multiple comparisons test. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, not significant. Each point on the graph represents a mouse and error bars denote means ± SD where applicable.
Statistical analysis for chemical compound based on several species of aquilaria essential oil
Authors: Ahmad Sabri Noor Aida Syakira , Nik Kamaruzaman Nik Fasha Edora , Ismail Nurlaila , Yusoff Zakiah Mohd , Almisreb Ali Abd , Tajuddin Saiful Nizam , Taib Mohd Nasir
Published: June 2024

The paper examines the characterization of Aquilaria essential oils from different species, namely Aquilaria malaccensis, Aquilaria beccariana, Aquilaria crassna, and Aquilaria subintegra, renowned for agarwood production in Malaysia. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID) were employed for extracting essential oil data, facilitating compound identification. Subsequently, a preliminary analysis focused on classifying significant chemical compounds in the samples. The study then utilized boxplot pre-processing for visualizing and interpreting data distribution. The statistical analyses were performed using MATLAB software version R2021b, considering two key parameters which are the peak area (%) of significant chemical compounds and the classification of Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) based on their chemical composition. The results, presented through boxplot analyses, demonstrated a clear representation of the parameters and their distribution in the data. This method not only confirmed the potential of boxplot analysis in statistical evaluation of significant compounds in Aquilaria essential oil but also suggested its applicability for further classification work.
Energy savings by adapting consumer behavior in grid-connected photovoltaic systems with battery storage
Authors: Andam Meriem , El Alami Jamila , Louartassi Younes , Zine Rabie
Published: June 2024

Today, the world faces many energy challenges that make the use of clean energy an obligation and an emergency. These challenges require changes in a wide range of sectors, including transport, industry and residential areas. At present, energy production is responsible for a large amount of greenhouse gas emissions, the main cause of climate change with its various dangerous effects on human life. Therefore, a change towards non-carbon energy is becoming a necessity. Certainly, this evolution has been underway for years and the renewable energy market has developed in a surprisingly efficient way. However, the current situation has reached an alarming state and requires the active participation of all stakeholders, especially consumers. Therefore, the main objective of this paper is to illustrate how consumer behavior can significantly influence and contribute to the optimization of renewable energy systems, especially photovoltaic systems. The paper emphasizes the beneficial integration of batteries and storage systems to achieve energy savings. The results show that with some adjustments in daily behavior, an overall energy saving of 62% can be achieved compared to the normal consumption scenario: the energy obtained from the grid and then the electricity bill is reduced.
An improved rule-based control of battery energy storage for hourly power dispatching of photovoltaic sources
Authors: Jusoh Mohd Afifi , Ibrahim Mohd Zamri , Daud Muhamad Zalani
Published: June 2024

This paper presents an improved rule-based control scheme for battery energy storage (BES) system with the goal of minimising the fluctuation output from photovoltaic (PV) sources while ensuring the operational constraints of BES are regulated at the specified ranges for safety purposes. The control scheme is formulated in accordance with the intended operational limitations of the BES, including charge/discharge current limits and state-of-charge (SOC). The simulation studies were carried out using MATLAB/Simulink to evaluate the effectiveness of an improved rule-based control scheme on the 1.2 MW PV system data acquired from a location in Malaysia. Furthermore, a comparative study on the effectiveness of an improved rule-based control scheme compared with the conventional rule-based control scheme has been carried out. The simulation results show that an improved rule-based control scheme can effectively reduce the fluctuations in the output power of the PV sources and dispatch the output to the utility grid on an hourly basis with an efficiency of up to 94.47%. Finally, the comparison results on the effects of the BESS capacity also illustrate that an improved rule-based control scheme is more effective compared to the conventional rule-based control scheme in the previous study.
Efficient rectifier with wide input power range for 5G applications
Authors: Yamna Mounira Ben , Dakhli Nabil , Sakli Hedi , Aoun Mohamed
Published: June 2024

This article presents three efficient rectifiers for radio frequency energy harvest-ing (RFEH) systems operating at the fifth generation (5G) band (3.5 GHz). Eachrectifier operates at various input power levels (high, low, and across a widepower range). The high and low-power rectifiers feature a single serial topologyusing HSMS-2860 and SMS-7630 Schottky diodes, respectively, along with mi-crostrip lines to implement the input and output filters and the impedance match-ing network. At an radio frequency (RF) power level of 15 dBm, the high-powerrectifier harvests 67.4% to direct current (DC) power with a 300Ωload resistorand an output voltage of 2.5V. The low-power rectifier achieves its maximumpower conversion efficiency (PCE) at -2 dBm, reaching 45% efficiency with a1200Ωload. The rectifier with a extended input power range comprises twobranches of subrectifiers functioning at both high and low power levels. De-pending on the power level, the considered subrectifier harvests radio frequencypower into DC power, while the other subrectifier is deactivated. Across a powerspan of 32.5 dB (ranging from -13 to 19.5 dBm), the rectifier maintains an effi-ciency above 30%. The proposed rectifiers are efficient and suitable for imple-mentation in 5G-enabled RFEH systems.
An 8-bit successive-approximation register analog-to-digital converter operating at 125 kS/s with enhanced comparator in 180 nm CMOS technology
Authors: Zghoul Fadi Nessir , Al-Bakrawi Yousra Hussein , Etier Issa , Kannan Nithiyananthan
Published: June 2024

Data converters are necessary for the conversion process of analog and digital signals. Successive approximation register (SAR) analog-to-digital converters (ADC) can achieve high levels of accuracy while consuming relatively low amounts of power and operating at relatively high speeds. This paper describes a design of 8-bit 125 kS/s SAR ADC with a proposed high-speed comparator design based on dynamic latch architecture. The proposed design of the comparator enhances the performance compared to a conventional dynamic comparator by adding two parallel clocked input complementary metal-oxide semiconductor (CMOS) transistors which reduce the parasitic resistance in the latch ground path and serve to minimize the latch delay time. The design of each sub-system for the ADC is explained thoroughly, which contains a sample and hold circuit, successive approximation register, charge redistribution types digital-to-analog converter, and the new proposed comparator. The proposed design is implemented using 180 nm CMOS technology with a power supply of 1.2 V. The average inaccuracy in differential non-linearity (DNL) is +0.6/−0.8 LSB (least significant bit), and integral non-linearity (INL) is +0.4/−0.7 LSB. The proposed design exhibits a delay time of 157 ps at 1 MHz clock frequency.
Robust identification of users by convolutional neural network in MATLAB and Raspberry Pi
Authors: Murillo Paula Useche , Jiménez-Moreno Robinson , Baquero Javier Eduardo Martinez
Published: June 2024

The following article presents the development of an algorithm embedded in a Raspberry Pi 3B board, where a user identification was made, using the convolutional neural network (CNN) for 5 predefined users, with the option of loading remotely a new network for a new user. Comparatively, the same application was programmed in MATLAB programming software to evaluate the results and identify the advantages between them. Networks were trained for 5 different users, using the Caffe library on the Raspberry Pi, and the MATLAB neural network package on the computer. Where it was found that the training made by Caffe on an embedded system is much slower and less efficient than the ones performed in MATLAB, obtaining less than 55% accuracy with Caffe networks and more than 90% with MATLAB networks, training with the same number of samples, the same architecture, and the same database. Finally, the accuracy obtained through confusion matrix is over 88% in each case of users identification.
Contactless logging and disinfection solution with automated face mask detection
Authors: Bolima Dominic , Huerto Christian Albert
Published: June 2024

Coronavirus disease-19 (COVID-19) mitigation includes health screening procedures at entrances of public and private establishments. Conventional methods include manual operations on temperature and face mask checking, personal identification, and hand disinfection. In this paper, a gateway kiosk is designed by integrating and automating the primary screening procedures. It uses radio-frequency identification (RFID) for contactless access control and attendance, with an infrared thermometer for body temperature monitoring. Face mask detection is automated through artificial intelligence (AI), while proximity sensors activate the disinfection system. Internet-of-things (IoT) interfaces these subsystems, and local access is available via an Internet browser. RFID overcomes the slower response rate of the quick response (QR) code-based solution. The repeated-measure analyses showed that the system’s thermometer has only +0.28 °C error while its residual neural network-10 (ResNet-10) and MobileNetV2 models for detecting masked faces achieved a 98.2% accuracy. The system reduces the primary key performance indicators–service and queuing times by 56.86% and 79.95%, respectively. Its audio and visual notifications ensure the proper screening implementation, thus reducing unnecessary and risky interactions with entrance personnel. It improves the screening procedures by significantly reducing human interactions and enhancing queuing.
Content based COVID-19 image retrieval system using local histogram equalization and deep convolutional neural network
Authors: Shetty Rani , S. Bhat Vandana , Pujari Jagadeesh , Shetty Rashmi
Published: June 2024

Doctors play a critical role in interpreting medical images as part of their core responsibilities. They need to find comparable examples that can assist in making informed decisions, especially when encountering ambiguous visuals. Traditionally, Systems such as content-based medical image retrieval (CBMIR) have been used for this. The proposed method employs a novel technique, local histogram equalization (LHE) for preprocessing, transfer learning-based convolutional neural network to extract the representative features with Manhattan and Euclidean distance metrics to assess how similar the query image and database image are to one another. This model is trained on a standard dataset namely Chest X-Ray images. Top-k, Precision and Recall measure is employed to assess system performance. From the results, the suggested enhanced convolutional neural network (CNN) model demonstrates significantly superior performance in the top 10 retrieval rates of 97.13% for coronavirus disease 2019 (COVID-19), 96.84% for normal, 82.63% for pneumonia-bacterial, and 81.72% for pneumonia-viral and precision@recall10 of 93.14% for COVID-19, 91.88% for normal, 77.84% for pneumonia-bacterial and 74.71% for pneumonia-viral.
Artificial bee colony-based nonrigid demons registration
Authors: Roy Abhisek , Roy Pranab Kanti , Mitra Anirban , Daw Swarnali , Choudhury Sraddha Roy , Chakraborty Sayan , Misra Bitan
Published: June 2024

The artificial bee colony (ABC) algorithm has gained popularity in recent years for its ability to solve optimization problems. The accuracy and resilience of ABC-based image processing techniques have demonstrated encouraging outcomes. The ABC method is an excellent solution for image processing issues since it has the ability to swiftly and effectively explore the search space. The current research intends to address image registration issues by refining the existing image registration strategy using ABC algorithm. The process of nonrigid demons registration is frequently employed in the processing of medical images. The combination of these two techniques is referred to as the ABC-based nonrigid demons registration method. The proposed method has shown superior performance in registration accuracy and efficiency compared to other existing methods. Applications in medical image analysis and computer-assisted diagnosis are highly promising for the ABC-based nonrigid demons registration. Particle swarm optimization (PSO) and frameworks based on genetic algorithms (GA) have been compared with the suggested framework. The observed results showed improved accuracy and faster convergence in ABC-based demons registration.
Deep autoencoder based image enhancement approach with hybrid feature extraction for plant disease detection using supervised classification
Authors: Huddar Suma , Prabhushetty Kopparagaon , Jakati Jagadish , Havaldar Raviraj , Sirdeshpande Nandakishor
Published: June 2024

Plant leaf diseases pose significant threats to global agriculture, leading to reduced crop yields and economic losses. Rapid and accurate disease detection is essential for timely interventions and sustainable farming practices. This study presents an innovative approach for plant leaf disease detection by integrating wavelet analysis, color, and texture features, coupled with autoencoder denoising and support vector machine (SVM) classification. Wavelet analysis is employed to extract multi-resolution features, capturing intricate details at different scales. Furthermore, color and texture characteristics are extracted to encompass a broad spectrum of visual information crucial for distinguishing diseases. The Autoencoder model helps to enhance the feature representation that mitigates the impact of noise and irrelevant data. The SVM classifier is utilized to learn complex patterns and accurately classify different disease classes. The combined model of wavelet, color, and texture attributes, in combination with autoencoder denoising and SVM classification, markedly enhances the precision and efficiency of disease detection in contrast to conventional methods. The system's performance is evaluated using a PlantVillage dataset, showcasing its adaptability to different plant species and disease types. The overall performance is obtained as 98.60%, 97.25%, 96.89%, and 97.20% in terms of accuracy, precision, recall, and F-Score, respectively.
CycleInSight: An enhanced YOLO approach for vulnerable cyclist detection in urban environments
Authors: Narkhede Manish , Chopade Nilkanth
Published: June 2024

As urbanization continues to reshape transportation, the safety of cyclists in complex traffic environments has become a pressing concern. In response to this challenge, our research introduces a CycleInSight framework, which harnesses advanced deep learning and computer vision techniques to enable precise and efficient cyclist detection in diverse urban settings. Utilizing you only look once version 8 (YOLOv8) object detection algorithm, the proposed model aims to detect and localize vulnerable cyclists near vehicles equipped with onboard cameras. Our research presents comprehensive experimental results demonstrating its effectiveness in identifying vulnerable cyclists amidst dynamic and challenging traffic conditions. With an impressive average precision of 90.91%, our approach outperforms existing models while maintaining efficient inference speeds. By effectively identifying and tracking cyclists, this framework holds significant potential to enhance urban traffic safety, inform data-driven infrastructure planning, and support the development of advanced driver assistance systems and autonomous vehicles.
Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels
Authors: Hikmah Nada Fitrieyatul , Hajjanto Ariq Dreiki , A. Surbakti Armand Faris , Prakosa Nadhira Anindyafitri , Asmaria Talitha , Sardjono Tri Arief
Published: June 2024

Brain tumors, a subset of these malignancies, demand accurate and efficient diagnosis. Traditional methods use non-invasive medical imaging like magnetic resonance imaging (MRI) and computed tomography (CT). Although necessary for diagnosis, manual brain MRI picture segmentation is tedious and time-consuming. Using deep learning is a promising solution. This study proposes an innovative approach for brain tumor detection, focusing on meningioma tumors. Utilizing threshold-based segmentation, the MobileNetV2 architecture, a modified feature pyramid network (FPN), and single shot MultiBox detector (SSD), our model achieves precise localization and object detection. Pre-processing techniques such as grayscale conversion, histogram equalization, and Gaussian filtering enhance the MRI image quality. Morphological operations and thresholding facilitate tumor segmentation. Data augmentation and a meticulous dataset division aid in model generalization. The architecture combines MobileNetV2 as a feature extractor, SSD for object detection, and FPN for detecting small objects. Modifications, including lowering the minimum FPN level, enhance small object detection accuracy. The proposed model achieved a recall value of around 98% and a precision value of around 89%. Additionally, the proposed model achieved approximately 93% on both the dice similarity coefficient (DSC) value and the index of similarity. Based on the promising results, our research holds significant advancements for the field of medical imaging and tumor detection.
Optimizing glaucoma diagnosis using fundus and optical coherence tomography image fusion based on multi-modal convolutional neural network approach
Authors: Krishna Nanditha , Kenchappa Nagamani
Published: June 2024

A novel approach that combines segmented fundus images (FIs) and optical coherence tomography image (OCTIs) are presented here, by incorporating deep learning network (DLN) techniques, to address the imperative need for advanced diagnostic algorithms in detecting and classifying glaucoma. By combining these two images, glaucoma diagnoses are made to improve the accuracy with more reliability. Multi modal convolutional neural networks (MMCNNs) are proposed for automatically extracting discriminatory features from both segmented FIs and OCTIs, allowing for comprehensive ocular analysis. A significant improvement in glaucoma diagnosis is achieved through segmentation of both FIs and OCTIs, ensuring robustness generalization to diverse clinical scenarios, DLN models are trained on datasets encompassing a wide range of glaucoma cases. The integrated approach outperforms individual modalities in terms of early detection of glaucoma and accurate classification. This method demonstrates promising potential in early glaucoma detection due to its effectiveness. By combining segmented features from both FIs and OCTIs through MMCNNs, improved efficiency in diagnosing predominant ocular glaucoma disorder is achieved compared to existing methods. Within the scope of this research, GoogLeNet (GN) is applied to independently classify glaucoma (uni-modal) in segmented FIs and OCTIs, providing a basis for comparison with the evaluation of MMCNNs.
Deep learning approaches for recognizing facial emotions on autistic patients
Authors: El Rhatassi Fatima Ezzahrae , Ghali Btihal El , Daoudi Najima
Published: June 2024

Autistic people need continuous assistance in order to improve their quality of life, and chatbots are one of the technologies that can provide this today. Chatbots can help with this task by providing assistance while accompanying the autist. The chatbot we plan to develop gives to autistic people an immediate personalized recommendation by determining the autist’s state, intervene with him and build a profil of the individual that will assist medical professionals in getting to know their patients better so they can provide an individualized care. We attempted to identify the emotion from the image's face in order to gain an understanding of emotions. Deep learning methods like Convolutional neural networks and Vision Transformers could be compared using the FER2013. After optimization, CNN achieved 74% accuracy, whereas the VIT achieved 69%. Given that there is not a massive dataset of autistic individuals accessible, we combined a dataset of photos of autistic people from two distinct sources and used the CNN model to identify the relevant emotion. Our accuracy rate for identifying emotions on the face is 65%. The model still has some identification limitations, such as misinterpreting some emotions, particularly "neutral," "surprised," and "angry," because these emotions and facial traits are poorly expressed by autistic people, and because the model is trained with imbalanced emotion categories.
Message steganography using separate locations and blocks
Authors: Rasras Rashad J. , Abu Sara Mutaz Rasmi , Alqadi Ziad
Published: June 2024

A novel method of message steganography is introduced to solve the disadvantages of traditional least significant bit (LSB) based methods by dividing the covering-stego image into a secret number of blocks. A chaotic logistic map model was performed using the chaotic parameters and the number of image blocks for generating a chaotic key. This key was then sorted, and the locations of blocks 1 to 8 were used to select the required blocks to be used as covering-stego blocks. The introduced method simplifies the process of message bits hiding and extracting by adopting a batch method of bits hiding and extracting. A comparative analysis was conducted between the outcomes of proposed method and those of prevalent approaches to outline the enhancements in both speed and quality of message steganography.
Enhancing privacy-preserving in vehicular cloud through an incentive-based strategy
Authors: Mistareeh Hassan , Tennyson Matthew , Bany Salameh Haythem
Published: June 2024

The literature has extensively explored vehicle ad hoc networks (VANETs) and vehicular clouds, with a common assumption in these studies being incorporating onboard units (OBUs) in vehicles. OBUs are used to collect and disseminate information between vehicles. Furthermore, numerous studies assume the presence of road infrastructure for communication. Implementing a vehicular cloud can play a vital role in aggregating data on events such as weather conditions, traffic information and accidents. This information is distributed to other vehicles, allowing drivers to make informed decisions and ensure safe driving practices. To protect privacy within the vehicular cloud, pseudonyms serve as a means of communication between vehicles and roadside units (RSU). Numerous existing approaches suggest more frequent updates to vehicle pseudonyms to reduce the likelihood of linking transmitted messages by vehicles. However, some of these strategies overlook situations where vehicle density is low, and vehicles have limited willingness to engage in the pseudonym-changing process. This article introduces an architecture that encourages vehicles to participate in the pseudonym-changing process to enhance vehicle privacy. This is achieved by issuing rewards to vehicles that can be used to access cloud services.
Wicked node detection in wireless ad-hoc network by applying supervised learning
Authors: Ranganathan Chitra Sabapathy , Sampathrajan Rajeshkumar
Published: June 2024

A wireless ad-hoc network (WANET) is a decentralized network supported by wireless connections without a pre-existing architecture. However, the mobility of nodes is a defining characteristic of WANETs, and the speed with which nodes may act poses several security risks. As a result of these wicked nodes, more data packets are lost, which might cause a significant delay. Thus, it is very important to identify wicked nodes in WANET. This work provides a support vector machine approach for detecting (SVMD) wicked nodes in the internet of things. The number of characteristics is reduced using the linear correlation coefficient (LCC) technique. With the LCC technique, we can precisely measure the strength of the connection between any two nodes while clearing the field of irrelevant information. Further, the support vector machine (SVM) algorithm may identify the wicked nodes by analyzing metrics such as the packet received ratio, packet delay ratio, and remaining energy ratio. The next step is to reach a verdict in which the wicked nodes are punished by being rendered inoperable. The simulation results show that the network latency is minimized, and the chance of missing detection is decreased using this method in WANET.
Automatic notes based on video records of online meetings using the latent Dirichlet allocation method
Authors: Arianto Rakhmat , Asmara Rosa Andrie , Nurhasan Usman , Rahmanto Anugrah Nur
Published: June 2024

Meeting minutes can also be used as a benchmark for whether the meeting objectives have been achieved or not. Minutes are taken during the meeting until the end of the meeting, which contain essential points from the meeting. Minutes in online meetings are currently still done manually, and generally, every meeting is recorded as documentation that requires more human resources to change the recording of the meeting file. Based on the problems above, a solution to this problem is needed by creating an automatic note-taking system that can assist the note-takers in concluding the meeting, especially in the Information Technology Department. This study uses the latent Dirichlet allocation (LDA) method to determine text summarization and topic modeling. Based on this research, the system calculation using the LDA method produces a pretty good accuracy value for text summarization of 57.91% and topic modeling with a coherence score of 64.56%. Based on this research, the implementation of the latent Dirichlet allocation method for text summarization and topic modeling provides a fairly good level of similarity accuracy when compared to the minutes that are written manually and can be implemented in the Information Technology Department.
A comprehensive review of early detection of COVID-19 based on machine learning and deep learning models
Authors: Al-Khafaji Ali J. Askar , Sjarif Nilam Nur Amir
Published: June 2024

This paper reviews the use of machine learning (ML) and deep learning (DL) for early coronavirus disease (COVID-19) detection, highlighting their potential to overcome the limitations of traditional diagnostic methods such as long processing times and high costs. We analyze studies applying ML and DL to imaging, clinical, and genomic data, assessing their performance in terms of accuracy, sensitivity, specificity, and efficiency. The review discusses the advantages, limitations, and challenges of these models, including data quality, generalizability, and ethical considerations. It also suggests future research directions for improving model efficacy, such as integrating multi-modal data and developing more interpretable models. This concise review serves as a guide for researchers, healthcare practitioners, and policymakers on the advancements and prospects of ML and DL in early COVID-19 detection, promoting further innovation and collaboration in this vital public health domain.
Method for design and implementation of telecommunication devices for aircraft
Authors: Yerzhan Assel , Kozhabayeva Indira , Manbetova Zhanat , Boykachev Pavel , Nauryz Kanysh , Zhazykbaeva Zhazira , Seitova Zhadra , Aitzhanova Nursulu
Published: June 2024

This article highlights the importance of electrical filters in radio engineering devices, emphasizing their role in transmitting signals in the transparency band and suppressing signals in the stop band. We examined methods for designing frequency-selective circuits with lumped parameters, which, in general, are a complete field of radio engineering and allow the synthesis of devices of varying complexity. The focus of the article is the frequency region, where the distributed properties of the synthesized structures appear. The article also provides an overview of various methods for synthesizing ultra-high frequency (UHF) filters. It is emphasized that for low-pass filters a transition from a low-frequency prototype to a high-frequency representation is applied, which, despite the crudeness of the approach, provides satisfactory results that can be improved at the production stage. The article also discusses various methods for implementing bandpass filters on distributed elements, including the use of short-circuited and open-circuited stubs, as well as weak-coupled lines. In conclusion, the paper highlights the need to improve these methods to improve process accuracy and make filter designers more efficient in radio engineering.
Secure aware software development life cycle on medical datasets by using firefly optimization and machine learning techniques
Authors: Obulesu Ooruchintala , Suneel Sajja , Jangili Sudhakar , Ledalla Sukanya , Swetha Ballepu , Borra Subba Reddy
Published: June 2024

The abstract highlights the critical need for securing sensitive medical data, emphasizing the challenges in the medical domain due to the confidentiality of patient, disease, doctor, and staff information. The proposed study introduces a novel approach using machine learning, specifically integrating the firefly optimization technique with the random forest algorithm, to classify medical data in a secure manner. The significance lies in addressing the security concerns associated with medical datasets, offering a solution that prioritizes confidentiality throughout the software development life cycle (SDLC). The proposed algorithm achieves an impressive accuracy of 96%, showcasing its efficacy in providing a robust and secure framework for the development of applications involving medical data. This research contributes to advancing the field of medical data security, offering a practical solution for safeguarding sensitive information in healthcare applications.
Hybrid convolutional neural network-long short-term memory combined model for arrhythmia classification
Authors: Badiger Raghavendra , Manickam Prabhakar
Published: June 2024

The automated examination of electrocardiogram (ECG) signals holds significant importance within the medical field for managing various critical cardiac conditions. Identifying cardiomyopathy and arrhythmias is presently recognized as a challenging endeavor. While machine learning techniques have garnered substantial attention for categorizing these patterns, a predominant focus has been on the classification of arrhythmias. However, existing studies have overlooked instances where arrhythmia leads to cardiomyopathy, a specific cardiac disease scenario. In our research, we introduce an innovative method aimed at distinguishing between cardiomyopathy and cardiomyopathy accompanied by arrhythmia by employing a convolutional neural network (CNN-based) model. This novel approach fills the gap in existing literature by addressing the critical need to classify cases where arrhythmia induces cardiomyopathy, thereby presenting a potential advancement in accurately identifying and managing complex cardiac conditions. The proposed model uses convolution-based CNN model for feature extraction and combines these features with temporal features. Further, a CNN combined long short-term memory (CNN-LSTM) model is presented for classification where CNN models help to obtain the spatial information and LSTM helps to retain the temporal information resulting in improved classification accuracy. the experimental analysis is carried out into two phases where we have classified the rhythms and arrhythmias.
Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs
Authors: Fauzan Hermawan , Sarno Riyanarto , Saikhu Ahmad
Published: June 2024

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.
A machine learning model for predicting phishing websites
Authors: Odette Boussi Grace , Gupta Himanshu , Hossain Syed Akhter
Published: June 2024

There are various types of cybercrime, and hackers often target specific ones for different reasons, such as financial gain, recognition, or even revenge. Cybercrimes are not restricted by geographical boundaries and can occur globally. The prevalence of specific types of cybercrime can vary from country to country, influenced by factors such as economic conditions, internet usage levels, and overall development. Phishing is a common cybercrime in the financial sector across different countries, with variations in techniques between developed and developing nations. However, the impact, often leading to financial losses, remains consistent. In our analysis, we utilized a dataset featuring 48 attributes from 5,000 phishing webpages and 5,000 legitimate webpages to predict the phishing status of websites. This approach achieved an impressive 98% accuracy.
Crypto-steganographic model using chaos and coding based in deoxyribonucleic acid
Authors: López Torres Edison Andrés , Alvarado-Nieto Deicy , Amaya-Barrera Isabel , Suárez Parra César Augusto
Published: June 2024

Given the increase of information circulating through public channels, it is essential to create robust schemes to ensure the security of such information. The results presented here were part of the research project entitled computer security models based on mathematical tools and artificial intelligence. An algorithm focused on the encryption of images carrying steganographed texts is proposed, using chaos, artificial vision and coding based in deoxyribonucleic acid (DNA). The process consists of steganographic and cryptographic steps. In the steganographic stage, a color image was taken, the combined Canny and Sobel filters were applied to achieve its dilated edges, using Chen's chaotic attractor, the positions of the edges were selected, to hide a text in binary ASCII code using the least significant bit technique. In the encryption stage, Chen's chaotic system was used to permute the stego-image and to create a chaotic image used in the diffusion process. These two images were divided into blocks represented in DNA coding, selecting the rule to apply through the three-dimensional Logistics system, and finally applying the XOR operation by layers, obtaining a single encrypted image. To validate the proposed model, safety and performance tests were applied, obtaining comparable indicators with some current scientific references.
An efficient object detection by autonomous vehicle using deep learning
Authors: Kolukula Nitalaksheswara Rao , Kalapala Rajendra Prasad , Ivaturi Sundara Siva Rao , Tammineni Ravi Kumar , Annavarapu Mahalakshmi , Pyla Uma
Published: June 2024

The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day’s automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are mainly on-road vehicles, traffic signals, cars, trucks and buses. These inputs are given to the models to predict and detect the objects. The Performance of the proposed system is compared with performance of deep learning algorithms convolution neural network (CNN). The proposed system accuracy greater than 76.5% to 93.3%, mean average precision (Map) and frame per second (FPS) are 0.895 and 43.95%.
Characterization of facial and ocular gestures through electroencephalogram
Authors: Ovalle Silva Juan Sebastián , Anzola Anzola John Petearson , Canova Garcia Walder De Jesus
Published: June 2024

This article describes the characterization of facial and ocular gestures using the electroencephalogram (EEG) method connected with an EMOTIV EPOC+ Brainwear® device. This characterization is developed by the storage of raw data (unprocessed data) acquired by the device. The experiment was applied to nine subjects, considering that EEG explores neurophysiologically with high levels of statistical confidence the bioelectric activity in the brain in the condition of resting state such as wakeups or dreaming states. In contrast to non-resting states, the registered data showed a random and distinct activation of hyperpnea and intermittent luminous stimulus. Despite the reduced number of samples in the experiment, the results showed that the level of confidence was greater than 75%. The data was characterized and processed by a support vector machine (SVM).