bluuebunny/crossref_metadata_2025 huggingface dataset:
Abstract SY34-01: Plasticity, dormancy, and glycosylation of circulating tumor cell clusters in response to therapy
Authors: Liu Huiping
Published: April 2024

Cellular plasticity and stemness properties enable dynamic changes of circulating tumor cells (CTCs) during cancer dissemination, such as aggregation or cohesion of single CTCs into multicellular CTC clusters with 20-100 times higher metastatic propensity than the singles. Our work demonstrated that stemness glycoproteins on breast cancer stem cells, such as CD44, CD81, and ICAM1 drive CTC aggregation in metastatic breast cancer, such as triple negative breast cancer (TNBC). To determine the glycosylation patterns in CTCs and its association with clinical outcomes, we established multiple CTC analysis approaches including CellSearch, flow cytometry, and immunohistochemistry using patient blood and tissue sections collected at the pre-treatment baseline and after-therapy timepoints. We found that chemotherapy-evasive CTC clusters are relatively quiescent with a specific loss of terminal sugar residues α2,6-sialic acids in glycoproteins. CTCs showed dynamic hypo-sialylation in the blood with loss of the sialyl-transferase ST6GAL1, promoting CTC cluster formation with cellular quiescence (proliferative dormancy) and evading chemotherapy in breast cancer. Seeded tumor cells regained ST6GAL1 to enable metastatic colonization. Many adhesion proteins and stemness drivers as glycoprotein substrates of ST6GAL1 drive CTC clustering and metastatic seeding. Neutralizing antibodies against these clustering drivers inhibit CTC cluster formation and improve therapy response, thereby blocking lung metastasis in TNBC.

Citation Format: Huiping Liu. Plasticity, dormancy, and glycosylation of circulating tumor cell clusters in response to therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY34-01.
Neutrophil Extracellular Trap Formation and Deoxyribonuclease I Activity in Patients with Otitis Media with Antineutrophil Cytoplasmic Antibody-Associated Vasculitis
Authors: Morita Shinya , Nakamaru Yuji , Fukuda Atsushi , Fujiwara Keishi , Suzuki Masanobu , Hoshino Kimiko , Honma Aya , Nakazono Akira , Homma Akihiro
Published: April 2024

Introduction: No previous studies have evaluated the levels of neutrophil extracellular trap (NET) remnants or the importance of deoxyribonuclease (DNase) I activity based on the disease activity of otitis media with antineutrophil cytoplasmic antibody-associated vasculitis (OMAAV). The aim of this study was to explore the formation of NETs in the middle ear of patients with OMAAV during the onset and remission phases of the disease, with a particular focus on the relationships between the quantifiable levels of NET remnants and DNase I activity. Methods: OMAAV patients were eligible for inclusion. Patients with otitis media with effusion (OME) were examined as controls. The levels of cell-free deoxyribonucleic acid (DNA), citrullinated-histone H3 (cit-H3)-DNA complex, and myeloperoxidase (MPO)-DNA complex were quantified using an enzyme-linked immunosorbent assay. DNase I activity was measured using a fluorometric method. Results: The quantifiable levels of cell-free DNA, cit-H3-DNA complex, and MPO-DNA complex in the middle ear lavage of patients with OMAAV at onset were significantly higher than those in patients with OMAAV at remission and in patients with OME. DNase I activity in the patients with OMAAV at onset was significantly lower than those in patients with OMAAV at remission and OME and was negatively correlated with the level of MPO-DNA complex. Conclusions: This study suggests that NET remnants and DNase I activity may be potentially useful biomarkers for the diagnosis and disease activity of OMAAV.
Speed control for traction motor of urban electrified train in field weakening region based on backstepping method
Authors: Thu Anh An Thi Hoai , Tung Ngo Manh
Published: April 2024

Tractor motors always operate in the speed region higher than rated speed, but is limited to the module of the stator current, stator voltage vectors. Additionally, mathematical model of traction motor has shown nonlinearity through the product of the state variables 𝑖𝑠𝑑, 𝑖𝑠𝑞 with the input variable 𝜔𝑠:𝜔𝑠𝑖𝑠𝑞, 𝜔𝑠𝑖𝑠𝑑. Therefore, this paper focuses on the study of speed control of traction motors in weakening field region while optimizing torque control, and choosing the backstepping method in designing speed–flux controller in order to solve the nonlinear structure. The simulation results of the responses: speed, torque, power, and flux performed on MATLAB/Simulink software with parameters collected from metro Nhon-Hanoi Station, Vietnam have proven the correctness in theoretical research.
New control scheme for a dynamic voltage restorer based on selective harmonic injection technique with repetitive controller
Authors: Tapre Pawan C. , Thakre Mohan P. , Pawase Ramesh S. , Thorat Jaywant S. , Dahigaonkar Dipak J. , Mapari Rahul G. , Kadlag Sunil Somnath , Khule Shridhar
Published: April 2024

Repetitive controller and selective harmonic injection technique (SHI) in medium and low voltage distribution networks improve dynamic voltage restorer (DVR) DC bus voltages as well as nullify power quality (PQ) problems. DVRs use sinusoidal pulse width modulation (SPWM) firing control, but DC bus use seems to be limited, affecting density, cost, and power packaging. By adding 1/6th of the 3rd harmonic waveform to the basic waveform, SPWM yields the developed model. According to the findings, 15% of DC bus usage improves and produces high voltage AC. Nevertheless, just control systems perturb PQ. The proposed controller uses feed forward and feedback to enhance transient response and justify stable zero error. 3rd third harmonic injection pulse width modulation (THIPWM) improves total harmonic distortion (THD) in the proposed scheme. Power system computer aided design (PSCAD) simulation produced high accuracy for THIPWM and repetitive controllers.
Comparative analysis of reactive routing protocols for vehicular adhoc network communications
Authors: Kaushal Payal , Khurana Meenu , Ramkumar Ketti Ramchandran
Published: April 2024

In the recent past, vehicular adhoc networks (VANETs) have gained a lot of importance. The routing protocols play a vital role to deliver payloads from one vehicle to another one while they are moving at relative speeds. It is a challenging task to select a routing protocol for VANETs because of the uneven distribution and high mobility of vehicles. In this paper, we have analysed the two standard reactive protocols, adhoc on-demand distance vector (AODV) and ant colony optimization (ACO). The performance comparison of AODV and ACO routing protocols has been presented in this paper. The results show AODV performed better in terms of energy consumption and routing overhead. While considering the throughput, energy loss ratio, and delay ACO has performed. ACO resulted as upperhand.
Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon
Authors: Kannan Deeba , Balakrishnan Amutha , Devi K. Mekala , Singh Nagendra , Kiruba P. Angelin , Ramkumar Ravindran , Karthikeyan Dhandapani
Published: April 2024

Disease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant.
Stereo matching algorithm using deep learning and edge-preserving filter for machine vision
Authors: Abd Gani Shamsul Fakhar , Miskon Muhammad Fahmi , Hamzah Rostam Affendi , Hamid Mohd Saad , Kadmin Ahmad Fauzan , Herman Adi Irwan
Published: April 2024

Machine vision research began with a single-camera system, but these systems had various limitations from having just one point-of-view of the environment and no depth information, therefore stereo cameras were invented. This paper proposes a hybrid method of a stereo matching algorithm with the goal of generating an accurate disparity map critical for applications such as 3D surface reconstruction and robot navigation to name a few. Convolutional neural network (CNN) is utilised to generate the matching cost, which is then input into cost aggregation to increase accuracy with the help of a bilateral filter (BF). Winner-take-all (WTA) is used to generate the preliminary disparity map. An edge-preserving filter (EPF) is applied to that output based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line to eliminate these artefacts. The transform warps the input signal adaptively to allow linear 1D filtering. Due to the filter's resistance to high contrast and brightness, it is effective in refining and removing noise from the output image. Based on experimental research employing a Middlebury standard validation benchmark, this approach gives high accuracy with an average non-occluded error of 6.71% comparable to other published methods.
Best Agile method selection approach at workplace
Authors: Merzouk Soukaina , Jabir Brahim , Marzak Abdelaziz , Sael Nawal
Published: April 2024

Selecting the most suitable agile software development method is a challenging task due to the variety of available methods, each with its strengths and weaknesses. To achieve project goals effectively, factors such as project needs, team size, complexity, and customer involvement should be carefully evaluated. Choosing the appropriate agile method is crucial for achieving high client satisfaction and effective team management, but it can be a challenging task for project managers and higher-level management officials.This paper presents a solution aiming to help them in selecting the most suitable software development method for their project. In this regard, this solution includes a pre-project management approach model and a decision tree that considers the unique requirements of the project. In the proposed solution results, Scrum was found to be suitable for both small and large projects, on the condition that roles and responsibilities are clearly defined and that the approach is people-centric. Furthermore, high-risk mitigation measures should be added for small projects. To facilitate the use of our model, a software application has been developed which implements the decision-making tree.
System design of a microstrip antenna by dimension and substrates optimization
Authors: Africa Aaron Don M. , Pasia Samuel Alexander , Sy Jereme Adriane
Published: April 2024

The microstrip antennas matured and improved over the last 25 years. Throughout these years, the limitations, and specifications of the said antennas have been overcome and significantly improved. Known to be lowprofile, suitable for mobile, and lightweight, these microstrip antennas are the focus of this research. In this paper, the researchers designed a microstrip antenna with varying lengths and substrates. They tested the changes and the effects in microstrip antennas of different lengths, along with altering substrates. To verify the differences, the researchers compared the performance parameters maximum gain (dBi), minimum gain (dBi), and S11 graph on each tested length and changed substrate. The rough set theory was used to determine the optimal design via MATLAB. From there, the researchers analyzed the results gathered and drew their respective conclusions. Additionally, they saw and compared each data result to know what antenna has the best performance parameters. From the results, a change in the dimension will result in a decrease in the said performance parameters. Furthermore, the change in substrate thickness also diminishes these changes.
Comparative analysis of ARIMA and LSTM for predicting fluctuating time series data
Authors: Taslim Deddy Gunawan , Murwantara I Made
Published: April 2024

The investigation of time series data forecasting is a critical topic within the realms of economics and business. The autoregressive integrated moving average (ARIMA) model has been prevalently utilized, notwithstanding its limitations, which include the necessity for a substantial quantity of data points and the presumption of data linearity. However, with recent developments, the long short-term memory (LSTM) network has emerged as a promising alternative, potentially overcoming these limitations. The objective of this study is to determine an effective approach for managing time series data characterized by volatility and missing values. Evaluation was conducted using RMSE for accuracy assessment, and the execution time measured using the Python Timeit library. The findings indicates that in a dataset comprising 60 data points, the LSTM model (RMSE 0.037618) surpasses the ARIMA model (RMSE 0.062667) in terms of accuracy. However, this trend reverses in a larger dataset of 228 data points, where the ARIMA model demonstrates superior accuracy (RMSE 0.006949) compared to the LSTM model (RMSE 0.036025). In scenarios with missing data, the LSTM model consistently outperforms the ARIMA model, although the accuracy of both models diminishes with an increase in the number of missing values. The ARIMA model significantly outpaces the LSTM model.
Classifying possible hate speech from text with deep learning and ensemble on embedding method
Authors: Caprisiano Ebenhaiser Jonathan , Ramadhansyah Muhammad Hafizh , Zahra Amalia
Published: April 2024

Hate speech can be defined as the use of language to express hatred towards another party. Twitter is one of the most widely used social media platforms in the community. In addition to submitting user-generated content, other users can provide feedback through comments. There are several users who intentionally or unintentionally provide negative comments. Even though there are regulations regarding the prohibition of hate speech, there are still those who make negative comments. Using the deep learning method with the long short-term memory (LSTM) model, a classifier of possible hate speech from messages on Twitter is carried out. With the ensemble method, term frequency times inverse document frequency (TF-IDF) and global vector (GloVe) get 86% accuracy, better than the stand-alone word to vector (Word2Vec) method, which only gets 80%. From these results, it can be concluded that the ensemble method can improve accuracy compared to only using the stand-alone method. Ensemble methods can also improve the performance of deep learning systems and produce better results than using only one method.
Enhancing speech emotion recognition with deep learning using multi-feature stacking and data augmentation
Authors: Al Mukarram Khasyi , Mukhlas M. Anang , Zahra Amalia
Published: April 2024

This study evaluates the effectiveness of data augmentation on 1D convolutional neural network (CNN) and transformer models for speech emotion recognition (SER) on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset. The results show that data augmentation has a positive impact on improving emotion classification accuracy. Techniques such as noising, pitching, stretching, shifting, and speeding are applied to increase data variation and overcome class imbalance. The 1D CNN model with data augmentation achieved 94.5% accuracy, while the transformer model with data augmentation performed even better at 97.5%. This research is expected to contribute better insights for the development of accurate emotion recognition methods by using data augmentation with these models to improve classification accuracy on the RAVDESS dataset. Further research can explore larger and more diverse datasets and alternative model approaches.
Development of IoT based intelligent irrigation system using particle swarm optimization and XGBoost techniques
Authors: Santosh D. Teja , Anuradha Nandula , Kolukuluri Madhavi , Gupta Gaurav , Pathak Mrunal Kishor , Krishnan V. Gokula , Raghuvanshi Abhishek
Published: April 2024

A crop needs regular watering throughout its life to grow well. Irrigation improves food growth. Machines irrigate plants. The dry Sahel, which gets a lot of rain during the summer season but is dry in winter, needs irrigation. When it doesn't rain enough, crops need watering. By constantly monitoring soil moisture, humidity, temperature, and pH, precision agriculture reduces water use and increases crop output. Precision gardening uses less water. In many wealthy nations, efficient farming requires the internet of things (IoT). Particle swarm optimization (PSO) and XGBoost are used in this IoT-based intelligent watering system. Humidity and moisture sensors gather soil data at grass roots. Sensors constantly gather this data. These data are useless for smart watering. PSOselects smart watering data. This reduces central cloud info storage. Then, machine learning methods are trained using soil humidity, moisture, crop, and weather data. These programs can calculate a crop's water requirements. IoT devices control irrigation system water flow and results in saving fresh water. XGBoost algorithm is saving water from 23% to 27% for different crops.
A convolution neural network integrating climate variables and spatial-temporal properties to predict influenza trends
Authors: Watmaha Jaroonsak , Kamonsantiroj Suwatchai , Pipanmaekaporn Luepol
Published: April 2024

The spread of influenza is contingent upon a multitude of outbreak-related factors, including viral mutation, climate conditions, acquisition of immunity, crowded environments, vaccine efficacy, social gatherings, and the health and age profiles of individuals in contact with infected individuals. An epidemic in the region impacted by spatial transmission risk from adjacent regions. A few influenzas epidemic models start highlighting the spatial correlations between influenza patients and geographically adjacent regions. The proposed model is based on the concept of climatic, immunization, and spatial correlations which are represented by a convolution neural network (CNN) for influenza epidemic forecasting. This study presents an integration of three determinants for predicting influenza outbreaks, multivariate climate data, spatial data on influenza vaccination, and spatial-temporal data of historical influenza patients. The performance of three comparison models, CNN, recurrent neural network (RNN), and long short-term memory (LSTM) was compared by the root mean squared error metric (RMSE). The findings revealed that the CNN model represents human interaction at intervals of 12, 16, 20, 24, and 28 weeks resulting in the best effectiveness of the lowest RMSE=0.00376 with learning rate=0.0001.
A new topology of non-isolated AC-DC quadratic boost converter with enhanced power traits
Authors: Ahmed Istiak , Azad Ferdous S. , Hasan Shameem , Al Mamun Abdullah , M. Salim Khosru
Published: April 2024

A novel AC-DC quadratic boost converter (QBC) topology is presented in this paper which can provide higher power factor (PF), lower total harmonic distortion (THD) and higher voltage when compared to a conventional AC-DC boost convertero This is achieved by using additional switched capacitor and switched inductor in the power processing stage. The presented converter is analyzed theoretically, and a voltage gain equation is derived. A simulation model is created to evaluate the converter performance under various duty cycles, switching frequency, and changing output load. The input PF, THD, and voltage gain of the simulated model were compared with conventional converter to determine the validity of the suggested converter circuit. The results show that the efficiency, PF, THD and voltage gain value reaches approximately 97.9%, 0.98, 16.14, and 2.83 respectively at a duty cycle of 50% with fixed output load of 100 Ω. A dual loop voltage and current controller is also arrayed with the proposed circuit which allows further enhancement in PF (0.997) and THD (7.45%). This converter can be a suitable option for systems where isolation is not necessary but high PF, high voltage gain and low THD are required.
Design of a novel compact low specific absorption rate multiple input multiple output antenna for 5G sub-6 GHz terminals
Authors: Ennajih Abdelhadi , Sardi Azzeddine , Mouzouna Youssef , Sadik Mohamed , Errkik Ahmed
Published: April 2024

This investigation presents a new design and analysis of a fourteen elements massive multiple input multiple output (MIMO) compact low specific absorption rate (SAR) antenna system for 5G and beyond terminals. The MIMO antennas are realized for sub-6 GHz LTE-band 46 (5.1-5.8 GHz) long-term evolution 5G wireless communication services. The proposed antenna system is designed on an FR-4 dielectric constant 4.3 substrate consisting of the main board and four sideboards. The radiating elements are developed on the sideboards consisting of a monopole antenna and two metamaterial unit cells, as well as the feed line is designed on the principal board. The overall volume of the proposed structure is 150×80×7.5 mm. Through the use of the metamaterial unit cells, the proposed antennas have been arranged in close proximity without developing a strong mutual coupling. The results obtained were very important in terms of impedance matching, isolation among the antennas, and good gain. Even, the SAR analysis at the operating frequency band of 5.5 GHz proves that the proposed MIMO antenna system satisfies safety standards set for human exposure at the international level. Therefore, the proposed massive MIMO antenna structure is a potential solution for future communication applications.
Combining dual attention mechanism and efficient feature aggregation for road and vehicle segmentation from UAV imagery
Authors: Nguyen Trung Dung , Pham Trung Kien , Ha Chi Kien , Le Long Ho , Ngo Thanh Quyen , Nguyen Hoanh
Published: April 2024

Unmanned aerial vehicles (UAVs) have gained significant popularity in recent years due to their ability to capture high-resolution aerial imagery for various applications, including traffic monitoring, urban planning, and disaster management. Accurate road and vehicle segmentation from UAV imagery plays a crucial role in these applications. In this paper, we propose a novel approach combining dual attention mechanisms and efficient multi-layer feature aggregation to enhance the performance of road and vehicle segmentation from UAV imagery. Our approach integrates a spatial attention mechanism and a channel-wise attention mechanism to enable the model to selectively focus on relevant features for segmentation tasks. In conjunction with these attention mechanisms, we introduce an efficient multi-layer feature aggregation method that synthesizes and integrates multi-scale features at different levels of the network, resulting in a more robust and informative feature representation. Our proposed method is evaluated on the UAVid semantic segmentation dataset, showcasing its exceptional performance in comparison to renowned approaches such as U-Net, DeepLabv3+, and SegNet. The experimental results affirm that our approach surpasses these state-of-the-art methods in terms of segmentation accuracy.
Energy management in hybrid complexes based on wind generation and hydrogen storage
Authors: Shklyarskiy Yaroslav , Andreeva Iuliia , Sutikno Tole , Jopri Mohd Hatta
Published: April 2024

This work is devoted to the analysis of scientific articles in the field of hybrid energy complexes based on hydrogen technologies and renewable energy sources. A special attention is given to wind generation. The review of the topics of scientific publications indexed in the Scopus database in the field of research is carried out, the most frequently encountered topics and the rarest ones are highlighted. Brief statistics about publications selected for detailed analysis are given. The most interesting direction for studying is development of control systems for hybrid energy complexes. Several traditional approaches, which are commonly used in research on this topic and methods are highlighted. The existing shortcomings and inaccuracies in the overviewed works are identified. Conclusions are drawn about the need to transform existing methods and specific proposals are made to improve management systems to increase the efficiency of decision-making and achieve greater economic benefits. Promising areas of research that also require special attention are formulated.
Low insertion loss open-loop resonator–based microstrip diplexer with high selective for wireless applications
Authors: Elabd Rania Hamdy , Al-Gburi Ahmed Jamal Abdullah , Alhassoon Khaled , Muzafar Ismail Mohd , Zakaria Zahriladha
Published: April 2024

This paper presents a low-insertion-loss open-loop resonator (OLR)-based microstrip diplexer with high-selective for wireless applications. We used two series capacitive gaps in the microstrip transmission line, loaded with rectangular-shaped half-wavelength OLRs, to create a high-selectivity bandpass filter (BPF). The planned BPFs are linked through a T-junction combiner, precisely tuned to align with both filters and the antenna port in order to produce the proposed diplexer. The system is implemented on a rogers TMM4 substrate with a loss tangent of 0.002, a dielectric constant of 4.7, and a thickness of 1.52 mm. The suggested diplexer has dimensions of (90×70) mm². It achieves a modest frequency space ratio of R=0.1646 in both transmit and receive modes by having two resonance frequencies of ft=2.191 GHz and fr=2.584 GHz, respectively. The simulated structure displays good insertion losses of approximately 1.2 dB and 1.79 dB for the two channels, respectively, at fractional bandwidths of 1.24% at 2.191 GHz and 0.636% at 2.584 GHz. The simulated isolation values for 2.191 GHz and 2.584 GHz are 53.3 dB and 66.5 dB, respectively.
Experimental investigation of a hybrid photovoltaic-thermal energy system for hot air production
Authors: Hiendro Ayong , Husin Fitriah , Taufiqurrahman Muhammad , Aula Abqori
Published: April 2024

Solar energy as a non-fossil alternative energy source has become the best choice to overcome the problem of energy demand in most countries in the world. There are two different techniques to convert solar energy: photovoltaic (PV) panels to produce electricity and thermal collectors to generate heat. The two technologies can be combined to provide electrical and thermal energy either simultaneously or separately. In order to optimize the performance of a hybrid photovoltaic-thermal (PVT) solar air heater, it is necessary to collect experimental data on solar irradiation and temperature. This paper emphasized the development of a PVT energy system for hot air production in a temperature range of 50-55 °C. Additionally, experiments were constructed to monitor the information acquired from the proposed PVT solar air heater and the environment, such as hot air temperature, ambient temperature, and solar irradiation. The real-time monitoring system was set for five sample days. A microcontroller unit was used to control the hot air temperature and save the measurement data into memory. The experimental results showed that the proposed PVT solar air heater is capable of maintaining a certain level of hot air temperature throughout the day and night.
Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification
Authors: Rahouma Kamel Hussein , Mabrouk Shahenda Mahmoud , Aouf Mohamed
Published: April 2024

Cancer of the lungs is considered one of the primary causes of death among patients globally. Early detection contributes significantly to the success of pulmonary cancer treatment. To aid the pulmonary nodule classification, many models for the analysis of medical image utilizing deep learning have been developed. Convolutional neural network (CNN) recently, has attained remarkable results in various image classification tasks. Nevertheless, the CNNs performance is heavily dependent on their architectures which still heavily reliant on human domain knowledge. This study introduces a cutting-edge approach that leverages genetic algorithms (GAs) to automatically design 3D CNN architectures for differentiation between benign and malignant pulmonary nodules. The suggested algorithm utilizes the dataset of lung nodule analysis 2016 (LUNA16) for evaluation. Notably, our approach achieved exceptional model accuracy, with evaluations on the testing dataset yielding up to 95.977%. Furthermore, the algorithm exhibited high sensitivity, showcasing its robust performance in distinguishing between benign and malignant nodules. Our findings demonstrate the outstanding capabilities of the proposed algorithm and show an outstanding performance and attain a state of art solution in lung nodule classification.
A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions
Authors: Rahaman Md Obaidur , Kashem Mohammod Abul
Published: April 2024

Cardiovascular diseases engender serious public health concerns in developing nations since access to specialized medical equipment is often limited and standard treatment expenses can be prohibitive. This study proposes an efficient and relatively affordable electrocardiogram (ECG) monitoring system that reads and analyzes a person's electrocardiogram data to provide affordable and quality healthcare solutions. The device initially extracts features from electrocardiogram records by reading electrical signals in the heart. Extracted data are then analyzed by a trained deep learning model to determine precisely if the heart is in a healthy state or undergoing complexities. Experimental results showed that the fine-tuned ANN architecture outperformed the state-of-the-art architectures in this field with an accuracy of 98.95%. The data can also be sent to specialists through an MQTT server if necessary, allowing for remote diagnosis and treatment. The system is intended to be deployed in countries where rural regions lack access to specialized healthcare equipment and professionals. Additionally, the device is inexpensive and, hence can be made accessible to people with limited affordability.
Palembang songket fabric motif image detection with data augmentation based on ResNet using dropout
Authors: Ermatita Ermatita , Noprisson Handrie , Abdiansah Abdiansah
Published: April 2024

A good way to spread knowledge about Palembang songket woven cloth patterns is to use information technology, especially artificial intelligence technology. This study's main goal is to develop a ResNet model with dropout regularization methods and find out how dropout regularization affects the ResNet model for detecting Palembang songket fabric motif with more data. Data was collected in places like tujuh saudara songket, Zainal songket, songket PaSH, AMS songket, and batik, Ernawati songket, Nabilah collections, Ilham songket, and Marissa songket. We used eight class of data for this research. A dataset of 7,680 data for training, 960 data for validation, and 960 data for testing is a dataset that has been prepared to be implemented in experiments. In the final results, the experimental results for DResNet demonstrated that accuracy at the training stage was 92.16%, accuracy at the validation stage was 78.60%, and accuracy at the submission stage was 80.3%. The experimental results also show that dropouts are able to increase the accuracy of the ResNet model by adding +1.10% accuracy in the training process, adding +1.80% accuracy in the validation process, and adding +0.40% accuracy in the testing process.
A novel method of detecting malware on Android mobile devices with explainable artificial intelligence
Authors: Vanjire Seema Sachin , Lakshmi Mohandoss
Published: April 2024

The increasing prevalence of malware targeting android mobile devices has raised significant concerns regarding user privacy and security. In response, effective methods for malware classification and detection are crucial to protect users from malicious applications. This paper presents an approach that leverages deep learning techniques and explainable artificial intelligence (XAI) for android mobile malware classification and detection. Convolutional neural networks (CNNs) are deep learning model that has shown impressive performance in several application areas, including image and text classification. In the context of android mobile malware, CNNs have shown promising results in capturing intricate patterns and features inherent in malware samples. By training these models on large datasets of benign and malicious applications, accurate classification can be achieved. To enhance transparency and interpretability, XAI techniques are integrated into the classification process. These techniques provide insights into the decision-making process of the deep learning models, enabling the identification of critical features and characteristics that contribute to the classification results. This research, by combining deep learning and XAI methods, presents a fresh strategy for identifying and categorizing Android malware. This research paper will focus on a fascinating CNN-based malware categorization technique.
Smart indoor gardening: elevating growth, health, and automation
Authors: Alrawashdeh Tawfiq , Alkore Alshalabi Ibrahim , Al-Jaafreh Moha'med , Alksasbeh Malek
Published: April 2024

Recently, indoor systems for growing plants have emerged as a promising approach to address the problems related to extreme weather conditions outdoors. However, such systems must manage the plants surrounding environments to satisfy the environmental and the economical requirements. In this line, any proposed solution must address challenging factors such as plants diseases and unordinary climate situations. In this paper, propose an internet of things (IoT) indoor system that can be used to facilitate the plant growing process. The proposed system is designed to provide alternatives for outdoor climate dependency such as the vitamins provided through sunlight. Moreover, renewable energy sources (sunlight) are employed to reduce the impact on the environment. With the help of several types of sensors, the system continuously monitors the plants through their growing journey. Whereas actuator devices are employed to control the plant-feeding process based on the sensors’ reported values. All the collected data will be uploaded to the cloud for analysis, utilizing a website. Additionally, the architecture of the provided system eliminates the need for human involvement, which has a degrading effect on the plant growing process.
Secure map-based crypto-stego technique based on mac address
Authors: Kasasbeh Dima S. , Al-Ja’afreh Bushra M. , Anbar Mohammed , Hasbullah Iznan H. , Al Khasawneh Mahmoud
Published: April 2024

Steganography and cryptography are spy craft cousins, working differently to achieve the same target. Cryptography is perceptible and observable without understanding the real content, while steganography hides the content so that it is not perceptible or observable and without producing noticeable changes to the carrier image. The challenge is finding the right balance between security and retrievability of embedded data from embedding locations without increasing the required embedded information. This paper proposes a secure map-based steganography technique to enhance the message security level based on the sender and recipient mac addresses. The proposed technique uses rivest-shamir-adleman (RSA) to encrypt the message, then embeds the cipher message in the host image based on the sender and recipient media access control addresses (mac addresses) exclusive or operation "XOR" results without increasing the required embedded information for the embedding location map. The proposed technique is evaluated on various metrics, including peak signal-to-noise ratio (PSNR) and embedding capacity, and the results show that it provides a high level of security and robustness against attacks without an extra location map. The proposed technique can embed more data up to 196.608 KB in the same image with a PSNR higher than 50.58 dB.
A survey on planet leaf disease identification and classification by various machine-learning technique
Authors: Pujar Premakumari , Kumar Ashutosh , Kumar Vineet
Published: April 2024

An overview of methods for identifying plants diseases is given in this article. Each sample is categorized by being divided into various groups. The approach of classification involves identifying healthy and diseased leaves based on morphological traits including texture, color, shape, and pattern, among others. Sorting and categorizing plants can be challenging, especially when doing so across a large area, due to the closeness of their visual qualities. There are several methods based on computer vision and image processing. Selecting the right categorization method can be difficult because the outcomes rely on the data you supply. There are several applications for the categorization of plant leaf diseases in fields like agriculture and biological research. This article gives a summary of several approaches currently in use for identifying and categorizing leaf diseases, as well as their benefits and drawbacks. These approaches include preprocessing methods, feature extraction and selection methods, datasets employed, classifiers, and performance metrics
Leveraging machine learning techniques for student’s attention detection: a review
Authors: Lim Eng Lye , Murugesan Raja Kumar , Balakrishnan Sumathi
Published: April 2024

With the advances of the internet and today's innovation, it has become conceivable to conduct teaching and learning activities remotely through the online platform. Existing research says that student’s attention state and learning result are strongly correlated. However, despite its importance, this can be a challenging task, as students in general taking an online class may be in a variety of different environments and may be multitasking or distracted by other factors. This review paper aims to address these challenges by exploring the opportunities offered by machine learning techniques in attention detection for effective online teaching and learning. By leveraging machine learning algorithms, which can analyze large volumes of data, including eye-tracking, facial expressions, and body movements, we can develop robust models for attention detection in online learning environments. This paper reviews the challenges specific to online learning, such as students' attention deficits and learning styles, and highlights the limitations of current attention detection methods. Furthermore, it provides recommendations to advance attention detection technology, emphasizing the potential of machine learning to enhance attention detection technology for effective online teaching and learning.
Smart prison technology and challenges: a systematic literature reviews
Authors: Imandeka Ejo , Hidayanto Achmad Nizar , Mahmud Mufti
Published: April 2024

The rapid rise of intelligent technology, particularly in government, is igniting a new phase of the industrial revolution around the world. As governmental entities, prisons oversee upholding social order and lowering current crime. The concept of the smart prison has not received much attention but is gaining traction. The goal of this research is to conduct a literature review to identify current prison technologies and to analyse the challenges associated with implementing smart prisons using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. Nine credible publishers were looked up between October 2022 and December 2022. The initial search yielded 362 articles, of which 25 were included in the final phase. This research provides the current state of prison according to technology-organization-environment (TOE). Some challenges arise in the context of TOE, such as the high cost of smart technology, inadequate technology design, poor management, ineffective service, overcrowding, ageing facilities, increasing violence, disease spread, and ethical problems. This study also classifies smart prison technology based on the internet of things (IoT) architecture layer. By providing the first comprehensive review on smart prison technology, this study makes an essential contribution to the subject of prisons.
Aspect based sentiment analysis using fine-tuned BERT model with deep context features
Authors: Rajan Abraham , Manur Manohar
Published: April 2024

Sentiment analysis is the task of analysing, processing, inferencing and concluding the subjective texts along with sentiment. Considering the application of sentiment analysis, it is categorized into document-level, sentence-level and aspect level. In past, several researches have achieved solutions through the bidirectional encoder representations from transformers (BERT) model, however, the existing model does not understand the context of the aspect in deep, which leads to low metrics. This research work leads to the study of the aspect-based sentiment analysis presented by deep context bidirectional encoder representations from transformers (DC-BERT), main aim of the DC-BERT model is to improvise the context understating for aspects to enhance the metrics. DC-BERT model comprises fine-tuned BERT model along with a deep context features layer, which enables the model to understand the context of targeted aspects deeply. A customized feature layer is introduced to extract two distinctive features, later both features are integrated through the interaction layer. DC-BERT mode is evaluated considering the review dataset of laptops and restaurants from SemEval 2014 task 4, evaluation is carried out considering the different metrics. In comparison with the other model, DC-BERT achieves an accuracy of 84.48% and 92.86% for laptop and restaurant datasets respectively.
Cost-aware optimal resource provisioning Map-Reduce scheduler for hadoop framework
Authors: Bhaskar Archana , Ranjan Rajeev
Published: April 2024

Distributed data processing model has been one of the primary components in the case of data-intensive applications; furthermore, due to advancements in technologies, there has been a huge volume of data generation of diverse nature. Hadoop map reduce framework is responsible for adopting the ease of deployment mechanism in an open-source framework. The existing Hadoop MapReduce framework possesses high makespan time and high Input/Output overhead and it mainly affects the cost of a model. Thus, this research work presents an optimized cost aware resource provisioning MapReduce model also known as the cost-effective resource provisioning MapReduce (CRP-MR) model. CRP-MR model introduces the two integrated approaches to minimize the cost; at first, this model presents the optimal resource optimization and optimal Input/Output optimization cleansing in the Hadoop MapReduce (HMR) scheduler. CRP-MR is evaluated considering the bioinformatics dataset and CRP-MR performs better than the existing model. 
Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration
Authors: A. Sujathakumari Bettadapura , Patil Kulkarni Sudarshan , Hallikeri Vikas
Published: April 2024

Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. 
Ubiquitous-cloud-inspired deterministic and stochastic service provider models with mixed-integer-programming
Authors: Sumarlin Sumarlin , Zarlis Muhammad , Suherman Suherman , Efendi Syahril
Published: April 2024

The ubiquitous computing system is a paradigm shift from personal computing to physical integration. This study focuses on the deterministic and stochastic service provider model to provide sub-services to computing nodes to minimize rejection values. This deterministic service provider model aims to reduce the cost of sending data from one place to another by considering the processing capacity at each node and the demand for each sub-service. At the same time, stochastic service provider aims to optimize service provision in a stochastic environment where parameters such as demand and capacity may change randomly. The novelties of this research are the deterministic and stochastic service provider models and algorithms with mixed integer programming (MIP). The test results show that the solution found meets all the constraints and the smallest objective function value. Stochastic modeling minimizes denial of service problems during wireless sensor network (WSN) distribution. The model resented is the ability of wireless sensors to establish connections between distributed computing nodes. Stochastic modeling minimizes denial of service problems during WSN distribution.
A triangle decomposition method for the mobility control of mecanum wheel-based robots
Authors: Olivier Akansie Kouame Yann , C. Biradar Rajashekhar , Karthik Rajendra , D. Devanagavi Geetha
Published: April 2024

Mobile robots are used in a variety of applications including research, education, healthcare, customer service, security and so on. Based upon the application, the robots employ different locomotion systems for their mobility. When it comes to rolling locomotion, the wheels used to provide mobility to robots can be categorized as: tracks, omnidirectional wheels, and unidirectional wheels with a steering system. The ability of omnidirectional wheels to drive machines in small spaces makes them interesting to use. Among the types of omnidirectional wheels, mecanum wheels are widely used due to their inherent benefits. With the right control strategy, robots equipped with mecanum wheels can move freely, in all possible directions. In this study, a triangle decomposition approach is employed for controlling omnidirectional mecanum wheel-based robots. The method consists of breaking down any path into a set of linear motions that can be horizontal, vertical, or oblique. Furthermore, the oblique paths are divided into smaller segments that can be resolved into a horizontal and vertical component in a right-angle triangle. The suggested control method is tested and proved on a simple scenario using Webots simulation software.