bluuebunny/crossref_metadata_2025 huggingface dataset:
A novel multi-objective economic load dispatch solution using bee colony optimization method
Authors: Khamsen Wanchai , Takeang Chiraphon , Janthawang Thawinan , Aurasopon Apinan
Published: January 2025

This article presents a novel multi-objective economic load dispatch solution with the bee colony optimization method. The purposes of this research are to find the lowest total power generation cost and the lowest total power loss at the transmission line. A swarm optimization method was used to consider the non-smooth fuel cost function characteristics of the generator. The constraints of economic load dispatch include the cost function, the limitations of generator operation, power losses, and load demand. The suggested approach evaluates an IEEE 5, 26, and 118 bus system with 3, 6, and 15 generating units at 300, 1,263, and 2,630 megawatt (MW) and uses a simulation running on the MATLAB software to confirm its effectiveness. The outcomes of the simulation are compared with those of the exchange market algorithm, the cuckoo search algorithm, the bat algorithm, the hybrid bee colony optimization, the multi-bee colony optimization, the decentralized approach, the differential evolution, the social spider optimization, and the grey wolf optimization. It demonstrates that the suggested approach may provide a better-quality result faster than the traditional approach.
Comparison design of dynamic voltage restorers, distribution static compensators and unified power quality conditioner series shunts on voltage sag, and voltage swell
Authors: Siregar Yulianta , Mubarok Syahrun , Mohamed Nur Nabila
Published: January 2025

One issue with the power system is electrical power quality, which is brought on by short circuit disruptions and growing nonlinear loads. Power systems frequently have short circuits, resulting in voltage sags that can harm delicate loads. Voltage sage and swell issues can be resolved using unified power quality conditioner series shunts (UPQC-S), distribution static compensators (DSTATCOM), and dynamic voltage restorers (DVR). Custom power devices are very useful in overcoming problems with electrical networks. In this research, due to 3-phase short circuit faults, voltage sag and swell simulations were conducted using a load equal to 70% of the total load and a fault location point of 75% of the feeder length, from the results of research conducted with the case study PT. PLN (Persero) UP3 Sibolga Feeder SB 02 shows that DVR performs better than DSTATCOM and UPQC-S in handling voltage sag and voltage swell due to 3-phase short circuit disturbances. The DVR succeeded in providing the largest voltage sag recovery in phase C, increasing the voltage from 0.2481 pu to 0.9776 pu. The DVR is also effective in overcoming voltage swell on phase A, reducing it from 1.724 pu to 0.9969 pu.
Optimized control and simulation of a grid-integrated photovoltaic and fuel cell hybrid power system
Authors: Satif Amal , Hlou Laamari , Elgouri Rachid
Published: January 2025

In this study, we propose an advanced power control strategy tailored for a grid-connected photovoltaic-hydrogen hybrid system aimed at ensuring reliable energy supply to consumers. This hybrid system integrates photovoltaic (PV) panels as the primary energy source supplemented by hydrogen fuel production through electrolysis of surplus PV-generated electricity. To optimize system flexibility and efficiency, we employ several advanced control techniques. A novel maximum power point tracking (MPPT) method, enhanced with a proportional-integral (PI) controller, surpasses conventional perturb and observe (P&O) techniques to maximize PV power output. For precise control of the three-phase photovoltaic inverter, we utilize a space vector pulse width modulation (SVPWM) algorithm. Synchronization with the utility grid is ensured by a phase-locked loop (PLL), maintaining phase coherence between the inverter output and grid supply. The primary goal is to develop a comprehensive and efficient control strategy for grid-connected hybrid systems, addressing both energy generation and storage challenges. MATLAB simulations validate the system's performance, demonstrating high energy conversion efficiency and robust control across varying conditions. This study underscores the potential of hybrid PV-hydrogen systems to provide sustainable and resilient energy solutions for future grid integration.
Design of low power complementary metal-oxide semiconductor static random-access memory cell for embedded memories at three different technology nodes
Authors: Venkatesh Seerapu , Sahukara Krishna Veni
Published: January 2025

In this work, the complementary metal-oxide semiconductor (CMOS) static random-access memory (SRAM) cell is proposed using a hybrid model. It is designed by combining two different methods and simulated at different technologies which are 180, 90, and 45 nm. The proposed hybrid model SRAM cell has less power consumption. The power consumption results of the hybrid model SRAM cell are contrasted with the 6T CMOS SRAM, Stacked SRAM cell, and 8T SRAM cell at 180, 90, and 45 nm. Tanner tool was used for designing and simulating these different SRAM cell topologies at 180, 90, and 45 nm technology nodes. S-edit is used for designing circuit diagrams, T-edit is used for simulating spice net lists and W-edit is used for observing waveforms in Tanner tool. The hybrid SRAM cell at 45 nm got better power consumption results than other SRAM cell topologies at different technology nodes.
Tilted fiber Bragg grating based optical sensor for simultaneous measurement of vital signs: a novel approach
Authors: Arumugam Ramya , Kumar Ramamoorthy , Dhanalakshmi Samiappan
Published: January 2025

Vital signal monitoring acts as a highly significant diagnostic method. Continuous monitoring of vital signs like temperature, heart rate, and blood pressure aids in easy and fast disease diagnosis. Existing methods that enable continuous monitoring, such as smart watches, are not accurate enough to be used as a benchmark for diagnosing diseases. In this paper, we propose simultaneous temperature and heart rate measurement using tilted fiber Bragg grating (TFBG) which enables concurrent measurement of measurands due to its intrinsic optical property. Temperature and heart rate were considered for measurement. The proposed TFBG-based optical sensor has a sensitivity of 11.81 pm/°C and 1.73 pm/µε towards variation in temperature and strain, respectively. The sensitivity was determined by a shift in the wavelength of core mode resonance for temperature and a differential change in the wavelength of cladding mode resonance for heart rate. The average response time of the proposed TFBG sensor was found to be 3 seconds. Accuracy of 99.68% and 98.42% were achieved in temperature and heart rate measurements by the proposed TFBG sensor. The acceptability of the proposed TFBG sensor was analyzed using the Bland- Altman plot.
Trust factor validation for distributed denial of service attack detection using machine learning
Authors: Jayakumar Raghvin Manju , Bharamagoudra Manjula R. , Dash Ritesh
Published: January 2025

Distributed denial of service (DDoS) attacks, predicted to be 100 Gbps and greater, are expected to begin in the first quarter of 2019, with 77% of all attacks concentrated on at least two vectors. According to a Neustar Research Agency assessment, DDoS attacks are becoming more powerful and common. Among many other issues, distributed denial of service is a notable security issue. A large number of research projects have been conducted to address this issue, but their methodologies are either inaccurate or computationally expensive, making developing an effective DDoS assault detection method a critical demand of current research. A DDoS attack employs a huge number of agents or resources to carry out the attack, resulting in a large-scale attack power. The attackers use their intelligence to discover the weak system, which is then coordinated and managed remotely. The suggested detection framework uses a frequent time interval balancing module with node trust factor validation (FTIBM-NTFV) that is used to identify the DDoS attacks in the system for improving the security levels of the network. The proposed model is compared with the traditional methods and the results are analyzed that represents the proposed model is achieving better outcomes.
Developing an algorithm for the adaptive neural network for direct online speed control of the three-phase induction motor
Authors: Al-Mahasneh Ahmad J. , Salah Samer Z. , Ghaeb Jasim A. , Baniyounis Mohammed
Published: January 2025

In this paper, an online adaptive general regression neural network (OAGRNN) is presented as a direct online speed controller for a three-phase induction motor. To keep the induction motor running at its rated speed in real-time and under a variety of load conditions, the speed error and its derivative are continuously measured and fed back to the OAGRNN controller. The OAGRNN controller provides the inverter with the control signal it needs to produce the proper frequency and voltage for the induction motor instantly. Notably, the OAGRNN controller demonstrated remarkable performance without the need for a learning mode; it was able to track the desired motor speed, starting its operation from scratch. A setup utilizing a three-phase induction motor has been developed to show the high capacity of OAGRNN for tracking the desired speed of the motor while subjected to the varied load torque. The performance of OAGRNN is examined in two phases: the MATLAB simulation and the experimental setup. Furthermore, when the OAGRNN performance is compared with that of the proportional integral (PI) controller, it demonstrates its outstanding ability and superiority for online adjustments related to the three-phase induction motor's speed control.
System level optimization of series hybrid electric vehicle through plug-in charging feature using ADVISOR
Authors: Hassan Zain ul , Ahmad Naseer , Sohaib Muhammad
Published: January 2025

This research addresses the optimization of series hybrid electric vehicles (SHEVs) to enhance sustainable transportation by integrating a plug-in charging feature. The primary objective is to extend the range and improve battery management. Using MATLAB Simulink and the advanced vehicle simulator (ADVISOR), three SHEVs scenarios were simulated under the urban dynamometer driving system (UDDS) cycle. The study maintains constant parameters for the fuel converter and generator while optimizing the battery and motor controller. Compared to conventional hybrid electric vehicles (HEVs), this optimized SHEVs demonstrates a 17% improvement in battery thermal management and a 13.5% reduction in power losses. Additionally, the plug-in series hybrid electric vehicle (P-SHEVs) configuration shows a 5.26% increase in power output and a 35.71% improvement in the state of charge (SOC) over standard SHEVs configurations. The P-SHEVs design also achieves a 12.20% increase in the UDDS single-cycle range and an 11.5% reduction in fuel consumption. The integration of the electric vehicle (EV) charging feature further enhances the SHEVs, resulting in an 8.33% boost in motor power input and a 6.35% improvement in motor temperature profile, reaching a peak enhancement of 50% (18 kW). It contributes to the field by demonstrating the effectiveness of optimized configurations and the integration of a plug-in charging feature in SHEVs, thereby advancing the capacity of these vehicles to promote greener transportation solutions.
Modeling and enhancing inverse kinematics algorithms for real-time target tracking in inertial stabilization systems
Authors: Kriouile Abderahman , Hamida Soufiane , Moussa Abdoul Latif Abdou
Published: January 2025

This study develops a two-axis gimbal system designed to maintain a target within its field of view by compensating for motion of either the target or the platform. The focus is on inertial stabilization platforms (ISPs), where accurate, real-time tracking is essential for applications such as surveillance, navigation, and scientific observation. The research prioritizes the design and optimization of inverse kinematics algorithms to enhance system performance. A detailed analysis of mathematical models underpins the development, addressing challenges in real-time processing with advanced optimization techniques to minimize latency and maximize accuracy. The proposed algorithms achieve a mean tracking error of 0.002 m and a mean convergence time of 2.12 seconds, surpassing traditional methods in precision and efficiency. Performance is evaluated within a simulation framework using Simscape Multibody, testing the algorithms under various conditions. Validation extends to real-world scenarios to ensure robustness and practical applicability. The results demonstrate significant improvements in tracking accuracy and responsiveness, offering a reliable solution for dynamic environments. This work paves the way for more efficient gimbal systems, contributing to advancements in technologies requiring stable and precise tracking in dynamic and challenging settings.
Enhanced automated Alzheimer’s disease detection from MRI images by exploring handcrafted and transfer learning feature extraction methods
Authors: Menad Touati , Bentoumi Mohamed , Larbi Arezki , Mimi Malika , Ahmed Abdelmalik Taleb
Published: January 2025

The rising prevalence of Alzheimer’s disease (AD) poses a significant global health challenge. Early detection of AD enables appropriate and timely treatment to slow disease progression. In this paper, we propose an enhanced procedure for automated AD detection from magnetic resonance imaging (MRI) images, focusing on two primary tasks: feature extraction and classification. For feature extraction, we have investigated two categories of methods: handcrafted techniques and those based on pre-trained convolutional neural network (CNN) models. Handcrafted methods are preceded by a preprocessing step to improve the MRI image contrast, while the pre-trained CNN models were adapted by utilizing only a part of the models as feature extractors, incorporating a global average pooling (GAP) layer to flatten the feature vector and reduce its dimensionality. For classification, we employed three different algorithms as binary classifiers to detect AD from MRI images. Our results demonstrate that the support vector machine (SVM) classifier achieves a classification accuracy of 99.92% with Gabor features and 100% with ResNet101 CNN features, competing with existing methods. This study underscores the effectiveness of feature extraction using Gabor filters, as well as those based on the adapted pre-trained CNN models, for accurate AD detection from MRI images, offering significant advancements in early diagnosis.
Novel features extraction: pigment epithelial detachment detection using machine learning algorithms
Authors: Mercy Sheeba Thankappan , Saminathan Albert Antony Raj , Murugadhas Anand , Sheeba Anshy Princella Anand
Published: January 2025

The majority of the retinal diseases have visual symptoms. Any area of the retina, a delicate layer of tissue on the interior the posterior wall of the human eye, can be impacted by retinal disorders. Optical coherence tomography (OCT) is the utmost commonly used imaging procedure for diagnosing retinal disorders such as age-related macular degeneration (ARMD), diabetic retinopathy, pigment epithelial detachment (PED), macular holes, and more. In this study, we put forth a brand-new technique for accurately extracting features from OCT images to identify PED diseases. For the preprocessing step, we examined the wiener filtering method. After that, we segmented the retinal pigment epithelium (RPE) layer used to the thresholding method, extracted the features from the RPE layer, and then gave the features to machine learning (ML) classifiers like the support vector machine (SVM), logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), naive Bayes (NB), and artificial neural network (ANN). The total dataset about 200 images among 100 is normal and 100 is PED, we trained the dataset as an unbalanced and balanced group. The RF is the best outcome in comparison of other classifiers. The overall outcome of random forest is 100% accuracy.
Combined-adaptive image preprocessing method based on noise detection
Authors: Shamshanovna Razakhova Bibigul , Amangeldy Nurzada , Kassymova Akmaral , Kudubayeva Saule , Kurmetbek Bekbolat , Barlybayev Alibek , Gazizova Nazerke , Buribayeva Aigerim
Published: January 2025

The image processing method involves several critical steps, with image preprocessing being particularly significant. Segmentation and contour extraction on digital images are essential in fields ranging from image recognition to image enhancement in various recording devices, such as photo and video cameras. This research identifies and analyzes the main drawbacks of existing segmentation and contour extraction methods, focusing on object recognition. Not all filters effectively remove noise; some may clear areas of interest, affecting gesture recognition accuracy. Therefore, studying the impact of image preprocessing on gesture recognition outcomes is crucial for improving pattern recognition performance through more efficient preprocessing methods. This study seeks to find an optimal solution by detecting specific features during the preprocessing stage that directly influence gesture recognition accuracy. This research is a key component of the AP19175452 project, funded by the ministry of science and higher education. The project aims to create automated interpretation systems for Kazakh sign language, promoting inclusivity and technological innovation in communication aids. By addressing these challenges, the study contributes to the development of more robust and adaptive image preprocessing techniques for gesture recognition systems.
Classification of brain stroke based on susceptibility-weighted imaging using machine learning
Authors: Kandaya Shaarmila , Saad Norhashimah Mohd , Abdullah Abdul Rahim , Shair Ezreen Farina , Muda Ahmad Sobri , Sabri Muhammad Izzat Ahmad
Published: January 2025

Magnetic resonance imaging (MRI) is used to identify brain disorders, particularly strokes. Rapid treatment, often referred to as "time is brain," is emphasized in recent studies, stressing the significance of early intervention within six hours of stroke onset to save lives and enhance outcomes. The traditional manual diagnosis of brain strokes by neuroradiologists is both subjective and time-intensive. To tackle this challenge, this study introduces an automated method for classify brain stroke from MRI images based on pre- and post-stroke patients. The technique employs machine learning, with a focus on susceptibility weighted imaging (SWI) sequences, and involves four stages: preprocessing, segmentation, feature extraction, classification and performance evaluation. The paper proposes classification and performance evaluation to determine stroke region according to three types of categories, those are poor improvement, moderate improvement and good improvement stroke patients based on pre and post patients. Then, performance evaluation is verified using accuracy, sensitivity and specificity. Results indicate that the hybrid support vector machine and bagged tree (SVMBT) yields the best performance for stroke lesion classification, achieving the highest accuracy which is 99% and showing significant improvement for stroke patients. In conclusion, the proposed stroke classification technique demonstrates promising potential for brain stroke diagnosis, offering an efficient and automated tool to assist medical professionals in timely and accurate assessments.
A novel hybrid statistical model for camouflaged target detection
Authors: K Karthiga , A Asuntha
Published: January 2025

Camouflage is a defense mechanism employed as a concealing technique to reduce the possibility of the target being identified. In order to complete military tasks accurately, the detection of camouflaged targets in different scenarios plays a crucial role. Traditional target detection systems have the problem of detecting camouflaged targets in complex environmental conditions. There are various challenges in camouflage target detection system development, such as size, random texture, and recognition accuracy. To resolve these issues, a hybrid statistical model is proposed to identify the target from camouflage images. In this work, hybrid statistical model consists of two modules. A feature extraction module is utilized to extract the low-level features of the image based on texture and scale. A segmentation module is utilized to extract the target region and enhance the boundaries based on seed point selection and detection of edges. Further, the use of morphological processes to highlight the target region. Experiments demonstrate that the proposed hybrid statistical model performs well in camouflage detection in different environments.
IC-CGAN: Imbalanced class-conditional generative adversarial network with weighted loss function
Authors: Ravi Chaitra , Gaddadevara Matt Siddesh
Published: January 2025

This research proposes an advanced deep learning model that deals with the over-distribution of plant leaf disease classes by using an imbalanced class-conditional generative adversarial network (IC-CGAN) that is coupled with a weighted loss function. IC-CGAN model provides a solution to class imbalance through the synthesis of tomato leaf disease images and adding them to the dataset which as a consequence, improves the accuracy of disease detection. The weighted loss function essentially does a crucial job of solving the problem of imbalance in class during the training stage. Mixing of these models leads to the generation of realistic leaf disease synthetic images and balancing class distribution in the dataset, hence improving of tomato disease detection model’s accuracy. This study is another step toward the development of effective disease detection systems for agricultural purposes by addressing the concern of class imbalance with IC-CGAN through the vector-weighted loss function. The proposed IC-CGAN has a high chance of enhancing the disease detection at its early stage with a much higher level of accuracy (99.95%), precision (99.98%), recall (99.98%) and F1-score (99.98%) in tomato plant leaf disease detection.
Advanced stress detection with optimized feature selection and hybrid neural networks
Authors: Patil Sangita Ajit , Paithane Ajay Namdeorao
Published: January 2025

Stress impacts both mental and physical health, potentially leading to serious conditions like cardiovascular diseases and mental disorders. Early detection of stress is crucial for reducing these risks. This study aims to improve stress detection by analyzing physiological signals, specifically electroencephalography (EEG) and electrocardiogram (ECG). EEG is affordable, while ECG provides detailed insights into cardiovascular health. Feature selection is a major challenge in analyzing these signals. To address this, the research introduces a novel method that combines the Archimedes optimization algorithm (AoA) with the analytical hierarchical process (AHP) to enhance accuracy in both single and multimodal systems. The proposed multimodal system employs a parallel-structured convolutional neural network (CNN) with a deep architecture to extract spatial features and uses a long short-term memory (LSTM) network to capture temporal dynamics. Experimental results show significant improvements: ECG stress detection accuracy rises from 88.6% to 91.79%, EEG accuracy increases from 95% to 96.6%, and multimodal stress detection accuracy reaches 98.6%. These results highlight the effectiveness of the AoA-AHP-based feature selection technique in boosting stress detection accuracy, contributing to improved mental health management and overall well-being.
Improved convolutional neural network-based bearing fault diagnosis using multi-phase motor current signals
Authors: Huu Hai Dang , Bui Ngoc-My , Hoang Van-Phuc , Bui Quy Thang , Hoang Thi Yen
Published: January 2025

Diagnosing bearing faults of the induction motor is crucial for the maintenance of rotating electrical machines. Numerous methods have been developed and published for monitoring and classifying these faults using sensor data such as vibration, audio, and current signals. Ideally, the current phases are balanced; however, faults disrupt this symmetry, causing each phase to reveal unique diagnostic details. Consequently, studies that rely on a single phase of the current signal may not capture all fault-related characteristics. Research on motor bearing fault diagnosis using two current phases typically extracts features from each phase separately, applying machine learning to classify the faults. Currently, no approach has been proposed to extract features from both phases simultaneously. Furthermore, the proposed solutions have only been published with noise-free data. To address these challenges, this paper introduces an enhanced solution that improves the accuracy of motor bearing fault classification based on an improved convolutional neural network that processes current signals from two phases simultaneously. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches, particularly in scenarios where the sample signals are noise-adding signals. Fault classification accuracy of the proposed improved convolutional neural network (MI-CNN) about 95.12% with noise-adding signals at the signal-to- noise ratio of 20 dB.
Low complexity rate control for versatile video coding with hybrid Lagrange multiplier
Authors: Bukit Alexander Victor , Suwadi Suwadi , Wirawan Wirawan
Published: January 2025

Versatile video coding (VVC) has a notable increase in encoding efficiency over high efficiency video coding (HEVC) but bandwidth and storage are limited in real-world video applications, so rate control is crucial. Even if VVC shows a notable increase in encoding capacity over HEVC, the rate control may create fluctuations in video quality and computational complexity. This fluctuation can have a significant impact on the watching experience, particularly in low-bitrate settings. The rate control variables of the consistent resolution and the decreased resolution are proposed in order to make the rate control method hybrid to consistent resolutions and reduce computational complexity. To achieve optimal coding parameters, the frames with varying resolutions Lagrange multiplier (λ), skip coding tree unit (CTU) and quantization parameter (QP) are combined. After evaluation, the average encoding time savings (𝑇𝑠𝑎𝑣𝑖𝑛𝑔) were found to be 19.49%, with a Bjontegaard delta bit rate (BDBR) of -0.44% indicating insignificant quality loss.
Low-dose computed tomography image denoising using graph wavelet transform with optimal base
Authors: Setiawan Iwan , Hidayat Rachmat , Najar Abdul Mahatir , Jaya Agus Indra , Rosiyadi Didi
Published: January 2025

Noise in electronic components of computed tomography (CT) detectors behaves like a virus that infects visual quality of CT scans and might distort clinical diagnosis. Modern CT detector technology incorporates high-quality electronic components in conjunction with signal and image processing to ensure optimal image quality while retaining benign doses of x-rays. In this study, a new strategy in signal and image processing directions is proposed by finding the most optimal wavelet base for denoising low-dose CT scan data. The process begins by selecting the appropriate wavelet bases for CT image denoising, followed by a wavelet decomposition, thresholding, and reconstruction. Other methods, such as graph wavelet and learning-based, are used to assess the consistency of the outcomes. The wavelet base of biorthogonal 5.5 achieves the highest optimum performance for CT image denoising. Meanwhile, the Daubechies wavelet base is inconsistent and performs poorly compared to the optimum base. This research highlights the importance of wavelet properties such as orthogonality, regularity, and the number of vanishing moments in selecting an appropriate wavelet basis for noise reduction in CT images.
Maximum expansion with contiguity constraints scheduling algorithm: enhancing uplink transmission in long-term evolution vehicular environments
Authors: Ismail Shafinaz , Maidin Shajahan , Ali Darmawaty Mohd , Rahim Mohd Kamarulnizam Abdul
Published: January 2025

Uplink scheduling has become increasingly important due to increased activities like uploading videos, photos, and file sharing. Many users share or stream live videos and engage with social networks, significantly increasing uplink data traffic volume. Single carrier frequency division multiple access (SC-FDMA) is favored for its power efficiency and high data rates, benefiting user equipment (UE) battery life. However, maintaining the contiguity of resource blocks (RBs) poses challenges in uplink scheduling. The maximum expansion with contiguity constraints (MECC) algorithm has been introduced to address this challenge. MECC prioritizes contiguity, fairness, and throughput for users at the cell edge. The algorithm operates in two phases: initially allocating RBs proportionally and assigning RBs with the highest metrics while ensuring contiguity. Performance evaluation of MECC, conducted under conditions simulating vehicular movement at 30 km/h, demonstrates its superiority over other algorithms. MECC provides high fairness and throughput for both real-time (RT) and non-real-time (NRT) traffic, making it the preferred scheduler for ensuring quality of service (QoS) for both traffic types. Its focus on contiguity, fairness, throughput, and spectral efficiency establishes MECC as a valuable tool for optimizing uplink transmission in mobile networks, addressing the evolving needs of users in today's digital landscape.
A novel competitive cuckoo search algorithm for node placement in wireless sensor networks
Authors: Gunjal Monica Shivaji , Kulkarni Pramodkumar H.
Published: January 2025

Wireless sensor networks (WSNs) are essential for many different types of applications, including industrial, commercial, and agricultural. Inadequate node location, dynamic nodes, network longevity, increased packet drop rates, scaling problems, limited adaptability, and changing climatic conditions pose challenges to the WSN's efficacy. Numerous bio-inspired algorithms have been previously introduced for node placement, demonstrating a significant improvement in data aggregation performance. However, because of the low variability in the solution, weak convergence, and poor balance between exploitation and exploration, the results of WSNs are challenging to interpret. With a unique competitive Cuckoo search algorithm (CCSA), this research presents a connectivity-aware, energy- efficient, and node placement method. Using the elite population to increase the variety of the answer, the suggested competitive strategy aims to enhance convergence. It additionally employs the fuzzy C-mean clustering algorithm. The clustering optimization based on cluster head energy, density, location, Gini coefficients, and other comparable factors uses an artificial bee colony optimization (ABC) algorithm to enhance the clustering and cluster head position. A comparison between the recommended scheme and the traditional state-of-the-art reveals that the suggested system performs better regarding network throughput, residual energy, and network lifetime.
High speed space division multiplexing based integrated fiber transmission system and its impact on atmospheric conditions
Authors: Kumar Kakarla Phaneendra , Reddy T. Sreenivasulu
Published: January 2025

With several advantages over traditional wireless and fiber optic cables, free space optical communication is becoming more and more popular because of its high speed and bandwidth applications. In particular, the high transmission capacity of free space optics (FSO) communication systems makes them preferred, which enables them to handle the growing amount of internet traffic. Because FSO has no regulatory limitations, operates over large distances with high levels of security, and transmits data faster than radio frequency communication technologies are widely utilized. However, given different atmospheric conditions, the availability and capacity of FSO optical bands represent a major concern. For next-generation information transmission networks to satisfy end users high-capacity demands, the transmission technology known as space-division multiplexing (SDM) has become essential. Hence in this work, High speed space division multiplexing based integrated fiber/FSO transmission system and its impact on atmospheric conditions is presented. The SDM transmission system developed in this study is based on an integrated multimode fiber (MMF)/ FSO link with linear polarized (LP) spatial modes. This approach takes into consideration extreme climate conditions like snow and turbulence while evaluating performance. Utilizing bit error rate (BER) and received optical power (ROP) are the performance of FSO is evaluated in different weather conditions.
Buffers balancing of buffer-aided relays in 5G non-orthogonal multiple access transmission internet of things networks
Authors: Alkhwatrah Mohammad , Qasem Nidal
Published: January 2025

Buffer-aided cooperative non-orthogonal multiple access (NOMA) enhances the efficiency of utilizing the spectral by allowing more users to share the same re- sources to establish massive connectivity. This is remarkably attractive in the fifth generation (5G) and beyond systems, where a massive number of links is essential like in the internet of things (IoT). However, the capability of buffer co-operation in reducing the outage is limited due to empty and full buffers, where empty buffers can not transmit and full buffers can not receive data packets. Therefore, in this paper, we propose balancing the buffer content of the inter-connected relays, so the buffers that are more full send packets to the emptier buffers, hence all buffers are more balanced and farther from being empty or full. The simulations show that the proposed balancing technique has improved the network outage probability. The results show that the impact of the balancing is more effective as the number of relays in the network is increased. Further- more, utilizing the balancing with a lower number of relays may lead to better performance than that of more relays without balancing. In addition, giving the balancing different levels of priorities gives different levels of enhancement.
Predicting stock prices using ensemble learning techniques
Authors: Elsayed Salma , Salah Ahmad , Elhenawy Ibrahim , Abdellah Marwa
Published: January 2025

Stock price prediction has grown in importance due to its role in determining the future worth of business shares. There are several approaches for stock price prediction that can be classified into machine learning, deep learning, and ensemble learning methods. To predict stock prices, we proposed collecting a dataset for different well-known stocks, e.g., Microsoft. The utilized datasets consist of two parts; the first part contains a set of tweets for the stocks under investigation in this study which were collected from the X social media platform and the other part contains the stock prices. Sentimental features of the tweets were extracted and merged with the stock price changes. Then, we framed the problem as a regression task. we aim to analyze the performance gap between ensemble learning and other machine learning (ML) and deep learning (DL) models for predicting stock prices based on tweets. In this context, different ensemble learning models were proposed to predict the price change of each stock. Besides, several machine learning and deep learning models were used for comparison purposes. Several evaluation metrics were utilized to evaluate the performance of the proposed models. The experimental results proved that the stacking regressor model outperformed the other models.
Privacy-aware enhanced homomorphic mechanism for group data sharing
Authors: Karemallaiah Jayalakshmi , Revaiah Prabha
Published: January 2025

Cloud-based group data sharing has gained huge popularity in recent years. Accomplishing the efficacy and security of the data in a cloud-computing framework is challenging. Sharing data in a cloud environment is quite challenging and needs to be resolved. Furthermore, while exchanging data on the cloud, it is challenging to achieve both anonymity and traceability. The main aim of this research work is to make it easier for the same group to share and store anonymous data on the cloud securely and effectively. This research work presents verifiable privacy-aware enhanced homomorphic (VPEH) encryption for multiple participants; moreover, the enhanced homomorphic encryption mechanism provides end-to-end encryption and allows the secure computation of data without revealing any data in the cloud. The proposed algorithm uses homomorphic multiplication to compute the hashes product of challenges blocks that makes it more efficient Furthermore, an additional security model is incorporated to verify the shared data integrity. The VPEH mechanism is evaluated considering parameters such as tag generation, proof generation, and verification; model efficiency is proved by observing the marginal improvisation over the other existing model by varying the number of blocks and several challenge blocks.
A convex hull based geofencing system to eradicate COVID
Authors: Arora Parul , Deswal Suman
Published: January 2025

The World Health Organization (WHO) has identified coronavirus disease (COVID-19), as a global pandemic due to its quick global spread to more than 183 countries. Many countries have used movement control orders (MCO) and high alert levels to halt the spread. The primary goal of this research is to provide a geofencing architecture that is specially tailored to the MCO's standard requirement for monitoring an individual's whereabouts during a lockdown. Whenever an individual tests Corona positive, Geofencing uses technology to notify an anticipated network of people who may be affected and to enable traceability for potential patients. Computational techniques such as Delaunay triangulation (inpolygon) and triangle weight characterization (inside polygon) are applied to analyze the geographical boundary in which the patient is isolated. Convex hull, on the other hand, is a better technique than computational algorithms. It is considered the best mathematical technique because it takes the least amount of time (0.014985 sec) to detect the patient within the geofence layer and has the lowest standard deviation when compared with the other computational techniques.
Automated tomato leaf disease recognition using deep convolutional networks
Authors: Sohel Amir , Rahman Md Mizanur , Hasan Md Umaid , Islam MD Kafiul , Rukhsara Lamia , Rabeya Tapasy
Published: January 2025

Agriculture is essential for the entire global population. An advanced, robust, and empirically sound agriculture sector is essential for nourishing the global population. Various leaf diseases cause financial hardships for farmers and related businesses. Early identification of foliar diseases in crops would greatly help farmers, leading to a substantial increase in agricultural productivity. The tomato is a widely recognized and nourishing food that is easily accessible and highly favored by farmers. Early diagnosis of tomato leaf diseases is crucial to maximize tomato crop production. This study aims to utilize a deep learning approach to accurately detect and classify damaged leaves and disease patterns in tomato leaf images. By employing a substantial quantity of deep convolutional network models, we achieved a high level of precision in diagnosing the condition. The dataset used in our study work is a self-contained dataset obtained by direct observation of tomato fields in rural areas of Bangladesh. It consists of four classes: healthy, black mold, grey mold, and powdery mildew. In this study work, we utilized various image pre-processing techniques and applied VGG16, InceptionV3, DenseNet121, and AlexNet models. Our results showed that the DenseNet121 model attained the higher accuracy of 97%. This discovery guarantees accurate detection of tomato diseases in a rapid manner, ushering in a new agricultural revolution.
Towards an automated weather forecasting and classification using deep learning, fully convolutional network, and long short-term memory
Authors: Shelke Nilesh , Maurya Sudhanshu , Ithape Rupali , Shaikh Zarina , Somkunwar Rachna , Pimpalkar Amit
Published: January 2025

Historically, weather forecasting was unreliable and imprecise, relying on intuition and local knowledge. Inaccurate weather forecasts can cause severe impacts on agriculture, construction, and daily life. Existing methods struggle with rural and urban weather prediction, requiring faster and more accurate solutions. This research proposes a deep learning system using real- time images to address this challenge. This research employs a deep learning model fully convolutional network-long short-term memory (FCN-LSTM) to analyze images and predict weather conditions. In this case, the model forecasts a sunny and cloudy environment, which facilitates defining the ideal conditions for every given climatic zone in the weather classification model. The model is trained on a dataset of weather images obtained from Kaggle. The performance of the proposed model FCN-LSTM achieves an accuracy of 96.88% and a validation accuracy of 91.22%. Also, the mean squared error (MSE) is 7.11, which is significantly lower and supports efficient enhancement in weather forecasting. This significant improvement demonstrates the potential of deep learning for real-time weather forecasting. The model provides efficient weather classification, enabling informed decision-making across various sectors. This research lays the foundation for automated weather analysis using deep learning, eliminating human bias and improving accuracy.
Next-generation offloading using hybrid deep learning network for adaptive mobile edge computing
Authors: Anusha P. , Bai V. Mary Amala
Published: January 2025

Deploying mobile application tasks that require a lot of computing and are time-sensitive to distant cloud-based data centers has become a popular method of working around the limitations of mobile devices (MDs). Deep reinforcement learning (DRL) techniques for offloading in mobile edge computing (MEC) environments struggle to adapt to new situations due to low sample efficiency for each new context. To address these issues, a novel computational offloading in mobile edge computing (COOL-MEC) algorithm has been proposed that combines the benefits of attention modules and bi-directional long short-term memory. This algorithm improves server resource utilization by lowering the cost of assimilating processing latency, processing energy consumption, and task throughput of latency-sensitive tasks. The experiment's findings show that, when used as intended, the recommended COOL-MEC algorithm minimizes energy consumption. When compared to the current deep convolutional attention reinforcement learning with adaptive reward policy (DCARL-ARP) and DRL techniques, the energy consumption of the proposed COOL-MEC is decreased by 0.06% and 0.08%, respectively. The average time per channel utilized for the execution of the proposed COOL-MEC also decreased by 0.051% and 0.054% when compared with existing DCARL-ARP and DRL methods respectively.
Exploring topic modelling: a comparative analysis of traditional and transformer-based approaches with emphasis on coherence and diversity
Authors: Riaz Ayesha , Abdulkader Omar , Ikram Muhammad Jawad , Jan Sadaqat
Published: January 2025

Topic modeling (TM) is an unsupervised technique used to recognize hidden or abstract topics in large corpora, extracting meaningful patterns of words (semantics). This paper explores TM within data mining (DM), focusing on challenges and advancements in extracting insights from datasets, especially from social media platforms (SMPs). Traditional techniques like latent Dirichlet allocation (LDA), alongside newer methodologies such as bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPT), and extra long-term memory networks (XLNet) are examined. This paper highlights the limitations of LDA, prompting the adoption of embedding-based models like BERT and GPT, rooted in transformer architecture, offering enhanced context-awareness and semantic understanding. The paper emphasizes leveraging pre-trained transformer-based language models to generate document embedding, refining TM and improving accuracy. Notably, integrating BERT with XLNet summaries emerges as a promising approach. By synthesizing insights, the paper aims to inform researchers on optimizing TM techniques, potentially shifting how insights are extracted from textual data.
Enhancing data security using a multi-layer encryption system
Authors: Qasim Osama Abd , Golshannavaz Sajjad
Published: January 2025

This study highlights the interesting potential of a new multilayer cryptography scheme for reliable data protection in the field of cybersecurity. To do so, an intensive examination of a multi-layer encryption mechanism is proposed to reinforce the defenses in opposition to online threats to touchy data. The strategy is multilevel, with a superior digital dictionary serving as the foundation for the primary layer. The laborious procedures that went into making this dictionary, including rotation differences, ASCII conversion, and chaotic matrix era, upload to its encoding trouble. A modified model of the advanced encryption standard (AES) algorithm with a brand-new key generation technique, which is based on the chaos idea is furnished through layer 2. A parameter is encrypted using the Rivest-Shamir-Adleman (RSA) method, and further precautions are taken to assure the security of the encryption key. When it comes to encryption time, the first layer significantly outperforms the AES method. In addition to exhibiting instantaneous efficiency in data protection, the first layer outperformed the AES algorithm in terms of encryption time which took more than 3 seconds, and the first layer took less than 0.01 seconds, while both approaches functioned identically in terms of information decryption. In-depth talks are given to customize the suggested method's performance. The results demonstrate the effectiveness of the suggested multi-stage encryption and decryption system and demonstrate its efficacy in protecting text documents.