bluuebunny/crossref_metadata_2025 huggingface dataset:
Lightning Artist Toolkit: A Hand-Drawn Volumetric Animation Pipeline
Authors: Fox-Gieg Nick
Published: July 2024

We propose a set of methods for freely integrating live-action volumetric video with hand-drawn volumetric animation, which our research develops as the Lightning Artist Toolkit (Latk)---a complete pipeline for hand-drawn volumetric animation, as far as we know the only open-source example of its kind. Our goal with this project is to make creation in 3D as expressive and intuitive as creation in 2D, retaining the human gesture from its origins in hand-drawn animation on paper. This effort is less a computer vision challenge with an objective goal, as with for example point cloud segmentation, than it is an attempt to approximate human vision---a drawing process that records only information from a scene that was subjectively important to an individual artist. In addition to supporting animation efforts in the near term, we believe the public TiltSet dataset assembled for this project will remain usable in new and unexpected ways.
A Computation Model to Estimate Interaction Intensity through Non-Verbal Behavioral Cues: A Case Study of Intimate Couples under the Impact of Acute Alcohol Consumption
Authors: Yu Zhiwei , Crane Cory , Chen Linlin , Testa Maria , Zheng Zhi
Published: July 2024

This work introduced a novel analysis method to estimate interaction intensity, i.e., the level of positivity/negativity of interaction, for intimate couples (married and heterosexual) under the impact of alcohol, which has great influences on behavioral health. Non-verbal behaviors are critical in interpersonal interactions. However, whether computer vision-detected non-verbal behaviors can effectively estimate the interaction intensity of intimate couples is still unexplored. In this work, we proposed novel measurements and investigated their feasibility to estimate interaction intensities through machine learning regression models. Analyses were conducted based on a conflict-resolution conversation video dataset of intimate couples before and after acute alcohol consumption. Results showed the estimation error was at the lowest in the no-alcohol state but significantly increased if the model trained using no-alcohol data was applied to after-alcohol data, indicating that alcohol altered the interaction data in the feature space. While training a model using rich after-alcohol data is ideal to address the performance decrease, data collection in such a risky state is challenging in real life. Thus, we proposed a new State-Induced Domain Adaptation (SIDA) framework, which allows for improving estimation performance using only a small after-alcohol training dataset, pointing to a future direction of addressing data scarcity issues.
HEalthRecordBERT (HERBERT): Leveraging Transformers on Electronic Health Records for Chronic Kidney Disease Risk Stratification
Authors: Moore Alex , Orset Bastien , Yassaee Arrash , Irving Benjamin , Morelli Davide
Published: July 2024

Risk stratification is an essential tool in the fight against many diseases, including chronic kidney disease. Recent work has focused on applying techniques from machine learning and leveraging the information contained in a patient’s electronic health record (EHR). Irregular intervals between data entries and the large number of variables tracked in EHR datasets can make them challenging to work with. Many of the difficulties associated with these datasets can be overcome by using large language models, such as bidirectional encoder representations from transformers (BERT). Previous attempts to apply BERT to EHR for risk stratification have shown promise. In this work we propose HERBERT, a novel application of BERT to EHR data. We identify two key areas where BERT models must be modified to adapt them to EHR data, namely: the embedding layer and the pretraining task. We show how changes to these can lead to improved performance, relative to the previous state of the art. We evaluate our model by predicting the transition of chronic kidney disease patients to end stage renal disease. The strong performance of our model justifies our architectural changes and suggests that large language models could play an important role in future renal risk stratification.
Switching Time Optimization for Binary Quantum Optimal Control
Authors: Fei Xinyu , Brady Lucas , Larson Jeffrey , Leyffer Sven , Shen Siqian
Published: July 2024

Quantum optimal control is a technique for controlling the evolution of a quantum system and has been applied to a wide range of problems in quantum physics. We study a binary quantum control optimization problem, where control decisions are binary-valued and the problem is solved in diverse quantum algorithms. In this paper, we utilize classical optimization and computing techniques to develop an algorithmic framework that sequentially optimizes the number of control switches and the duration of each control interval on a continuous time horizon. Specifically, we first solve the continuous relaxation of the binary control problem based on time discretization and then use a heuristic to obtain a controller sequence with a penalty on the number of switches. Then, we formulate a switching time optimization model and apply sequential least-squares programming with accelerated time-evolution simulation to solve the model. We demonstrate that our computational framework can obtain binary controls with high-quality performance and also reduce computational time via solving a family of quantum control instances in various quantum physics applications.
Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey
Authors: Pourpanah Farhad , Etemad Ali
Published: July 2024

Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring. These systems enable individuals to increase their independence by allowing them to monitor their health from the home and by allowing more control over their well-being. In this study, we perform a comprehensive survey on this topic by reviewing a large number of literature in the area. We investigate these systems from various aspects, namely sensing technologies, communication technologies, intelligent and computing systems, and application areas. Specifically, we provide an overview of in-home health monitoring systems and identify their main components. We then present each component and discuss its role within in-home health monitoring systems. In addition, we provide an overview of the practical use of ubiquitous technologies in the home for health monitoring. Finally, we identify the main challenges and limitations based on the existing literature and provide eight recommendations for potential future research directions toward the development of in-home health monitoring systems. We conclude that despite extensive research on various components needed for the development of effective in-home health monitoring systems, the development of effective in-home health monitoring systems still requires further investigation.
A Survey on Knowledge Graph Related Research in Smart City Domain
Authors: Wang Zhu , Han Fengxia , Zhao Shengjie
Published: July 2024

Knowledge graph employs the specific graph structure to store knowledge in the form of entities, relations, attributes, and so forth, which can effectively represent correlations among data and has been applied in many fields, including search engine optimization, intelligent question answering, and recommendation systems. In this article, we mainly focus on the research and application of the domain-specific knowledge graph in the field of the smart city, which has not been fully paid attention to. Currently, the major problem faced by the smart city lies in data mining and proper application. On the one hand, data are usually stored by government management departments, which creates challenges such as high data storing overhead and inefficient data usage. On the other hand, data cannot be coordinated and collaborated between different city management systems, because data silos exist. By constructing the corresponding knowledge graph, the data of urban traffic, services, and public resources are integrated to provide help for city builders and managers to make important decisions. Therefore, we will review the related literature on the knowledge graph existing in the smart city domain to expore reasearch scopes. Specifically, we will analyze and summarize knowledge graph construction research in the field of smart cities from four perspectives, i.e., smart city ontology, urban data processing, urban knowledge graph construction, and their application. Finally, the research limitations and prospects of the urban knowledge graph are provided.
iScan: Detection of Colorectal Cancer from CT Scan Images Using Deep Learning
Authors: Ghosal Sagnik , Das Debanjan , Rai Jay Kumar , Pandaw Akanksha Singh , Verma Sakshi
Published: July 2024

Colorectal cancer, a highly lethal form of cancer, can be treated effectively if detected early. However, the current diagnosis process involves a time-consuming and manual review of CT scans to identify cancerous regions and behavior, leading to resource consumption, subjectivity, and dependency on manual assessment. We propose a 3-phase deep neural system for automated colorectal cancer detection using CT scan images to address these challenges. It includes a SegNet network to identify tumor locations, an InceptionResNet V2 network to classify tumors as benign or malignant, and an analysis of tumor area cum perimeter to predict the cancer stage. The proposed model offers a fully automated solution by combining these functionalities under a single umbrella. In real-life CT scans from 37 patients, the proposed model achieved 95.8% ROI segmentation accuracy, a dice coefficient of 0.6214, 69.75% IoU score, and 95.83% tumor classification accuracy. The unique approach using Radial Length (RL) and Circularity (C) parameters predicted the T-stage with close to 85% accuracy. Based on these outcomes, the proposed system establishes itself as a reliable and suitable alternative to traditional cancer diagnosis techniques by leveraging the power of automation, deep learning, and innovative parameter analysis.
Review—Innovations in Flexible Sensory Devices for the Visually Impaired
Authors: Panwar Mistha , Dhankhar Akshika , Rajoria Harshita , Soreng Jasmine , Batsyas Ranya , Kharangarh Poonam Rani
Published: July 2024

This paper represents a comprehensive review that explores the paradigm shift brought about by intelligent technology in addressing the challenges faced by visually impaired individuals for obstacle detection and navigation. Divided into three pivotal categories, shoe-related devices, innovative blind sticks, and wearable devices utilizing facial recognition technology, this review dissects the underlying mechanisms, technologies, challenges, and real-world applications. The investigation reveals the integration of cutting-edge technologies like ultrasonic sensors, IMUs, 3D LIDAR sensors, and GPS into shoe-related devices, promising improved independence and safety. Smart blind sticks equipped with sensors, such as ultrasonic, water, LDR, and GPS, empower users with real-time feedback, enabling them to navigate their environment confidently. Additionally, wearable devices leveraging facial and object recognition hold the potential to revolutionize social interactions and object perception. Through a synthesis of image processing techniques, sensor technologies, and detectors, this paper underscores the importance of technological advancements in enhancing the quality of life for the visually impaired community. As the field evolves, the report concludes by envisioning a future of even more innovative and impactful smart technology solutions that empower visually impaired individuals to navigate the world with increased autonomy and confidence.
Design of Terahertz Refractive Index-Based Spiral Hollow-Core Photonic Crystal Biosensor Using Enhanced Probabilistic Pyramid Neural Networks for Brain Tumor Detection
Authors: G Purushothaman , R Arulmozhiyal
Published: July 2024

Cancer diagnosis is difficult and costly due to the complexity of the brain. Photonic technology-based biosensors show potential for identifying malignant tissues, including brain tumors, but they are often costly, time-consuming, and computationally difficult. To address these challenges, we propose an enhanced probabilistic pyramid neural networks (EPPNN)-based hollow-core photonic crystal fiber (PCF) biosensor with terahertz refractive index (THzBio-ECPPN) for detection of cancerous brain tumors. The approach is divided into two stages: biosensor design and brain tumor detection. Initially, PCF geometry with suspended cladding and a spiral-shaped hollow-core in the terahertz (THz) band is proposed. The PCF biosensors’ characteristics are then calculated using the EPPNN model. The EPPNN model’s hyperparameters are modified using the circle-inspired optimization algorithm to maximize accuracy and minimize effective mode loss. The proposed biosensor is then used to identify brain tumors. Experimental evaluations utilizing MATLAB show that the suggested strategy surpasses earlier methods, with a higher sensitivity (98%). The sensor has exceptional performance characteristics, such as a high figure of merit of 1.25–1.35 RI range and sensitivity of 50000 nm RIU−1, indicating its potential for precise detection of changes in refractive index. This combination of photonic crystal structures and neural networks has enormous potential for improving cancerous tumor accuracy to 99.92%, precision to 99.23%, specificity to 99.73%,and sensitivity to 99.36% of brain tumor diagnosis.
Impact of Delayed Sequential Bilateral Cataract Surgery on Diabetic Macular Edema: A Real‐World Study in Northwestern China
Authors: Ren Yuecong , Yan Hong
Published: July 2024

Objectives. To assess the incidence and characteristics of treatment‐requiring diabetic macular edema (TR‐DME) in eyes after delayed sequential bilateral cataract surgery in patients with diabetes mellitus. Methods. This was a retrospective study involving patients with diabetes who underwent cataract surgery at Shaanxi Eye Hospital between January 2019 and December 2021. The incidence and characteristics of a first episode of TR‐DME after delayed sequential bilateral cataract surgery were observed. Results. A total of 1553 individuals (3106 eyes) were included in the study. All patients underwent bilateral cataract surgery with the prescribed interval of surgery within one month. The incidence of TR‐DME was 0.52% in the year before surgery versus 1.87% in the year after surgery (p < 0.05). The highest risk period was between 2 and 4 months after surgery. A first episode of TR‐DME was observed in 58 eyes of 35 patients after delayed sequential bilateral cataract surgery. The patients were divided into four groups according to the interval between bilateral cataract surgeries. A higher incidence of TR‐DME was observed when the interval between the surgeries was less than two weeks. Conclusions. This is the first real‐world study of the effects of cataract surgery on the development of TR‐DME. The study was performed at the largest ophthalmic center in Northwestern China. The findings demonstrate that the incidence of TR‐DME increased significantly after cataract surgery, with the highest incidence between two and four months after surgery. Patients with shorter intervals between bilateral cataract surgeries were at a high risk.
The Use of Perioperative Liposomal Bupivacaine during Spine Surgeries Does Not Produce Improved Postoperative Pain Control in Adults: A Systematic Review
Authors: Patel Tarika , Paschell Peyton , McNicholas Meagan , Arnold Paul , Lee Cheng-Ting
Published: July 2024

Recovery following spinal surgery can be challenging, particularly concerning postoperative pain management. The first‐line treatment for postoperative pain is opioid analgesia, but opioid overuse can lead to adverse effects. Liposomal bupivacaine (LB), an extended‐release local anesthetic, has shown promise as a method of enhancing postoperative pain control. This systematic review aims to provide a comprehensive evaluation of the effectiveness of LB in improving postoperative outcomes after spinal surgery. Following the PRISMA guidelines, 18 studies met inclusion criteria and were evaluated for pain, opioid consumption, length of stay (LOS), and adverse events. Six studies showed no significant difference between treatment groups in all three major outcomes. Only two studies showed statistically improved pain control, opioid use, and LOS with LB. Similarly, another study also demonstrated improved pain control in the LB group; however, there was no significant change in opioid use or LOS. Two other studies also showed significantly improved postoperative pain control after the administration of LB, but the effect lasted only 24 hours after surgery. Three other reports saw a quicker transition off intravenous opioids with LB use, but no difference in total opioid use. Three of the four studies that focused on pediatric populations found improvement in all major outcomes with LB use; the fourth saw no significant difference between treatment groups. Currently, evidence is poor to suggest that LB significantly improves postoperative pain, opioid use, or LOS in adults. However, several patterns were identified that may influence study results: reports performed at military bases have different, more stringent protocols for recovery, and differences in spinal surgeries, some more invasive than others, can lead to different pain levels. The results of this systematic review demonstrate that the addition of LB does not provide improved postoperative pain control after spinal surgery in adults as demonstrated by both randomized controlled trials (RCTs) and the majority of retrospective cohort studies (RCSs). However, the use of LB for spine surgeries may be justified for the pediatric population, but further research is warranted given the limited reports available.
Zoonotic Parasites and Their Association With Human Activities in Northern Tanzania: An Integrated Ecosystem Approach for One Health
Authors: Ndossi Barakaeli Abdieli , Mjingo Eblate Ernest , Park Hansol , Lee Dongmin , Bia Mohammed Mebarek , Yang Heejae , Seo Sungbo , Eom Keeseon S.
Published: July 2024

The community’s awareness of the prevalence and impact of zoonotic diseases has been significantly underestimated, leading to insufficient implementation of control measures. This study was carried out in Northern Tanzania between 2019 and 2023 to investigate zoonotic parasites and the risks associated with human activities that contribute to zoonotic diseases. Cross‐sectional surveys were conducted in 12 villages, including nine in Loliondo Division and three in Babati District. Focus Group Discussions and Key Informant Interviews were conducted to assess the community’s knowledge and practices regarding the risks associated with zoonotic diseases in the surveyed areas. A total of 255 samples were collected from various sources, including latrines, households, livestock enclosures, domestic dogs, and chickens. Out of these samples, 152 tested positive for identifiable parasite eggs and oocysts. These parasites included hookworms (21.7%), Trichuris sp. (14.5%), Strongyloides sp. (13.8%), Eimeria sp. (19.7%), Taeniids (5.9%), Hymenolepis sp. (3.3%), Spirometra sp. (2.6%), and Dipylidium sp. (0.7%). Taeniids and Spirometra species were predominantly found in villages near protected areas such as Arash Sokoni, Oloipiri, Sukenya, Wasso, Orkuyiene, Haytemba, and Loliondo. Hookworms were most commonly detected in Arash Sokoni, Loliondo, Isuguro, and Hyatemba, while Strongyloides sp. was prevalent in Wasso, Sukenya, and Olobo villages. The quantitative analysis reveals significant associations between hygiene practices, proximity to livestock enclosures, ecological factors, and human–animal interaction, highlighting their pivotal roles in determining soil‐transmitted helminth (STH) prevalence across different villages. This study reveals that there was a generally low level of awareness regarding zoonotic diseases and STHs. The detection of STH samples, combined with the limited understanding of zoonotic diseases, emphasizes the importance of taking proactive measures to reduce transmission risks. Prioritizing education and promoting awareness along with implementing comprehensive strategies are essential steps to effectively tackle the problems linked to STH infections and substantially lessen the public health burden caused by zoonotic diseases.
Dioctylsulfosuccinate Functionalized NiAl‐Layered Double Hydroxide for Sensitive Fenuron Electroanalysis Using a Carbon Paste Electrode
Authors: Kameni Wendji Aude Peggy , Tcheumi Herve Leclerc , Kenfack Tonle Ignas , Ngameni Emmanuel
Published: July 2024

Environmental pollution resulting from the use of pesticides such as fenuron poses significant health risks due to the carcinogenic and teratogenic properties of these compounds. There is an urgent need to develop rapid and cost‐effective detection methods for quantifying fenuron. In this study, an inorganic‐organic composite material was obtained by intercalating sodium dioctylsulfosuccinate (DSS) within the interlayer space of a nickel‐aluminum‐layered double hydroxide (NiAl‐LDH). The pristine and modified LDHs (NiAl‐LDH) were characterized using Fourier transform infrared, X‐ray diffraction, and thermogravimetric analysis, confirming the successful intercalation of DSS in the mineral structure. The modified LDH was used to elaborate a sensor for detecting fenuron herbicide via differential pulse voltammetry (DPV) employing a carbon paste electrode (CPE). The electrochemical procedure for fenuron analysis consisted of immersing the working electrode in an electrolytic solution containing the appropriate amount of fenuron, followed by voltammetry detection without any preconcentration step. Compared to CPE modified by pristine LDH, the peak current obtained on the organo‐LDH‐modified CPE was twice as high. The increase in the fenuron signal was attributed to the high organophilic feature of this composite material induced by DSS modification. To optimize the sensitivity of the organo‐LDH modified electrode, the effects of several experimental parameters such as pH of the medium and proportion of the modifier in the paste on the stripping response were examined. Linear calibration curves were obtained for the fenuron concentrations ranging from 0.5 × 10−6 to 1 × 10−6 mol.L−1 and 1 × 10−6 to 5 × 10−6 mol.L−1. The limit of detection (LOD) calculated on the basis of a signal‐to‐noise ratio of 3 was found to be 1.8 × 10−8 mol.L−1 for the low concentration range with a limit of quantification (LOQ) which was 6 × 10−8 mol.L−1. Furthermore, the interference effect of several inorganic ions and other pesticides potentially affecting fenuron stripping was explored, and the method’s applicability was confirmed by determining fenuron levels in a river sample taken from down‐town Yaoundé.
BRCA Status Dictates Wnt Responsiveness in Epithelial Ovarian Cancer
Authors: Chehade Hussein , Gogoi Radhika , Adzibolosu Nicholas K. , Galoforo Sandra , Fehmi Rouba-Ali , Kheil Mira , Fox Alexandra , Kim Seongho , Rattan Ramandeep , Hou Zhanjun , Morris Robert T. , Matherly Larry H. , Mor Gil , Alvero Ayesha B.
Published: July 2024

The association of BRCA1 and BRCA2 mutations with increased risk for developing epithelial ovarian cancer is well established. However, the observed clinical differences, particularly the improved therapy response and patient survival in BRCA2-mutant patients, are unexplained. Our objective is to identify molecular pathways that are differentially regulated upon the loss of BRCA1 and BRCA2 functions in ovarian cancer. Transcriptomic and pathway analyses comparing BRCA1-mutant, BRCA2-mutant, and homologous recombination wild-type ovarian tumors showed differential regulation of the Wnt/β-catenin pathway. Using Wnt3A-treated BRCA1/2 wild-type, BRCA1-null, and BRCA2-null mouse ovarian cancer cells, we observed preferential activation of canonical Wnt/β-catenin signaling in BRCA1/2 wild-type ovarian cancer cells, whereas noncanonical Wnt/β-catenin signaling was preferentially activated in the BRCA1-null ovarian cancer cells. Interestingly, BRCA2-null mouse ovarian cancer cells demonstrated a unique response to Wnt3A with the preferential upregulation of the Wnt signaling inhibitor Axin2. In addition, decreased phosphorylation and enhanced stability of β-catenin were observed in BRCA2-null mouse ovarian cancer cells, which correlated with increased inhibitory phosphorylation on GSK3β. These findings open venues for the translation of these molecular observations into modalities that can impact patient survival.

Significance:

We show that BRCA1 and BRCA2 mutation statuses differentially impact the regulation of the Wnt/β-catenin signaling pathway, a major effector of cancer initiation and progression. Our findings provide a better understanding of molecular mechanisms that promote the known differential clinical profile in these patient populations.
Performance evaluation of software defined networking into vanets system
Authors: Taher Younus Hasan , Alsaadi Israa , Saad Mohammed Ayad , Ali Adnan Hussein , Essa Mohammed , Rashid Ahmed Hashim
Published: July 2024

Vehicular ad hoc networks (VANETs) is an important topic nowadays. A lot of research deal and attracts consideration owing to potential for increasing traffic and travel efficiency, improving road safety for vehicles, providing convenience and comfort to both drivers and passengers. The need for a packet delivery ratio (PDR) and low delivery delay time in communication are the key elements in modern life especially when traveling in vehicles. To satisfy these demands; researchs in VANET systems aims to develop some new technologies. One of these technologies is using software-defined- network (SDN) to enhance communication between vehicles on the road. Because of this, project evaluates using SDN protocol with two most viable VANET protocols which are ad hoc on demand distance vector (AODV) and optimized link state routing (OLSR) in LTE communication. Two performance metrics are used to evaluate the performances, the PDR and the delivery delay time. The simulation is performed in the varying density network and varying speed vehicles. The simulation results show that SDN displays better performance than AODV and OLSR in both PDR and delivery delay time. SDN uses global views of SDN controller to determine the shortest route with the highest vehicle density. Additionally, it solves the local maximum issue and adds dense connectivity.
Progress in self-powered medical devices for breathing recording
Authors: Abu Owida Hamza , Turab Nidal , Al-Nabulsi Jamal I. , Al-Ayyad Muhammad
Published: July 2024

Wearable and implantable medical technologies are increasingly being used for the diagnosis, treatment, and prevention of illnesses and other health concerns. One's respiration can be monitored using any number of different biosensors and tracking devices. Self-powered sensors, for example, have a reduced total cost, are easy to prepare, have a high degree of design-ability, and are available in a number of different forms when compared to other types of sensors. The mechanical energy stored in the respiratory system could be converted into electrical energy by using airflow to operate self-powered sensors. Self-recharging sensors and systems are now in development to make home health monitoring and diagnosis more practical. There has not been a lot of study devoted to the models of respiratory sickness or the output signals that connect with them. Thus, investigating the character of their bond is not only difficult but also crucial. This article examined the theory behind self-powered breathing sensors and systems, as well as their output characteristics, detection indices, and other cutting-edge developments. To help communicate knowledge to other academics working in this field and interested in this topic, we also explored the challenges and potential benefits of autonomous sensors.
A study on social media addiction analysis on the people of Bangladesh using machine learning algorithms
Authors: Mim Minjun Nahar , Firoz Mehedi , Islam Mohammad Monirul , Hasan Mahady , Habib Md. Tarek
Published: July 2024

Social media has become a fundamental element of contemporary life, providing countless benefits but also posing substantial concerns. While technology improves connectedness and information exchange, excessive use raises issues about social and personal well-being. The emergence of social media addiction emphasizes its influence on everyday routines and mental health, with many people favoring online activities above vital tasks, resulting in real repercussions. Twitter, Facebook, and Snapchat have a significant impact on emotional well-being, adding to global rates of despair and anxiety. To measure the frequency of social media reliance, we studied data from 1,417 individuals using machine learning methods such as decision tree (DT) classifier, random forest (RF) classifier, support vector classifier (SVC), k-nearest neighbors (K-NN), and multinomial naive Bayes (NB). Understanding the behavioral patterns that drive addiction allows us to create tailored therapies to encourage healthy digital behaviors. This study highlights the critical necessity to address social media addiction as a complicated societal issue. Our major goal is to determine the amount of people who are addicted to social media.
Analysis of distributed smart grid system on the national grids
Authors: Mythreyee M. , Anandan Nalini
Published: July 2024

In the power industry, advanced techniques have furthered the development of the smart grid's power system and management. The world's third-largest country is India, which has a producer and consumer of electricity, is struggling with different power-related problems, as well as distribution losses, transmission, environmental concerns, and electricity theft. The energy sector is investigating innovative technologies to enhance grid efficiency, security, and sustainability to address power-related issues. Recently, smart grid technology has ascribed significance to the energy scenario; the term "smart grid" relates to electric electricity. The study aims to thoroughly evaluate how smart grid technologies might improve the reliability and efficiency of India's electrical system. This article examines the impact of smart grid technologies on national grids and makes some proposals to authorities for switching their traditional grid system to a smart grid system. The results indicate the yearly wind profile, comparative analysis of energy consumption, and cost analysis of the system. Smart grid integration is strengthened by the useful insights provided by the annual wind profile study, which reveals the region's renewable energy potential. Analysis of costs and energy consumption patterns show that switching to a smart grid system is financially feasible in the long term, and studies of impacts on utilization of resources show that it is beneficial.
Differential evolution with adaptive mutation and crossover strategies for nonlinear regression problems
Authors: Wongsa Watchara , Puphasuk Pikul , Wetweerapong Jeerayut
Published: July 2024

This paper presents the differential evolution algorithm with adaptive mutation and crossover strategies (DEAMC) for solving nonlinear regression problems. The DEAMC algorithm adaptively uses two mutation strategies and two ranges of crossover rate. We evaluate its performance on the National Institute of Standards and Technology (NIST) nonlinear-regression benchmark containing many models of varying levels of difficulty and compare it with classic differential evolution (DE), enhanced differential evolution algorithm with an adaptation of switching crossover strategy (DEASC), and controlled random search methods (CRS4HC, CRS4HCe). We also apply the proposed method to solve parameter identification applications and compare it with enhanced chaotic grasshopper optimization algorithms (ECGOA), self-adaptive differential evolution with dynamic mutation and pheromone strategy (SDE-FMP), and JAYA and its variant methods. The experimental results show that DEAMC is more reliable and gives more accurate results than the compared methods.
Enhancing radar applications: FPGA-driven phase estimation with floating point arithmetic
Authors: Sivaprasad Ponduri , Venkataraman Anandi , Murty P Satyanarayana
Published: July 2024

This article introduces a paradigm shift in radar technology with field programmable gate array (FPGA)-driven Phase estimation using floating point arithmetic (FPA). Leveraging FPGA’s parallel processing and the precision of FPA, this work promises enhanced accuracy and efficiency. The proposed system’s key performance metrics include the following: number of slices: 20,941, number of look-up tables (LUTs): 22,371, number of digital signal processing (DSP) blocks: 2, delay: 112.9 ns, and power consumption: 7.2 mw. A comparative analysis showcases advantages in area utilization, LUT, and DSP blocks despite a trade-off with delay. The presented methodology and results demonstrate the feasibility of real-time phase estimation at GHz rates, positioning this approach as transformative for next-gen radar systems.
The new machine learning feature selection method used in fertilizer recommendation
Authors: D N Varshitha , Choudhary Savita
Published: July 2024

Fertilizer recommendation is the crucial factor to be considered in automation of agricultural predictions. Fertilizer fill the necessary portion of any farming region. There are some micronutrients and macro nutrients which need to be given to crops for proper growth. If fertilization is not done to an optimum level, it may badly harm the soil quality and crop health ,so optimum fertilization is important. In this paper we discuss fertilizer and nutrient recommender, where we have used a new feature selection methodology. We have shown the difference between two implementation cases considering presence and absence of feature ranking and selection. Feature ranking and selection has clearly increased the efficiency of the fertilizer nutrient recommender in our work from 85% to 98%. Feature selection raking has been introduced with random forest approach.
Braille letter recognition in deep convolutional neural network with horizontal and vertical projection
Authors: Rahmat Romi Fadillah , Purnamawati Sarah , Mardianto Willy , Faza Sharfina , Sulaiman Riza , Nadi Farhad , Lubis Arif Ridho
Published: July 2024

Brail is a written mode of communication utilized by individuals with visual impairments to engage in interpersonal exchanges. The braille writing system consists of patterns printed on specialized paper that feature embossed dots. Braille documents enable the visually impaired to acquire knowledge and information exclusively through the application of their sense of contact. Comprehending braille is not a simple undertaking, particularly for the general populace. Because braille is not a required subject in Indonesian education, the majority of the population lacks proficiency in the language. This may therefore result in a minor communication barrier between visually impaired individuals and non-impaired individuals. In order to address this challenge, the present study employs digital image processing via the deep convolutional neural network (DCNN) technique to facilitate comprehension of braille document contents by non-braille speakers. This study employs a deep learning technique that is highly accurate, effective at image processing, and capable of recognizing complex patterns. This study employed the following image processing methods: grayscaling, filtering, contrast enhancement, thresholding, morphological operation, and resizing. Following testing in this investigation, it was determined that the proposed method accurately identifies embossed braille images with a precision of 99.63%.
An image analysis technique for wheat head count detection using machine learning
Authors: Kalluri Ramadevi , Selvaraj Prabha
Published: July 2024

Deep learning (DL) techniques have significantly enhanced the potential for wheat head detection in recent times. The different development phases of canopy, genotype, wheat heads, and wheat head orientation provide considerable obstacles. The overlapping density of wheat heads and wind- induced picture blurring complicate wheat head recognition. This study describes an effective wheat head detection and counting method. Due to its high throughput in agricultural field analysis, remote sensing phenotyping has grown in popularity. Applying DL methods for image processing and other technological advancements has increased the scope for the quantitative evaluation of various crop traits. The ability to detect and characterize wheat heads in the industry is an important part of the wheat breeding process for selecting high-yielding cultivars. The proposed method uses the Mask region-based convolutional neural network (RCNN) framework to detect and classify the wheat ears. The complete detection task is done in three steps: region proposal generation, region of interest alignment, and mask generation. The global wheat head detection (GWHD) dataset is used for the experimental analysis of the dataset. The proposed method achieved an accuracy of 95.11% on the GWHD dataset, demonstrating its effectiveness in wheat head detection and classification tasks.
Prediction of mental illness using ensemble model and grid search hyperparameter optimization
Authors: Kudlapura Shivaiah Srinath , Krishnappa Kiran , Kumar Boraiah Naveen , Deepa Shenoy Punjalkatte , Kuppanna Rajuk Venugopal
Published: July 2024

The early prediction of mental illnesses reduces the severity of the disease. The symptoms like poor concentration, unstable energy of the body, pressure, and loss of interest cause depression. A large-scale group decision making (LSGDM) method is proposed in this paper along with the ensemble classifier model by combining convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) for effective classification of depression, anxiety and stress (DAS) levels. The data is collected from the depression anxiety stress scale-42 (DASS-42) dataset for efficient classification and predictions of mental health problems. The min-max normalization is used to pre-process the data, and the feature selection is done for extracting informative features. The extracted features are provided as input to the ensemble classifier. The proposed LSGDM model maximizes the classification accuracy with the help of grid search CV hyperparameter tuning, and results in an accuracy of 98.88%, precision of 98.21%, recall of 99.62%, F1-Score of 98.90%, and MCC of 99.41%. The proposed LSGDM method gives superior results when compared to the existing machine learning (ML) based ensemble model, a principal component analysis along with modified fast correlation based filtering (PCA-mFCBF), and LSTM based RNN (LSTM- RNN).
Chaotic ant colony algorithm to control congestion and enhance opportunistic routing in multimedia network
Authors: Ranganathan Chitra Sabapathy , Sampathrajan Rajeshkumar
Published: July 2024

The creation of wireless multimedia networks imposed wireless devices that can retrieve multimedia material such as video and audio streams, still photos, and scalar sensor data from the environment is made possible by the availability of low-cost devices. This approach considers the issues of routing packets across a multi-hop network consisting of several traffic sources and links when ensuring bounded delay. The exits of an obstacle create several geographic routing issues, for example, congestion and delay. This article, chaotic ant colony algorithm (CACA) to control congestion and enhance opportunistic routing (CAOR) in multimedia network, is proposed to solve these issues. This mechanism uses the CACA algorithm to detect the obstacle and transmit the data packets on the obstacle edges optimal nodes. Moreover, an opportunistic routing (OR) selects the best forwarder by the forward aware factor (FAF) from the forwarder list (FL). The FAF measures node energy, node received signal strength indication (RSSI), available bandwidth (AB), and packet transmission rate for choosing the best forwarder. Experimental outcomes demonstrate that established delay, energy utilization, and throughput performances are greater than the conventional mechanism.
Automatic prediction of learning styles: a comprehensive analysis of classification models
Authors: Lestari Uning , Salam Sazilah , Choo Yun-Huoy , Alomoush Ashraf , Al Qallab Kholoud
Published: July 2024

Learning styles are a topic of interest in educational research about how individuals acquire and process information in offline or online learning. Identification of learning styles in the online learning environment is challenging. The existing approaches for the identification of learning styles are limited. This study aims to review the many learning styles characterized by various classification approaches toward the automatic prediction of learning styles from learning management system (LMS) datasets. A systematic literature review (SLR) was conducted to select and analyze the most pertinent and significant papers for automatically predicting learning styles. Fifty-two research papers were published between 2015-2023. This research divides analysis into five categories: the classification of learning style models, the collection of the collected dataset, learning styles based on the curriculum, research objectives related to learning styles, and the comprehensive analysis of learning styles. This study found that learning style research encompasses diverse theories, models, and algorithms to understand individual learning preferences. Statistical analysis, explicit data collection, and the Felder-Silverman model are prevalent in research, highlighting the significance of algorithm improvement for optimizing learning processes, particularly in computer science. The categorization and understanding of various methods offer valuable insights for enhancing learning experiences in the future.
System dynamics modeling for predicting the impact of tutoring on student retention in the school of engineering
Authors: Andrade Arenas Laberiano , Giraldo Retuerto Margarita , Yactayo-Arias Cesar
Published: July 2024

Student retention is a persistent problem in many educational institutions, and we seek to address this issue through the implementation of tutoring programs. To achieve this objective, system dynamics (SD) modeling is proposed as a method. This analytical tool allows simulating and predicting the behavior of a complex system over time, considering the interactions between its components. The main objective of this research is to perform SD modeling to improve student retention through tutoring. It seeks to design more effective and personalized tutoring programs, adapted to the specific needs and challenges of the institution's students. The results obtained show that, in the period between 2022 and 2026, research degrees will be encouraged, reaching 50% participation. This increase is considered a positive indicator that encourages universities to become research protagonists. In conclusion, SD modeling makes it possible to forecast and strategically plan the expected results in terms of student retention. This method provides tools to more effectively address the problem of retention, ensuring the academic success of students and promoting the participation of universities in research.
Glaucoma detection in retinal fundus images using residual network architecture
Authors: Islami Fajrul , Sumijan Sumijan , Defit Sarjon
Published: July 2024

Glaucoma is a significant eye disease that can lead to irreversible vision loss if not detected and treated early. This research focuses on developing an automated glaucoma detection system using a combination of a convolutional neural network (CNN) with the residual network 18 (ResNet18) architecture, locality sensitive hashing (LSH), and Hamming distance calculation. The CNN model is trained to extract meaningful features from retinal images, while LSH enables efficient indexing and retrieval of similar images. Hamming distance calculations are utilized to measure the dissimilarity between binary codes obtained from LSH. A dataset of 506 retinal images, consisting of 117 glaucoma images, 19 glaucoma suspect images, and 370 healthy images. The proposed glaucoma detection system achieved an average accuracy of 99.96%, sensitivity of 99.97%, and specificity of 99.94% during training, and 82.37% accuracy, 86.78% sensitivity, and 73.55% specificity during testing. Comparative analysis demonstrated its superiority over traditional methods. Further research should focus on larger datasets and explore multi-class classification for different glaucoma stages. The proposed system has potential for early glaucoma detection, facilitating timely intervention, and preventing vision loss.
Novel entropy-based style transfer of the object in the content image using deep learning
Authors: Raghatwan Jyoti Sudhakar , Arora Sandhya
Published: July 2024

Recently neural style transfer (NST) has drawn a lot of interest of researchers, with notable advancements in color representation, texture, speed, and image quality. While previous studies focused on transferring artistic style across entire content images, a new approach proposes to transfer style specifically to objects within the content image based on the style image and maintain photorealism. Recent techniques have produced intriguing creative effects, but often only work with artificial effects, leaving real flaws visible in photographs used as references for styles. The suggested approach employs a two-dimensional wavelet transform (WT) to achieve style transfer by adjusting image structure with high-pass and low pass filters (LPF). Preserving the information content and numerical attributes of VGGNet19 through WT-based style transfer using the db5 WT at level 5, we can achieve a peak signal-to-noise ratio (PSNR) value of up to 96.76725. The qualitative result of the proposed methodology is compared with other existing algorithm. Also, the time complexity of the proposed methodology on different hardware platforms has been calculated and presented in the paper. The proposed methodology able to maintains appealing and precise quality of resultant image.
Deep learning based photovoltaic generation on time series load forecasting
Authors: Loganathan Umasankar , Nagarajan Geetha , Gopinath Srimathy , Chandrasekar Vignesh
Published: July 2024

In recent years, solar irradiance forecasting has become essential to managing, developing, and effectively integrating photovoltaic (PV) systems properly into the smart grid. The foundation of a conventional variational autoencoder (VAE) is an entirely coupled layer that includes both decoder and encoder components. In this study, a novel deep attention-driven model for forecasting named bidirectional long short-term memory (BiLSTM) which is combined with the VAE model is introduced as an enhanced version of the VAE. BiLSTM is integrated at the encoder side of VAE to effectively extract and learn temporal dependencies that are embedded in the panel irradiance data. Additionally, a self-attention mechanism (SAM) is added to bilateral variational autoencoder (BiVAE) which is known as BiVAE-SAM that highlights the important characteristics. The proposed BiVAE-SAM permits the VAE’s capacity to design the temporal dependency. The examined models are assessed using sun irradiance measurements from New York City, Turkey, Canopy, Los Angeles, California, and Florida. The outcomes exhibit that the proposed BiVAE-SAM model performs better mean absolute percentage error (MAPE) with values of 1.7935, 0.7828, 1.3491 and 2.8346 respectively for California, Los Angeles, New York City, and Florida, over existing stacked denoising auto-encoders (SDA) model at projecting solar irradiance.