bluuebunny/crossref_metadata_2025 huggingface dataset:
Abstract IA07: Improving CAR T cell therapy efficacy in hematologic malignancies with low-dose total body irradiation
Authors: Guzman Monica L.
Published: January 2025

Chimeric Antigen Receptor (CAR) T cell therapy represents a significant advance in the treatment of hematologic malignancies, as demonstrated by the high response rates and improved survival in diseases such as acute lymphoblastic leukemia (ALL), non-Hodgkin lymphoma (NHL), Diffuse large cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL). The FDA has approved several CAR T cell therapies, including some approaches targeting CD19 (CART19) for adult and pediatric patients with B-cell malignancies. Response rates for CD19 CART therapy have been quite promising, reaching 80% overall response and 60% complete response; however, relapses are observed at an estimate of 30-40% within the first year. Thus, finding clinically translatable approaches to improve response rates for patients receiving CART19 therapies, such as the potential of LD-TBI, is critical. Multiple mechanisms of CAR T cell therapy resistance include limited persistence of the CAR T cell product upon infusion, CAR T cell exhaustion, and reduced or complete loss of target expression. Here, we will describe the significant role of radiation therapy, particularly low-dose total body irradiation (LD-TBI), in enhancing the long-term response of CAR T cell therapy. LD-TBI can modulate antigen stability, augment the expression of death receptors in tumor cells, and improve the persistence of CAR T cells. Moreover, LD-TBI also enhances lymphodepletion and the trafficking of CAR T cells to the tumor-infiltrated organs. As a result, LD-TBI significantly prolongs the survival of mice bearing lymphoma or B-ALL in combination with anti-CD19 CAR T cells, thereby improving patient outcomes and offering a promising future for cancer treatment.

Citation Format: Monica L. Guzman. Improving CAR T cell therapy efficacy in hematologic malignancies with low-dose total body irradiation. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr IA07
Abstract IA10: Immune side effects in ferroptosis-targeted therapy
Authors: Tang Daolin
Published: January 2025

Ferroptosis, a regulated form of cell death driven by lipid peroxidation, has emerged as a promising strategy in cancer therapy, complementing established modalities such as chemotherapy, immunotherapy, and radiotherapy. While the mechanisms of ferroptosis are highly context-dependent, the classical approach to inducing ferroptosis involves targeting the GPX4 (glutathione peroxidase 4) pathway. GPX4 is a critical enzyme that detoxifies lipid hydroperoxides (L-OOH) by reducing them to non-toxic lipid alcohols (L-OH) using glutathione (GSH) as a cofactor, thereby preventing lipid peroxidation and ferroptotic cell death. Although targeting GPX4 effectively induces ferroptosis in cancer cells, its widespread expression in both tumor and normal cells presents challenges. Direct inhibition of GPX4 can lead to unintended immune cell death, potentially impairing antitumor immunity by reducing the activity of cytotoxic T cells and other immune effectors essential for robust immune responses. This immunosuppressive effect could undermine the therapeutic benefits of ferroptosis-targeted therapies. To overcome this limitation, alternative strategies have been explored. For instance, non-direct targeting of GPX4, such as inducing TRIM25-mediated degradation of GPX4, has shown promise in selectively killing cancer cells while sparing normal tissues and preserving immune function. This approach minimizes toxicity and maintains the activity of key immune cells, including cytotoxic T cells, enhancing the overall efficacy of ferroptosis-based treatments. Understanding the potential risks and immune-related side effects of ferroptosis-targeted therapies is critical for their successful clinical translation.

Citation Format: Daolin Tang. Immune side effects in ferroptosis-targeted therapy. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr IA10
A new trade-off approach to photovoltaic power smoothing
Authors: Galarza Jose , Condezo David , Calcina Armando , Saenz Bartolome
Published: January 2025

Renewable energy sources, such as grid-connected photovoltaic systems, present a challenge due to the intermittent nature of solar irradiance. In this research, the authors have suggested an approach for irradiance profile smoothing based on different sample sizes of measurements. The analysis has been performed using experimental measurements based on the irradiance and temperature of the series smart pyranometers (SMP3) pyranometer manufactured by Kipp & Zonen. The power smoothing uses a variable window size to compensate for a flat and intermittent profile. The moving standard deviation has been used to evaluate the window size in different ranges. The evaluation focuses on days with higher irradiance variability and the results for the suggested approach show better performance than the smoothing process with a large or small window. The suggested approach reaches an autocorrelation index of 0.988, which is more than 6% with respect to the constant window size. Furthermore, the analysis shows that more than 50% of the data have variability in the moving standard deviation within a range of 1% and 25%, as with this approach a flexible window size helps the smoothing process. An estimation of photovoltaic power between constant and variable window size has been evaluated and a difference of less than 1.5% has been obtained.
Improved colored cubes teaching kit in representing and simplifying Boolean logic functions
Authors: Elewah Ibrahim , Jalaleddine Sara , Tbeileh Omar A. , Yainnakou Stavros , Kamzoul Azzam , El-Morhabi Sara , Tabet Maria Gergi , Hafez Rania
Published: January 2025

This work presents, explains, and discusses a colored variables cubes teaching kit. The cubes teaching kit is designed based on the cubes method that was developed to graphically simplify the Boolean logic functions with three, four, five, and six variables. This renewed method is developed to overcome the limitation of the conventional Karnaugh maps method in terms of simplifying Boolean functions with a maximum of four variables only. Students can use the teaching kit to place each cube in its right position. Based on the label of each cube, students will be able to figure out the function minterm number. After that, the students will sort the cubes to represent the function. Eventually, the students will develop the competency to check the cubes adjacency, and this will lead them to formulate simplified Boolean expressions. Students' engagement is expected to improve when theoretical knowledge is implemented using a three-dimensional physical cube teaching kit. The aim of this work is that both the educators and students, in engineering and engineering technology programs, can benefit from the adaptation and even more from the modification of the proposed approach to facilitate the achievement of their learning objectives.
Control to compensate reactive power at medium voltage load nodes to improve performance and load voltage stabilization based on modular multilevel converter
Authors: Cuong Tran Hung , Tung Nguyen Nhat , Anh An Thi Hoai Thu
Published: January 2025

This paper will present a reactive power control method for medium voltage power grids based on the modular multilevel converter (MMC) structure. In particular, the MMC converter applies control algorithms to operate as a D-STATCOM device. A proportional–integral (PI) controller combined with an improved nearest level modulation (NLM) method performs the system control process. The purpose is to create sinusoidal voltage levels on the alternating current (AC) side to generate or absorb reactive power according to load requirements. This will ensure that the amount of reactive power for the load node is always within the allowable value and improve voltage quality, increasing the power factor for the load. Verifying and evaluating results are performed on MATLAB/Simulink software.
Vehicle side control of a wireless power transfer charger using optimized artificial neural network
Authors: Ancary Marouane El , Lassioui Abdellah , Fadil Hassan El , Hasni Anwar , Asri Yassine El , Idrissi Zakariae El
Published: January 2025

This paper investigates a new approach to control a wireless power transfer (WPT) charger for electric vehicles (EVs) employing an optimized artificial neural network (ANN). Enhancing the efficiency and robustness of such systems is crucial, and integrating artificial intelligence (AI)-based solutions has introduced innovative approaches in this field. The proposed method enables precise regulation of battery charging voltage even under challenging conditions, such as coil misalignment or shared grounding assemblies for multiple EVs. To assess the stability and robustness of the proposed controller, its performance was evaluated under scenarios of coil misalignment and shared grounding assemblies for EVs with varying battery voltages. The controller effectively eliminated overshoot and significantly reduced residual output voltage ripple by 4.33% compared to a conventional proportional-integral (PI) controller, demonstrating the superior performance and reliability of the ANN-based control approach.
Multi-robot architecture based on hybridized blockchain model
Authors: Kumar Rahul Harish , Subramanian Gopalakrishnan Muthu , Bailuguttu Sahana
Published: January 2025

Multi-robot systems (MRS) are groups of robots that coordinate to complete a given task. In communication-based systems, the integrity of the information shared between robots becomes highly important as any security threat due to a malicious node in the system can cause a chain reaction to compromise the entire system. This paper proposes a hybridized blockchain model-based architecture (HBMA) built on robot operating system (ROS) which offers a semi-decentralized environment into which any communication-based algorithm can be plugged in. A security monitoring system is also provided with the architecture that identifies and shuts down malicious robots while also sending out alerts about the threat. This architecture is used to create secured, coupled approaches to localization of multi-robots and multi-robot path planning. This approach is validated on both physical robots and simulations run on ROS.
Development of twig dryness sensor for internet of things-based peatland fire early detection system
Authors: Muid Abdul , Aminah Nina Siti , Budiman Maman , Djamal Mitra
Published: January 2025

Peatland fires are a severe threat to the global environment. Existing peatland fire early detection systems commonly detect parameters such as air temperature, humidity, gas, smoke, and fire. This paper proposes a new peatland fire early detection method using the twig moisture content parameter. This method utilizes the most significant parameter approach for fire vulnerability compared to current peatland fire early detection systems. In particular, we developed an internet of things (IoT)-based twig dryness sensor to realize a field-applicable system. We propose a twig dryness sensor using the resistive sensing method, which employs a needle electrode to measure twig moisture content. Using the twig dryness sensor, three classifications of flammability were obtained, namely very difficult (moisture above 30%), difficult (moisture between 5%-30%) and easy (moisture less than 5%). This device utilizes readily available compact and portable materials. This instrumentation is digitally controlled with a low- power consumption microcontroller and long range (LoRa) transmitter, providing a long-life battery and long-range data transmission. Sensor data visualization is presented as twig dryness values and categorized according to fire vulnerability levels. The proposed system provides real-time and sustainable measurement.
Two-scale decomposition and deep learning fusion for visible and infrared images
Authors: Azad Ruhan Bevi , Unnikrishnan Hari , Gopinath Lokesh
Published: January 2025

The paper focuses on the fusion of visible and infrared images to generate composite images that preserve both the thermal radiation information from the infrared spectrum and the detailed texture from the visible spectrum. The proposed approach combines traditional methods, such as two-scale decomposition, with deep learning techniques, specifically employing an autoencoder architecture. The source images are subjected to two-scale decomposition, which extracts high-frequency detail and low-frequency base information. Additionally, an algorithmic unravelling technique establishes a logical connection between deep neural networks and traditional signal processing algorithms. The model consists of two encoders for decomposition and a decoder after the unravelling operation. During testing, a fusion layer merges the decomposed feature maps, and the decoder generates the fused image. Evaluation metrics including entropy, average gradient, spatial frequency and standard deviation are employed to subjectively assess fusion quality. The proposed approach demonstrates promise for effectively combining visible and infrared imagery for various applications.
A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification
Authors: Rathinam Vinoth , Rajendran Sasireka , Krishnasamy Valarmathi
Published: January 2025

A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care.
Key management for bitcoin transactions using cloud based key splitting technique
Authors: Buchade Amar , Sharma Nakul , Jadhav Varsha , Nalavade Jagannath , Sapate Suhas , Sajjan Rajani
Published: January 2025

Bitcoin wallet contains the information which is required for making transactions. To access this information, user maintains the secret key. Anyone with the secret key can access the records stored in bitcoin wallet. The compromise of the key such as physical theft, side channel attack, sybil attack, DoS attack and weak encryption can cause the access of transactional details and bitcoins stored in the wallet to the attacker. The cloud-based key split up technique is proposed for securing the key in blockchain technology. The key shares are distributed across virtual machines in cloud computing. The approach is compared to the existing key management approaches such as local key storage, keys derived from password and hosted wallet. It is observed that our approach is most suitable among the other key management approaches.
Stress detection through wearable sensors: a convolutional neural network-based approach using heart rate and step data
Authors: Wijaya Rifki , Kosala Gamma
Published: January 2025

With the current technological advancements, particularly in sensing technologies, monitoring various health aspects, including heart rate, has become feasible. The problem addressed in this study is the need for effective stress detection methods to mitigate the significant consequences of high-intensity or long-term stress, which impacts safety and disrupts normal routines. We propose a stress detection system developed based on the convolutional neural network (CNN) method to address this. The study involves university students aged 20–22, focusing on mental stress. The dataset encompasses parameters such as heart rate, footsteps, and resting heart rate recorded through a smartwatch with 149,797-row data. Our results indicate that the CNN model achieves an 84.5% accuracy, 80.9% precision, 79.8% recall, and an 80.4% F1-score, confirming its efficacy in stress classification. The confusion matrix further validates the model’s accuracy, particularly for classes 1 and 2. This research contributes significantly to the development of an effective and practical stress detection method, holding promise for enhancing well-being and preventing stress-related health issues.
Enhancing engineering education through virtual reality: a systematic study on immersive engineering education practices
Authors: Riaji Tarek , Hassani Sanae El , Alaoui Fatima Ezzahrae M'hamdi
Published: January 2025

This article explores the integration of virtual reality (VR) and associated technologies in engineering education, focusing on the pedagogical approaches adopted in this integration, which we refer to as immersive engineering education. This study considers the application possibilities and the transformative impact of VR on engineering education. The article addresses the critical collection and analysis of VR applications in engineering education. It covers main VR-related papers published from 2015 to February 2024 and indexed in Scopus, Web of Sciences, or both, and discussing design, development challenges, and collaborative tools. Empirical evidence showcases improved engagement, motivation, and learning outcomes. The findings offer modern insights for educators and researchers on leveraging VR for impactful learning experiences, while also noting the need for further research in this evolving field.
Development and analysis of symmetric encryption algorithm
Authors: Khompysh Ardabek , Dyusenbayev Dilmukhanbet , Maxmet Muratkhan
Published: January 2025

This paper introduces a new block encryption algorithm designed for the cryptographic protection of data. The paper introduces and explains a newly devised exponentiation modulo (EM) transform method, utilized to obtain the S-block, an essential element within the presented algorithm. A method of optimizing the choice of keys and increasing the efficiency of calculation was also used. It is proposed that incorporating characteristics of cryptographic primitives functioning within the Galois field into the algorithm can lead to favorable outcomes. To increase the encryption algorithm's speed, non-positional polynomial notation systems and a working base index table are used. The paper discusses the implementation of an encryption algorithm in C++ and examines the statistical characteristics of the resulting ciphertexts. For experimental testing of statistical safety, a set of statistical tests by National Institute of Standards and Technology (NIST) and D. Knuth was used. Furthermore, the resulting S-box was examined using linear, differential, and algebraic cryptanalysis techniques. In the future, this proposed S-box will be implemented in the encryption algorithm being developed for the preliminary encryption of confidential data.
Embedded systems and artificial intelligence for enhanced humanoid robotics applications
Authors: Hdid Jalal , Lamsellak Oussama , Benlghazi Ahmad , Benali Abdelhamid , El Melhaoui Ouafae
Published: January 2025

This paper presents a method for collecting precise hand gesture (HG) data using a low-cost embedded device for an embedded artificial intelligence (EAI)-based humanoid robotics (HR) application. Despite advancements in the field, challenges remain in deploying cost-effective methods for accurately capturing and recognizing body gesture data. The ultimate objective is to develop humanoid robots (HRS) capable of better understanding human activities and providing optimal daily life support. In this regard, our approach utilizes a Raspberry Pi Pico microcontroller with a 3-axis accelerometer and a 3-axis gyroscope motion sensor to capture real- time HG data, describing ten distinct real-world tasks performed by the hand in experimental scenarios. Collected data is stored on a personal computer (PC) via a micro-python program, forming a dataset where tasks are classified using ten supervised machine learning (SML) models. Two classification experiments were conducted: the first involved predicting raw data, and the second applied normalization and feature extraction (FE) techniques to improve prediction performance. The results showed promising accuracy in the first phase (89% max), with further improvements achieved in the second phase (94% max). Finally, by employing similar methods, we can integrate highly trained machine learning (ML) models into embedded humanoid robotic systems, enabling real-time human assistance.
Topographic and flow direction model: a case study of Khuan Kreng peat swamp forest, Southern Thailand
Authors: Musik Panjit , Limchoowong Nunticha , Sricharoen Phitchan , Nateewattana Jintapat , Amnuaywattanakul Tanutta , Chansuvarn Woravith , Wanthong Uraiwun
Published: January 2025

Floods and droughts are contrasting natural phenomena. The risk of forest fires tends to be increased by the dry and hot conditions of the dry season. A topographic and flow direction model is aimed to be created using Mathematica and ArcGIS programs. The purpose of this model is to assist in water management to prevent forest fires in the Khuan Kreng peat swamp forest located in Nakhon Si Thammarat Province, Southern Thailand. Digital elevation models obtained from the Department of Land Development, representing altitude data of the terrain at a scale of 1:4,000, are utilized in this work. Using cellular automata principles with eight sub-cell flow pathways with a precision of 5×5 meters, identification was carried out. The Universal Transverse Mercator (UTM) coordinate system can store horizontal (X, Y) and vertical (Z) data in one cell, providing information about 2D and 3D topography. Our findings regarding flow direction are comparable to reference values for summer under dry conditions, where water mass is limited. The topographic model data was found to be compatible with data obtained from ArcGIS, Google Maps, and surveys. The ArcGIS flow modeling results are found to be suitable for flood simulation. The proposed method is applicable for regulating water use during droughts and preventing forest fires.
Design of an educational platform based on an innovative model of research in secondary school students
Authors: Andrade-Arenas Laberiano , Corzo-Zavaleta Janet Ivonne , Alvarado-Paucar Ada , Baldeón-Vilca Cecilia , Segovia-Fernández Luis , Reyes-Vilca Nelly , López-Tolentino Giovana , Villarreal-Chumbes Verónica , Canturín-Narrea Jhon
Published: January 2025

The development of research skills worldwide is more emphasized in postgraduate programs; however, the training of these skills should be carried out from basic education; that is, from elementary school. In this sense, this research aims to formulate a proposal to develop research skills in secondary school students through an innovative model. The methodology was carried out through student surveys and interviews with teachers and authorities. The ATLAS.ti 22 software was used for network analysis and SPSS 23 for statistical analysis. The results obtained in the survey show that the dimensions of reading comprehension, writing and argumentation, academic writing, and scientific writing are within the acceptable average. However, in the interviews, some students show difficulties in scientific writing, but they show a critical position in their arguments. It is concluded that the authorities should incorporate the proposed model of research skills in the curricular plan, adding it to their annual plan; for this purpose, teachers should be trained to transmit it to their students. In addition, an innovative model is proposed during the 5 years of high school studies to develop students' research skills. The beneficiaries of the proposal are the entire educational community and therefore the country.
Underwater energy harvesting model for agricultural applications using stochastic network calculus
Authors: Vignesh S. R. , Sukumaran Rajeev
Published: January 2025

Underwater wireless sensor network (UWSN) is a specialized type of wireless sensor network (WSN) designed for underwater communication among sensor nodes deployed in oceans for monitoring purposes such as observing marine life, detecting pollutants, and keeping track of oceanographic conditions. Managing limited energy in harsh underwater environments presents unique challenges compared to terrestrial networks. This research addresses this challenge by developing a reliable energy harvesting model. It analyzes the effects of delay and energy storage constraints on the energy harvesting rate (EHR), a measure of the energy replenished over time to maintain sensor node operations. It quantifies the amount of energy that can be harvested and stored within a given period, which is crucial for sustaining the network's functionality. The study includes analyzing and simulating the model analytically using discrete event simulators to evaluate delay performance bounds. Simulation results indicate that larger packet sizes require a higher minimum EHR, while stricter delay requirements decrease it for a fixed arrival rate.
A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks
Authors: Loughmari Mohamed , El Affar Anass
Published: January 2025

Wireless sensor networks (WSNs) are increasingly prevalent in the Internet of Things ecosystem and have been used in several fields such as environmental monitoring, military, and healthcare. However, their limited resources and distributed architecture remain two main challenges: energy and security. Furthermore, denial of service (DoS) attacks are one of the principal cyber threats to WSNs. This research proposes a lightweight machine learning (ML) approach based on the extreme gradient boosting (XGBoost) model to detect these attacks in WSNs. Through an extensive investigation, we evaluate four prominent ML algorithms: random forest (RF), k-nearest neighbor (KNN), stochastic gradient descent (SGD), and XGBoost, using the WSN-DS dataset. In addition, we implement and investigate several feature selection techniques in order to have an improved version of the original dataset. Moreover, we evaluate the performance using various performance metrics, which include accuracy, precision, recall, F1-score, and processing time. The latter is a crucial consideration in WSN environments. For validation, we have employed 5-fold cross-validation to ensure robust and reliable results. The proposed model has achieved good performance in all metrics, with a maximum accuracy of up to 99.73%, and a 68% lower processing time compared to the other investigated classifiers.
Nonlinear regression analysis to predict mandibular landmarks on panoramic radiographs
Authors: Nafiiyah Nur , Hanifah Ayu Ismi , Susanto Edy , Astuti Eha Renwi , Fatichah Chastine , Putra Ramadhan Hardani , Akbar Agus Subhan
Published: January 2025

An automatic system for determining mandibular landmark points on panoramic radiography can reduce errors due to differences in expert professionalism and save time. Previous research has shown that the linear regression method is ineffective at predicting condyle and gonion landmark points in panoramic radiography. So, this research proposes an analysis of nonlinear regression methods (support vector machine (SVM) kernel=‘polynomial’, polynomial regression, ensemble regression) for predicting condyle and gonion landmark points. There are four predicted landmark points, namely the right condyle, left condyle, right gonion, and left gonion. The nonlinear regression methods used are SVM, polynomial regression, and ensemble regression. The Dental and Oral Hospital, within the Faculty of Dentistry at Universitas Airlangga, provides the research data. The research encompasses 119 patients between the ages of 19 and 70, dividing 103 into training and 16 into testing. The research results show that the SVM method is only good at predicting the right condyle point with a mean radial error (MRE) of 4,724 pixels. Meanwhile, to predict the left condyle, right gonion, and left gonion points, it is better to use the polynomial regression method and ensemble regression with an order of success detection rate (SDR) of 37.5%, 18.75%, and 12.5%, respectively.
Homomorphic encryption, privacy-preserving feature extraction, and decentralized architecture for enhancing privacy in voice authentication
Authors: Murugesan Kathiresh , Subbarayalu Ramamurthy Lavanya , Palanisamy Boopathi , Chandrasekar Yamini , Shanmugam Kavitha Masagoundanpudhur , Nithya Balluru Thammaiahshetty Adishankar , Thiyagaraja Velumani , Muniappan Ramaraj
Published: January 2025

This paper introduces a novel framework designed to bolster privacy protections within automated voice authentication systems, addressing mounting concerns as voice-based authentication grows in prominence. The widespread adoption of these systems has underscored apprehensions regarding the storage and processing of sensitive voice biometric data without adequate safeguards. To mitigate these risks, a modified framework is proposed, aiming to enhance privacy without compromising authentication accuracy and efficiency. Three key techniques are implemented to address these challenges. Firstly, advanced encryption methods are employed for secure voice data storage and transmission, through the homomorphic encryption to enable authentication processing on encrypted data. Secondly, a privacy-preserving feature extraction method is introduced, transforming raw voice inputs into irreversible representations to shield original biometric information. Additionally, the framework incorporates differential privacy mechanisms, adding controlled noise to aggregated voice data to prevent individual identification within large datasets. A user-centric consent and control model is proposed, empowering individuals to manage their voice profiles and authentication settings. Experimental findings demonstrate that the framework achieves enhanced authentication accuracy while markedly reducing privacy risks compared to conventional systems. This contribution addresses the ongoing challenge of balancing security and privacy in biometric authentication technologies.
Optimization techniques applied on image segmentation process by prediction of data using data mining techniques
Authors: Muniappan Ramaraj , Selvaraj Srividhya , Vanathi Gurusamy Rani , Thiyagarajan Velumani , Sabareeswaran Dhendapani , Prasanth David , Krithika Varadharaj , Ilango Bhaarathi , Subramanian Dhinakaran
Published: January 2025

The research work presents an enhanced method that combines rule-based color image segmentation with fuzzy density-based spatial clustering of applications with noise (FDBSCAN). This technique enhances super-pixel robustness and improves overall image quality, offering a more effective solution for image segmentation. The study is specifically applied to the challenging and novel task of predicting the age of tigers from camera trap images, a critical issue in the emerging field of wildlife research. The task is fraught with challenges, particularly due to variations in image scale and thickness. Proposed methods demonstrate that significant improvements over existing techniques through the broader set of parameters of min and max to achieve superior segmentation results. The proposed approach optimizes segmentation by integrating fuzzy clustering with rule-based techniques, leading to improved accuracy and efficiency in processing color images. This innovation could greatly benefit further research and applications in real-world scenarios. Additionally, the scale and thickness variations of the present barracuda panorama knowledge base offer many advantages over other enhancement strategies that have been proposed for the use of these techniques. The experiments show that the proposed algorithm can utilize a wider range of parameters to achieve better segmentation results.
Application of machine learning methods to analysis and evaluation of distance education
Authors: Mukhiyadin Ainur , Mukasheva Manargul , Makhazhanova Ulzhan , Kassekeyeva Aislu , Azieva Gulmira , Kenzhebayeva Zhanat , Abdrakhmanova Alfiya
Published: January 2025

In recent decades, distance learning has become an essential component of the modern educational system, providing students with flexibility and access to knowledge regardless of location. This paper discusses creating a hybrid machine-learning model for assessing the quality of distance learning based on survey data. The model combines two feature extraction methods: Term frequency-inverse document frequency (TF-IDF) and Word2Vec. Combining these methods allows for a more complete and accurate representation of text data, improving the quality of machine learning models. The study aims to develop and evaluate the effectiveness of the proposed hybrid model for analyzing survey data and assessing the quality of distance learning. The paper considers the tasks of collecting and preprocessing text data, experimentally comparing various feature extraction methods and their combinations, training and evaluating a machine learning model based on a combination of TF-IDF and Word2Vec features, as well as analyzing the results and assessing the effectiveness of the proposed model using various metrics. In conclusion, the prospects for further development and application of the proposed model in educational institutions to improve the quality of distance learning are discussed.
Advancing network security: a comparative research of machine learning techniques for intrusion detection
Authors: Rysbekov Shynggys , Aitbanov Abylay , Abdiakhmetova Zukhra , Kartbayev Amandyk
Published: January 2025

In the current digital era, the advancement of network-based technologies has brought a surge in security vulnerabilities, necessitating complex and dynamic defense mechanisms. This paper explores the integration of machine learning techniques within intrusion detection systems (IDS) to tackle the intricacies of modern network threats. A detailed comparative analysis of various algorithms, including k-nearest neighbors (KNN), logistic regression, and perceptron neural networks, is conducted to evaluate their efficiency in detecting and classifying different types of network intrusions such as denial of service (DoS), probe, user to root (U2R), and remote to local (R2L). Utilizing the national software laboratory knowledge discovery and data mining (NSL-KDD) dataset, a standard in the field, the study examines the algorithms’ ability to identify complex patterns and anomalies indicative of security breaches. Principal component analysis is utilized to streamline the dataset into 20 principal components for data processing efficiency. Results indicate that the neural network model is particularly effective, demonstrating exceptional performance metrics across accuracy, precision, and recall in both training and testing phases, affirming its reliability and utility in IDS. The potential for hybrid models combining different machine learning (ML) strategies is also discussed, highlighting a path towards more robust and adaptable IDS solutions.
A constrained convolutional neural network with attention mechanism for image manipulation detection
Authors: Hamidja Kamagate Beman , Tokpa Fatoumata Wongbé Rosalie , Mosan Vincent , Oumtanaga Souleymane
Published: January 2025

The information disseminated by online media is often presented in the form of images, in order to quickly captivate readers and increase audience ratings. However, these images can be manipulated for malicious purposes, such as influencing public opinion, undermining media credibility, disrupting democratic processes or creating conflict within society. Various approaches, whether relying on manually developed features or deep learning, have been devised to detect falsified images. However, they frequently prove less effective when confronted with widespread and multiple manipulations. To address this challenge, in our study, we have designed a model comprising a constrained convolution layer combined with an attention mechanism and a transfer learning ResNet50 network. These components are intended to automatically learn image manipulation features in the initial layer and extract spatial features, respectively. It makes possible to detect various falsifications with much more accuracy and precision. The proposed model has been trained and tested on real datasets sourced from the literature, which include MediaEval and Casia. The obtained results indicate that our proposal surpasses other models documented in the literature. Specifically, we achieve an accuracy of 87% and a precision of 93% on the MediaEval dataset. In comparison, the performance of methods from the literature on the same dataset does not exceed 84% for accuracy and 90% for precision.
An innovative Arabic light stemmer developed using a hybrid approach
Authors: Namly Driss , Bouzoubaa Karim
Published: January 2025

Our study introduces an innovative light stemming tool tailored for Arabic morphology challenges. In conformance with the templatic and concatenative structures, our stemmer utilizes a combination of clitic stripping, lexicon-based, and statistical disambiguation techniques to ensure accurate stemming. To accomplish this, we rely on our clitic rules lexicon to detect all potential combinations of clitics for each input entry. Subsequently, we depend on an extensive lexicon of over 7 million stems to verify the potential stems. Lastly, we employ a statistical model to ascertain the most likely stem based on the sentence's context. Experimental results demonstrate the effectiveness of the proposed stemmer in comparison with existing ones. Using different datasets, our stemmer achieves higher accuracy and F1 scores, highlighting its efficiency in Arabic stemming tasks.
A comprehensive analysis of different models: skin cancer detection
Authors: Thorat Amruta , Jadhav Chaya
Published: January 2025

Due to fast-growing worldwide air pollution and ozone layer destruction, an alarming number of people are found to have skin cancer, more than any other kind of cancer combined. It is known to be one of the deadliest malignancies; if not identified and cured in its early stages, it is likely to spread to other body parts. Early detection is critical and helps prevent cancer from spreading. This allows for early decisions on diagnostic and treatment options. Early diagnosis and discovery, combined with the right treatment, can save lives. In this paper, we have done a detailed survey on various techniques and models developed for skin cancer detection and also discussed different security-related issues. This work thoroughly explores the several types of models utilized to identify cancer in the skin.
Evaluating tumor heterogeneity in oncology with genomic-imaging and cloud-based genomic algorithms
Authors: Gurulakshmanan Gurumoorthi , Amarnath Raveendra N. , Lebaka Sivaprasad , Reddy Munnangi Koti , Mohankumar Nagarajan , Muthumarilakshmi Surulivelu , Srinivasan Chelliah
Published: January 2025

The goal of this initiative is to rethink how oncology is traditionally practiced by integrating novel approaches to genomic imaging with cloud-based genomic algorithms. The research intends to give a thorough knowledge of cancer biology by focusing on the decoding of tumor heterogeneity as its primary objective. It is possible to get a more nuanced understanding of the intricacy of tumors via the integration of high-resolution imaging tools and sophisticated genetic analysis. It is a pioneering use of cloud computing, which enables the quick analysis of large genomic information. The major goal is to decipher the complex genetic variants that are present inside tumors in order to direct the creation of individualized treatment strategies. This discovery marks a significant step forward, since it successfully bridges the gap between genetics and imaging. Diagnostic accuracy and treatment effectiveness have both been improved. This innovative technique permits real-time analysis, which in turn enables treatment tactics to be adjusted in a timely manner. It makes a significant contribution to the continuous development of oncological research as well as its translation into better clinical outcomes for cancer patients.
D-RAKE compression for enhanced internet of things data management in air quality monitoring
Authors: Sari Kartika , Hidayati Rahmi
Published: January 2025

This study addresses the issue of air pollution in Pontianak, marked by high levels of pollutant particles and chemical compounds that cause respiratory health risks. The research involves essential air quality monitoring using various sensors for temperature, humidity (DHT22), O2 (MQ-135), CO (MQ-7), CO2 (MG-811), and dust (GP2Y1010AU0F), collected real-time, leading to a notable increase in data volume. Due to limitations in internet of things (IoT) devices, there is a need for integration between cloud and IoT through data transmission to reduce the communication time and memory usage. The escalation in sensor data volume requires a lossless compression technique to ensure efficient storage without sacrificing crucial information. Compression plays a vital role in overcoming complex storage challenges, facilitating real-time data access for monitoring, and contributing to sustainable efforts to improve air quality in Pontianak. This research applies the D-RAKE compression method based on basic counting procedures with minimal memory requirements, cost-effective, low-speed microcontrollers commonly used in IoT devices. Despite its simplicity, simulation results indicate that the D-RAKE algorithm outperforms well-established compression methods such as gzip, bzip2, and rar, particularly for data sequences with sparse elements. Moreover, when applied to real- world data, D-RAKE achieves superior compression ratios compared to IoT-focused compression techniques.
Tackling the anomaly detection challenge in large-scale wireless sensor networks
Authors: Zhukabayeva Tamara , Adamova Aigul , Zholshiyeva Lazzat , Mardenov Yerik , Karabayev Nurdaulet , Baumuratova Dilaram
Published: January 2025

One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbor (kNN) and Z Score methods is evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z Score methodology showed a 98.9% level of accuracy, which was much superior to the kNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z Score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN.
A HBMO-based batch beacon adjustment for improving the Fast-RRT
Authors: Suwoyo Heru , Tian Yingzhong , Adriansyah Andi , Andika Julpri
Published: January 2025

Fast-RRT improves on the original rapidly-exploring random trees (RRT) by incorporating two main stages: improved-RRT and fast-optimal. The improved-RRT stage enhances the search process through fast-sampling and random steering, while the fast-optimal stage optimizes the path using fusion and path arrangement. However, path fusion can only be optimal when the newly found path is unique and different from previous paths. This uniqueness rarely occurs in cases with narrow corridors, so path fusion only provides suboptimal conditions. To address this, the study explores using honey bee mating optimization (HBMO) to optimize or replace the fusion stage. HBMO helps determine new beacon coordinates, which are nodes between the start and goal points along the path, through a batch beacon adjustment approach. The results show that integrating HBMO into FastRRT improves its optimality, with a 21.85% reduction in path cost and a 5.22% decrease in completion time across environments with varying difficulty levels. This hybrid algorithm outperforms previous methods in terms of both path optimality and convergence rate, demonstrating its effectiveness in enhancing Fast-RRT’s performance.
Enhanced hippopotamus optimization algorithm for power system stabilizers
Authors: Aribowo Widi , Mzili Toufik , Sabo Aliyu
Published: January 2025

This article presents techniques for modifying the power system stabilizer's (PSS) parameters. An enhanced version of the hippocampal optimization algorithm (HO) is presented here. HO represents a novel approach in metaheuristic methodology, having been inspired by the observed clinging behavior in hippos. The notion of the HO is defined using a trinary-phase model that includes their position updates in rivers or ponds, defensive techniques against predators, and mathematically described evasive methods. To confirm the efficacy of the recommended approach, this article provides comparison simulations of the PSS objective function and transient response. This study employs validation through a comparison between Original HO and conventional methods. Simulation results demonstrate that, when compared to competing algorithms, the suggested approach yields optimal results and, in some cases, exhibits fast convergence. It is known that, in comparison to the original HO approach, the recommended way can lower the average undershoot of the rotor angel and speed by 12.049% and 26.97%, respectively.
Development of clustering with Bayesian algorithm for optimal route formation in software-defined radio underwater WSN
Authors: Sreeraj Anoop , P Vijayalakshmi , Rajendran Velayutham
Published: January 2025

Underwater wireless sensor networks (UWSNs) have recently offered chances to investigate oceans and thus enhance the underwater world. WSNs are imperative for discovering the ocean region. Software-defined networking (SDN) improves flexibility and uses the clustering method to improve lifespan. This article introduces the Development of a clustering process with a Bayesian algorithm (CPBA) for optimal route formation in software-defined radio UWSN. The clustering concept improves energy efficiency; however, cluster head (CH) selection is challenging. The present clustering mechanisms could be more successful in suitably assigning the node's energy. This mechanism utilizes a slap swarm optimization algorithm to pick out the optimal CH by node energy and distance among inter-cluster as well as intra-cluster. In addition, the Bayesian algorithm selects the best forwarder from sender to base station. Thus, enhances efficiency. The simulation results demonstrate that the UWSN improves both the 23% packet forward ratio and 0.014 joule energy. Furthermore, it minimizes the 30% network delay.