bluuebunny/crossref_metadata_2025 huggingface dataset:
Advanced Care Planning in Refugee Patients
Authors: McKeith Amanda , Aldulaimi Sommer , Rivers Patrick , Weiss Barry
Published: October 2024

Advance care planning (ACP) for end-of-life care is an important part of medical care, but fewer than half of older adults in the US have completed ACP documents. Among some population groups, notably African Americans and American Indian/Alaska Natives, rates are even lower. However, there are no published data on the rate of ACP among refugees resettled in the US. We interviewed 36 refugee patients from 15 different countries of origin who were receiving care in a primary care clinic. They ranged in age from 18–77 years, with approximately equal numbers of females and males. We asked them (a) if they knew what ACP documents were and if they did, (b) if they had prepared ACP documents, which we defined as living wills, medical durable power of attorney, or written advanced directive documents. Only 6 (16.6%) of the 36 patients had heard of ACP and only 4 (11.1%) had prepared ACP documents. If they did not have ACP documents, after explaining what they are we asked if they would be interested in discussing ACP with their physician, and 27 (75%) of the 36 said they would be interested in doing so. Our results suggest that the percentage of refugees who have ACPs in place is very low. With the increasing number of refugees entering the US each year, more and more clinicians will be seeing refugees in their practice. They should consider including discussions about ACP in their care of these patients.
A finite element biomechanical study of anterior transpedicular root screw plate fixation
Authors: Xu Xiaoping , Shang Wenzhao , Hou Zhipeng , Zhao Liujun
Published: October 2024

Purpose
To compare the biomechanical properties of anterior transpedicular root screw (ATPRS), anterior transpedicular screw (ATPS) and anterior cervical locked-plate (ACLP) in the lower cervical spine by finite element method.
Methods
We collect CT data of the cervical spine from a healthy 34-year-old adult male volunteer. Use Mimics 10.01 software to build a nonlinear complete model of the lower cervical spine. The model was subjected to 75N axial force and 1.0 N·M to induce various movements. The range of motion (ROM) and stress distribution of each model under different working conditions were compared.
Results
Compared with the intact model, the ROM in the ACLP, ATPS and ATPRS groups decreased to 0.65, 0.58 and 0.62 during flexion and extension. In terms of titanium mesh graft stress, the ATPS and ATPRS groups had the largest load during extension and the smallest load during flexion. In terms of bone-screw interface stress, the peak stress around screw C7 was higher than that around screw C4 during extension in ACLP, ATPS and ATPRS groups, respectively.
Conclusion
Biomechanical characteristics of anterior transpedicular root screw system are favorable.
Frailty mediates the association between activities of daily living disability and depressive symptoms: a prospective cohort study
Authors: Zhang Dongyi , Wei Jinying , Xin Xiuqin , Liu Yan , Zhu Ying , Li Shufei , Chen Hongyue , Meng Guangping
Published: October 2024

Background
Globally, depression is a serious public health problem and it has a high incidence and recurrence rate. The underlying mechanism between the activities of daily living (ADL) disability and depression was not well understood. Our study aimed to explore the mediating role of frailty on the longitudinal association between ADL disability and depressive symptoms.
Methods
This study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018. A total of 2,245 participants aged ≥45 years old were included. Linear regression, cox regression, and mediation analysis were used to explore the association of ADL disability with development of depressive symptoms and the potential mediating role of frailty.
Results
The results showed that both scores on ADL and frailty were positively associated with the risk of depressive symptoms (P<0.05). No interaction between ADL and covariates on depressive symptoms was found for all subgroups. The association between ADL disability and depressive symptom scores was partially mediated by frailty (mediated proportion = 16.05%) in the Chinese middle-aged and elderly population.
Conclusion
There were positive associations between both scores on ADL and frailty and new-onset depressive symptoms. Individuals with high scores on ADL disability had a higher risk of depressive symptoms, and frailty partially mediated this association.
Fault diagnosis of rolling bearing based on acousto-vibration signal fusion
Authors: Fu Qiang , yi xueliang , lai yanchen , chen hong , ou quankai
Published: October 2024

Typically, accelerometers need to be installed in multiple directions simultaneously to enhance the accuracy of bea-ring fault diagnosis.However, due to certain environmental constraints, it is sometimes Impractical to install accelerometers in multiple directions simultaneously. In contrast, acoustic sensors can overcome the limitations of contact-based measurements but are more susceptible to interference from environmental noise.To address this issue, a novel method for fault diagnosis of rolling bearings that integrates both acoustic and vibration signals is proposed. First, a 2D convolutional fusion layer is employed to process the two types of signals, achieving an initial fusion of the different signals. Secondly, to effectively extract sound-vibration fusion features, a multi-scale CNN-GRU module is introduced to enhance the method's ability to capture features at different scales. Finally, a model pre-training-based transfer learning strategy is used, achieving high performance in experi-ments with an average accuracy exceeding 90%.
Effect of elevation on soil quality under bamboo (Bambusa nutans subsp. cupulata) stands outside forest area in Eastern Nepal
Authors: Ayer Santosh , Poudel Sandip , Adhikari Kishor , Shapkota Jun , Bhatta Kishor Prasad , Maraseni Tek , Maharjan Menuka
Published: October 2024

Globally, bamboo covers 37 million hectares (about 1% of the world’s forests), with over 50% growing outside official forests on degraded, marginal, and agricultural lands. However, little research has explored the relationship between soil properties and bamboo in these non-forest areas, as most studies focus on bamboo forest. Using stratified random sampling, this study assessed soil quality under bamboo stands outside forests in the Udayapur district of Nepal across three elevation zones: lower (0-400 m), middle (400–800 m), and higher (800–1200 m). A total of thirty 100 m² circular plots (10 in each zone) were sampled at two soil depths; the surface layer (0–15 cm) and the subsurface layer (15–30 cm) to assess soil quality using different soil quality indicators. At the 0–15 cm depth, soil organic carbon, total nitrogen, and available potassium were significantly higher at middle elevation (p < 0.05). Available phosphorus and soil pH were significantly higher at middle elevation, while available potassium was significantly higher at higher elevation at 15–30 cm depth (p < 0.05). SQI was higher at the 0-15cm depth (0.53) compared to the 0–30 cm depth (0.49), though this difference was not statistically significant (p > 0.05). Elevation had a significant effect (p < 0.05) on SQI only at the 0–15 cm depth. This study observed significantly higher SQI at middle elevation (0.57) whereas lower SQI at lower elevation (0.48). Further studies with larger samples and broader geographic coverage, incorporating additional soil indicators and environmental variables, could more effectively inform sustainable land management and local livelihood improvements in bamboo stands outside forest areas.
The Molecular Mechanism of Quercetin Improving Cerebral Ischemia Injury by Regulating NLRP3 Inflammasome Mediated Pyroptosis Axis
Authors: Yuan Huiling , Shen Da , Wang Yue , Li Lin , Sang Tingting , Chu Lisheng , Ge Lijun
Published: October 2024

Background
In the pathological process of cerebral ischemia, the neuroinflammation triggered by NLRP3 inflammasome-mediated neuronal pyroptosis is considered a crucial factor contributing to brain damage. Although, previous research has revealed the anti-inflammatory and neuroprotective effects of quercetin (Que), its precise mechanisms in intervening and alleviating neuroinflammation and the pyroptosis axis triggered by cerebral ischemia remain to be fully elucidated.
Objective
To investigate the role and mechanism of Que in alleviating cerebral ischemia injury by modulating the NLRP3 inflammasome-mediated pyroptosis axis.
Methods
The oxygen-glucose deprivation and reperfusion (OGD/R) model with BV2 microglial cells and the photothrombosis (PT) model with C57/BL male mice were utilized. The study explored the neuroprotective effect of Que against cerebral ischemia injury through the regulation of the pyroptosis axis via the NLRP3 inflammasome, assessed through experiments including reactive oxygen species measurement, cell pyroptosis rate detection, behavioral tests, HE staining, Nissl staining, and examination of pyroptosis-related proteins and inflammatory factors.
Results
Que alleviated cell damage and pyroptosis induced by OGD/R, reduced intracellular reactive oxygen species levels, improved behavioral impairment in mice after modeling, decreased brain injury area, maintained neuronal morphology, and lowered the expression of pyroptosis axis-related proteins such as NLRP3, Caspase-1, ASC, GSDMD, as well as inflammatory factors IL-18, IL-1β, TNF-α.
Conclusion
Que alleviated cerebral ischemia injury by regulating the pyroptosis axis through inhibiting the activation of NLRP3 inflammasome. This provides robust experimental evidence for the anti-neuroinflammatory effects of Que and the development of neuroprotective drugs.
Versammeln, besprechen, beschließen
Authors: Kypta Ulla
Published: October 2024

Different forms of diets, councils and assemblies shaped the political landscape of late medieval and early modern Europe, and especially the Holy Roman Empire. Princes and clerics, the representatives of towns and of monasteries came together in various constellations to discuss problems, adopt decisions and perform rituals. It has been repeatedly suggested that it might be fruitful to compare these different forms of assemblies. The actual attempts to do so, however, often remain unsatisfactory even to their own authors. What is still required is a systematic comparison in the hope of creating a typology of assemblies. At a summer school held in Lübeck in 2019, some initial ideas were put forward on how to undertake such an staggering endeavor. A catalogue of criteria for comparing different forms of assemblies would need to include how often they were convened, who their participants were (both by number and status), what issues were discussed (their content and intended scope), what procedures were obtained for discussion and decision-making, how they interrelated with other types of assemblies, what the aim of the meeting was and how it was attained. These criteria can also help one to grasp how one type of assembly changed over time. An initial comparison led to the conclusion that the different forms of meetings may have assembled a different set of participants and discussed different issues, yet they employed strikingly similar procedures and underwent a similar process of institutionalization.
Alles andere als modern
Authors: Baur Kilian
Published: October 2024

Der Frieden von Stralsund von Mai 1370 wurde und wird zum Teil noch immer als Teil eines epochalen Konflikts zwischen zwei gegensätzlichen Ständen, rückständige Monarchie vs. fortschrittliche Bourgeoisie.
«ВАРВАРСКИЙ» ЛАНДШАФТ И АНТИЧНЫЕ ФОРМЫ ОБЩЕЖИТИЯ: НЕСКОЛЬКО СЮЖЕТОВ ИЗ ИСТОРИИ ДРЕВНЕГРЕЧЕСКИХ КОЛОНИЙ В СЕВЕРНОМ ПРИЧЕРНОМОРЬЕ
Authors: И.Е. СУРИКОВ
Published: October 2024

Глава включает в себя три очерка, освещающих различные аспекты ландшафтного (включая климатическое) влияния на жизнь древнегреческих колоний и колонистов в «варварском» северопричерноморском регионе. В первом очерке рассматриваются причины распространенности земляночного домостроительства у тамошних эллинов на первом этапе истории их полисов и, в частности, предлагается ответ на вопрос о том, имело ли в данном отношении место влияние на них со стороны местных варваров. Предмет второго очерка — специфика расселения в Крыму античных греков, которые, в отличие от своих византийских потомков, совершенно избегали освоения благодатного во всех смыслах южного побережья полуострова. Третий очерк предлагает некоторые перспективы прочтения источников, появляющиеся в связи с аргументированной гипотезой о существовании в I тыс. до н.э. впоследствии исчезнувшего пролива — восточного «дублера» Боспора Киммерийского; под этим углом зрения проанализирован известный рассказ Диодора о войне между сыновьями Перисада I.

This chapter includes three essays, which highlight various aspects of the influence of landscape (climate included) on the life of Greek colonies and colonists in the «Barbaric» Northern Pontic area. The first essay deals with the reasons of popularity of dugout house-building with the local Hellenes at the first stage of their poleis’ history and, in particular, offers an answer to the question whether in this respect influence on them took place on the part of local Barbarians. The subject of the second essay is some specific features of the settling in the Crimea of Ancient Greeks, who, unlike their Byzantine descendants, totally kept off the southern shore of the peninsula, which is a land of plenty. The third essay offers some new prospects in the reading of sources, which appeared in connection with the well-reasoned hypothesis about the existence in the first millennium B.C. of a strait (now extinct) that was an eastern “dub” of the Cimmerian Bosporus; from this standpoint, the well-known story of Diodorus about the war between Paerisades I’s sons is analyzed.
ФОРМЫ ВАРВАРСКОГО ПОВЕДЕНИЯ В СРЕДНЕВЕКОВОМ ГОРОДЕ (ФРАНЦИЯ, XIV–XV ВВ.)
Authors: М.В. КУЗЬМИНА
Published: October 2024

Глава посвящена рассмотрению вопроса о влиянии городского ландшафта и городской среды на поведение человека в Средние века. Именно благодаря особенностям городской жизни — скученности, плотной застройке, не дававшей возможности вести вполне уединённую жизнь, зависимости от мнения соседей, строго следящих за жизнью каждого члена общества; необходимости эмоциональной разрядки в играх и других развлечениях, из которых одни требовали больших пространств, и поэтому порой приобретали странные формы, другие же, такие как таверны и публичные дома, как раз благодаря своей востребованности становились не только центрами проведения досуга и социализации, но и криминальным местом, куда стекались люди различных социальных групп — варварское в людях получало выход в виде сверхагрессивных поступков, вплоть до убийств.

The chapter is devoted to the consideration of the issue of the influence of the urban landscape and urban environment on human behavior in the Middle Ages. It is due to the peculiarities of urban life — crowding, dense development, which did not allow for a completely secluded life, dependence on the opinion of neighbors, strictly monitoring the life of each member of society; the need of emotional release in games and other entertainment, some of which required large spaces, and therefore sometimes acquired strange forms, while others, such as taverns and brothels, precisely due to their demand, became not only the centers of socialization and leisure, but also a criminal place where people of various social groups flocked to — the barbaric in people found an outlet in the form of super-aggressive actions, including murder.
Talent Management Model in the Context of Coopetitive Interaction and the Knowledge Economy
Authors: Mayboroda Tetyana , Karpusha Vasyl , Balahurovska Inna
Published: October 2024

This article examines the talent management model in the context of the modern knowledge economy, emphasizing the importance of coopetitive interaction among business, education, and science. Effective talent management has become a critical factor in ensuring organizational development and innovation in this rapidly evolving environment. The study addresses how coopetition—a strategic combination of cooperation and competition—can enhance the effective utilization of intellectual potential across different sectors, contributing to long-term competitiveness and innovation. The authors thoroughly analysed the literature, exploring fundamental principles and approaches to talent management that are essential for developing competitive organizations in the knowledge economy. The study identifies critical challenges that organizations face in attracting, developing, and retaining talent, particularly in light of fast-paced technological advancements and the growing complexity of the labor market. Additionally, the researchers emphasize the need for cross-sector cooperation, arguing that integrating business, education, and science is pivotal for creating an innovative environment where talent can thrive. The article's central focus is coopetition, which involves a delicate balance between collaborative efforts and competitive strategies among businesses, educational institutions, and research entities. This coopetitive approach is presented to maximize the intellectual resources available within the knowledge economy, ensuring the effective development of creativity, knowledge, and innovation. Furthermore, the research addresses talent retention challenges, particularly in sectors with high demand for skilled workers. It emphasizes the importance of providing continuous opportunities for professional growth and innovation integration. The proposed talent management model is comprehensive and considers the unique aspects of the modern labor market. The model highlights the necessity for organizations to attract talent and create an environment where employees can continuously develop their skills and contribute to the organization's innovation processes. The practical implications of this study offer a framework for organizations seeking to implement effective talent management strategies that align with the needs of the knowledge economy. In conclusion, the article presents a detailed analysis of a new talent management model that promotes competitiveness and innovation through coopetitive interaction among business, education, and science. This model is particularly relevant in today’s knowledge-driven economy, where the ability to manage talent effectively plays a crucial role in organizational success. By fostering coopetition and ensuring the optimal use of intellectual potential, the proposed model contributes to developing innovative, competitive, and sustainable organizations capable of thriving in the knowledge economy.
Recent Advancement in Drug Designing as Small Molecules in Targeted Cancer Therapy: Challenges and Future Directions
Authors: Prakash Satya , Tyagi Priyanka , Singh Pratibha , Rajkumar  , Singh Atul Pratap
Published: October 2024

The adverse outcome that patients experience as a result of anti-cancer therapy failure is primarily caused by metastasis. Making cancer a chronic disease with regular but controlled relapses is the real issue in increasing cancer patient lifespans. This can only be achieved by developing efficient therapeutic techniques that target critical targets in the metastatic process. New targeted therapy medications continue to emerge, and research into the molecular targeted therapy of tumors is flourishing. The ineffectiveness of conventional chemotherapy in targeting metastatic cells is primarily due to its ability to promote the selection of chemo-resistant cell populations that engage in epithelial-to-mesenchymal transition (EMT), which in turn encourages the colonization of distant sites and maintains the initial metastatic process. In considering this circumstance, research into a broad range of small molecules and biologics has been initiated to develop anti-metastatic medications that target particular targets implicated in the different stages of metastasis. With their ability to concentrate on cancer cells while avoiding normal cells, tar-geted medications offer a promising alternative to conventional chemotherapy that is both highly effective and relatively safe. Many obstacles, including an inadequate response rate and drug resistance, persist for small-molecule targeted anti-cancer medications, despite significant ad-vancements in this area. We encouraged small-molecule-focused anti-cancer therapy develop-ment by extensively assessing them by target classification. We reviewed current challenges, listed licenced drugs and key drug candidates in clinical trials for each target, and made sugges-tions for improving anti-cancer drug research and development. This review aims to discuss pre-sent and future small molecule inhibitor research and development for cancer treatment.
Discovery of Two GSK3β Inhibitors from Sophora flavescens Ait. using Structure-based Virtual Screening and Bioactivity Evaluation
Authors: Pan Dabo , Zeng Yong , Jiang Dewen , Zhang Yonghao , Wu Mingkai , Huang Yaxuan , Han Minzhen , Jin Xiao-jie
Published: October 2024

Objective:

Kushen (Sophora flavescens Ait.) has a long history of medicinal use in China
due to its medicinal values, such as antibacterial, antiviral, and anti-inflammatory. Rapid discovery
of the components and the medicinal effects exerted by Kushen will help elucidate the science
of Kushen in curing diseases. GSK3β (glycogen synthase kinase-3 beta) is a protein kinase
with a wide range of physiological functions, such as antibacterial, antiviral, and anti-inflammatory.
The discovery of inhibitors targeting GSK3β from Kushen was not only helpful for the rapid
discovery of the components responsible for the efficacy of Kushen but also important for the development
of novel drugs.

Methods:

In this study, the chemical composition of Kushen was extracted from the TMSCP
database. Molecular docking, GSK3β enzyme assay, and molecular dynamics simulations were
used to discover the GSK3β inhibitors from the chemical composition of Kushen.

Results:

A total of 113 chemical compositions of Kushen were extracted from the TMSCP
database. Molecular docking indicated that 15 chemical compositions of Kushen scored better
than -8 kcal/mol against GSK3β. GSK3β enzyme assay demonstrated several inhibitory activities
of kushenol I and kushenol F with IC50 values of 7.53 ± 2.55 μM and 4.96 ± 1.29 μM, respectively.
Molecular dynamics simulations were used to reveal the interactions of kushenol I and
kushenol F with GSK3β from structural and energetic perspectives.

Conclusion:

Kushenol I and kushenol F could be the material basis for the antibacterial, antiviral,
and anti-inflammatory properties of Kushen.
GROUND MODIFIED ELECTRICALLY SMALL ANTENNA FOR DCS 1800 APPLICATIONS
Authors:   Government College - Chittur, India, M Nair Sreejith , M S Nishamo ,   Government College - Chittur, India, Abraham Manju ,   Baseliose Poulose II Catholicose College, India
Published: October 2024

A Uni-planar coplanar waveguide fed modified ground radiating structure with very compact size and simple structure, suitable for Global System for Mobile 1800 DCS application is discussed. Developed antenna offers a 2:1 VSWR bandwidth of 180 MHz ranging from 1.71 GHz to 1.89 GHz which is wider to cover both uplink and downlink bands of GSM1800. Antenna possesses linear polarization characteristics and offers a radiation pattern just like a quarter wave monopole. Developed antenna offers an average and uniform gain of 2.1 dBi with a moderate gain of 79.8% in the entire band of operation.
SIMULATION ANALYSIS OF A HYBRID ELECTRIC VEHICLE ASSISTED BY A SOLAR PANEL FOR SPEED CONTROL USING A FUZZY LOGIC CONTROLLER
Authors:   ndian Institute of Technology (Indian School of Mines), Dhanbad, India, Sahw Probeer , Maity Tanmoy ,   Indian Institute of Technology (Indian School of Mines), Dhanbad, India, R K Yadav ,   Raj Kumar Goel Institute of Technology, India
Published: October 2024

Transportation is a major sector which contributes to pollution at a global level. And, road transportation is the biggest source of pollution under the transportation sector. The prevailing global energy and climate conditions clearly suggest that less polluting vehicles must be used for transportation to curb carbon and other harmful emissions like SOX and NOX. An Electric Vehicle (EV) is a good option to combat pollution but its driving range is low. Thus, a Hybrid Electric Vehicle (HEV) is a well-suited option in this regard as it produces low tailpipe emissions and provides a long driving range. Integration of a solar panel with the on-board battery bank to power a HEV helps further in this regard as solar energy is a renewable form of energy. In this paper a solar assisted HEV is analyzed for speed control using a Fuzzy Logic Controller (FLC). The FLC performs throttle control of the Internal Combustion (IC) engine and torque control of a Permanent Magnet Synchronous Motor (PMSM) drive to achieve the control of speed of the HEV. The modelling and validation of results through simulations is carried out using the software package MATLAB/Simulink.
OPTIMISED META-HEURISTIC QUEUING MODEL IN VLSI PHYSICAL DESIGN
Authors:   St. Xavier’s Catholic College of Engineering, India, L Mary Florida , S Brilly Sangeetha ,   IES College of Engineering, India, K Krishna Prasad ,   Srinivas University, India
Published: October 2024

In this paper, the Markov M/D/1/B queuing model that has been created will be discussed. One approach to evaluating the effectiveness of a router is to use a model of a queue that is analogous to the one that we presented above. Having said that, this can be accomplished in a variety of different ways. According to the results of the studies, altering the service rate has a discernible bearing not only on the amount of data the system processes but also on the effectiveness of its operation. For the purpose of demonstrating the viability of our methodology, we built an output-queuing router using FPGA. When utilising this method, it is possible to observe that the queues and the control unit of the router take up an excessive amount of space on the silicon. This is a consequence of the fact that this method is utilised. The difference in effectiveness between a theoretical model and a real prototype is only 2%. This is a very small margin. The study implemented our design on a Xilinx FPGA Vertix II Pro family 100K chips. In the following subsections, we present the router FPGA synthesis results and evaluate its performance.
AN INTELLIGENT WEARABLE ROBOTIC DEVICE FOR KNEE REHABILITATION
Authors:   Dr. N.G.P. Institute of Technology, India, S Prabakar , K Porkumaran ,   Dr. N.G.P. Institute of Technology, India, S Ramanan ,   National Institute for Empowerment of Person with Multiple Disabilities, India, K Cheranmadevi ,   National Institute for Empowerment of Person with Multiple Disabilities, India, R Karthikeyan ,   Dr. N.G.P. Institute of Technology, India
Published: October 2024

Around the globe, the population suffered with stroke and spinal cord injury are rapidly increasing who are in need of an effective and efficient solution for their day to day life management. Paraplegia is a form of paralysis in which function is substantially impeded from the waist down or lower extremities but not the arms and it is sometimes called as partial paralysis. More than 73% people with paraplegia have perfectly healthy legs. The main aim of this proposed study is to develop an efficient rehabilitation system to treat paralysis patients of paraplegia. In this proposed research work, an assistive knee exoskeleton is designed and developed as a wearable robot with One Degree of Freedom (ODF) movement. This system is attached with the disabled limb with the aim of delivering appropriate motion and pragmatic functionality to the affected knee for suitable rehabilitation. This exoskeleton knee is controlled by the instrumented cane as a wearable sensory system to establish the coordinated movement through patient’s motion task estimation in order to control the trajectory of ODF exoskeleton robot. The computational intelligent control systems provides assisted rehabilitation motion in coherence with the motion of other unassisted upper limbs through the wearable sensors and switches in the instrumented cane. The instrumented cane is a helpful assistive device for rehabilitation in terms of balance and movement for those who suffering neurological disorders, in particular stroke and spinal cord injury. The pilot study on a subject using the proposed system describes utilization of instrumented cane as a feasible interface between the patient and exoskeleton to improve in terms of motion in their day-to-day activities. Also, the system is designed with inertial measurement module integrated with wireless communication device in order to assist real-time control of exoskeleton. The outcomes of the proposed research work validated and appreciated by the expert physicians and therapists. This study strives the purpose of an instrumented cane that able to contribute to the cognitively assisted locomotion technology as well as improves the mobility of physically challenged people.
USER ACTIVITIES ANALYSIS IN LOCATION BASED SOCIAL NETWORK VIA ASSOCIATION RULES
Authors:   Ferhat Abbas University, Algeria, Belayadi Yahia , Khababa Abdallah ,   Ferhat Abbas University, Algeria, Attia Abdelouahab ,   Mohamed El-Bachir Ibrahimi University of Bordj Bou Arreridj, Algeria, Akhtar Zahid ,   State University of New York Polytechnic Institute, United States of Americ
Published: October 2024

In recent years, the field of the Internet of Things (IoT), including smart and wearable devices, has witnessed a tremendous advancement leading to the collection of a wide variety of information not only about users but also their activities via various systems such as social networks, apps and so on. Thus, the collection of this large amount of data allows social systems to reach a wide variety of targets and gives more visibility about users and their profiles. It can also help to improve the services and functionalities of the users. Besides, the analysis and prediction of user’s activities in location-based social networks (LBSNs) have received much attention both from industries and research communities, especially in smart city developments, which give much importance to the automation of the LBSNs. In this paper, we present a new method based on association rules for user activity analysis in LBSNs. In particular, the Apriori algorithm has been applied to extract the consequential and advantageous rules to categorize users’ profiles. Empirical evaluations on a publicly available large-scale real-world dataset, named Gowalla, demonstrate the effectiveness of the presented association rules-based system in analyzing users’ activities via LBSNs.
CODE PRESENCE USING CODE SENSE
Authors:   Bharathiar University, India, M A Krishna Priya , Selwyn Justus ,   Vellore Institute of Technology, Chennai, India
Published: October 2024

Programmers are tightly occupied with the development workflow. Conscious contemplation of the right code solution and proactive presence of the solution lists is presented in this paper. Refining and sensing the optimum solution lists and the proactive presence of code solutions is incorporated in this code sense algorithm. This architecture allows developers to generate and integrate best code solutions directly in to solution bases. Code Sense Interface can support collaboratively with various platforms which can be plugged in with any development interface. Using Code sense, proactive presence of code is implemented as an assistant named Code Proactive Assistant. Code suggestions for a particular requirement scenario are considered as the primary goal. In addition to showing the feasibility of this approach, it provides further evidence in support of the claim that integrating specialized code sense interfaces directly into the editor is valuable to professional developers.
PERFORMANCE ANALYSIS OF ANOMALOUS DETECTION SCEHMES BASED ON MODIFIED SUPPORT VECTOR MACHINE AND ENHANCED RELEVANCE VECTOR MACHINE
Authors:   Sona College of Technology, India, S Senthil Kumar , S Mythili ,   Sona College of Technology, India
Published: October 2024

Anomalous transactions are common activity happening on the financial oriented transaction. Detecting those anomalous transactions from the financial transaction patterns is the most complex task which is focused in this work. In the existing work it is achieved by introducing the method namely Fuzzy Exception and Fuzzy Anomalous Rule (FEFAR). The accuracy of this existing work FEFAR found to be lesser which is resolved in the proposed research work. There are two research works has been proposed those are namely Rule Pruning based Anomalous Rule Detection Strategy (RPARD) and Lasso Regression based Improved Anomalous Detection Scheme (LR-IADS). Both of these methods attempt to find the anomalous transaction from the given input database by finding the anomalous rules. Each method differ in its methodologies, thus the accuracy of the methods would differ. The main goal of this analysis work is to compare the performance of existing and proposed methodologies based on simulation outcome. This research work aims to highlight the performance variation between the proposed and existing techniques and the best method that can offer accurate anomalous transaction detection. The analysis of the research work is carried out on matlab environment over four databases namely soil, bank, german statlog and auto mpg based on which performance outcome has been given.
CLASSIFICATION OF TOMATO DISEASES USING ENSEMBLE LEARNING
Authors:   Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, India, S Jeyalakshmi , R Radha ,   Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, India
Published: October 2024

A Plant disease is any dysfunction of a plant, caused by living organisms, which affects the quality and quantity of yield. These symptoms are visually shown on the plant leaves. This paper discusses classification of Tomato diseases such as Late Blight, Septoria Leaf Spot and Yellow leaf curl virus while distinguishing the healthy leaf at the same time. An experimental sample size of 1817 was considered in conducting this study. This work differentiates diseased tomato leaf images with healthy leaf images. The classifiers Random Forest, Multilayer Perceptron Neural Network and Support Vector Machines were trained and got a prediction accuracy of 88.74%, 89.84%, and 92.86% respectively in classifying diseases. Then, the prediction results of Random Forest, Multilayer Perceptron and Support Vector Machines were combined using Soft Voting classifier and obtained a highest accuracy of 93.13% in classifying tomato diseases.
GRAPH NEURAL NETWORK LEARNING IN LARGE GRAPHS - A CRITICAL REVIEW
Authors:   University of Mumbai, India, Gavande Ashish , Kulkarni Sushil ,   University of Mumbai, India
Published: October 2024

Graph Neural Networks have been extensively used to learn non-Euclidian structures like graphs. There have been several attempts to improve the training efficiency and to reduce the learning complexity in modelling of large graph datasets. In this paper we have reviewed the approaches which perform convolutions to model large graphs for classification and prediction. We have critically analysed each of these approaches and veracity of their claims of reduced complexity and have reported their shortcomings. We have further analysed the approaches from graph-dataset perspective.
PLANT DISEASE RECOGNITION AND CLUSTERING USING FUZZY ALGORITHM ON DATA MINING
Authors:   Hindustan College of Engineering and Technology, India, R Sabitha , G Kiruthiga ,   IES College of Engineering, India
Published: October 2024

Deep learning models are capable of performing sophisticated calculations, but they''''re not suitable for mobile and handheld devices due to their vast size and demanding computational requirements. Using an automated process, we intend to identify the diseases of plants so that we can build a process which begins with pre-processing, separates diseased leaf area, calculates features based on the Gray-Level Co-occurrence Matrix (GLCM), chooses and classifies features, and ends with decision making. Through threshold segmentation, we were able to isolate the diseased leaf areas in the maize plants, and then use this information to create fuzzy decision rules for the assignment of images of Common Rust to its severity class. These results were obtained with six colour and texture features. Plant disease clustering is performed with the Fuzzy Algorithm. The measurements show higher performance to the conventional methodologies and are ranked highest in terms of feature extraction method. This suggests that leaf-based plant disease diagnosis is the most appropriate method. These capabilities can be considered by the addition of new disease classifications or specific crop or disease classifications.
A COMPARISON OF MISSING DATA HANDLING TECHNIQUES
Authors:   Sona College of Technology, India, S David Samuel Azariya , V Mohanraj ,   Sona College of Technology, India, J Jeba Emilyn ,   Sona College of Technology, India, G Jothi ,   Sona College of Arts and Science, India
Published: October 2024

Missing data is a regular concern on data that professionals have to deal with. Efficient analysis techniques have to be followed to find interesting patterns. In this study, we are comparing 16 different imputation methods namely Linear, Index, Values, Nearest, Zero, slinear, Quadratic, Cubic, Barycentric, Krogh, Polynomial, Spline, Piecewise Polynomial, From derivatives, Pchip and Akima. These techniques are performed on real time UCI dataset and are under Missing Completely at a Random (MCAR) assumption, our result suggests the nearest, zero, quadratic and polynomial imputation methods which provides above 96% of accuracy when compared to the other techniques.
A HYBRID ENSEMBLE METHOD FOR ACCURATE FUZZY AND SUPPORT VECTOR MACHINE FOR GENE EXPRESSION IN DATA MINING
Authors:   KSG College of Arts and Science, India, S Vasanthakumar , N Ranjith ,   KSG College of Arts and Science, India
Published: October 2024

Malignancy is a bunch of infection which spreads all through the human body. Since it is an exceptionally deceptive illness its determination is of vital importance. Information mining innovation helps in arranging and bunching the malignancy information and this procedure assists with distinguishing potential disease patients by investigating the data alone. In this examination we analyze three information mining calculations, namely PCA, Genetic calculation and Hierarchical Fuzzy C Means (HFCM). The hereditary calculation is done using the Quantum-enhanced Support Vector Machine (QSVM). The outcome demonstrates that the proposed calculation accomplishes a better outcome when contrasted to the other two calculations.
IOT BASED ENVIRONMENT COMFORT LEVEL PREDICTION MODEL USING ENSEMBLE LEARNING
Authors:   Bishop Heber College, India, R Vijayalakshmi , L Jayasimman ,   Bishop Heber College, India
Published: October 2024

Predicting indoor environment comfort through machine learning algorithm is considered as an important research topic nowadays. People spend most of the time inside the building by doing some kitchen work, reading, watching TV, work in office building, learning in classroom, patients in hospital, workers in industry etc. Environment should be comfortable for healthy living. Thus predicting the comfort level of the environment is necessary for keeping good health and well being. Machine learning algorithm play an important role in prediction model. This paper focus on predicting the comfort of environment using machine learning classifier model. This model is used to train and improve the robustness of the model. This ensemble model is applied to reduce bias factor to enhance the stability and accuracy of the result.
AUTOMATIC GENERATION OF PARAMETERS IN DENSITY-BASED SPATIAL CLUSTERING
Authors:   University of Mumbai, India, Ravi Jayasree , Kulkarni Sushil ,   University of Mumbai, India
Published: October 2024

As a result of emerging new techniques for scientific way of collecting data, we are able to accumulate data in large scale pertaining to various fields. One such method of data mining is Cluster analysis. Of all clustering algorithms, density-based clustering is better in terms of clustering quality and the way the data are handled. Density based clustering is advantageous over other clustering algorithms in the following ways – arbitrary shaped clusters are formed; number of clusters need not be known and noise is handled. However, there are two main points that are critical in density-based clustering. Firstly, it is not effective while handling datasets of varied density. Secondly, the selection of input parameters e and MinPts play a critical role in the quality of clustering. This paper proposes a model – Automatic Generation of Parameters in Density-Based Spatial Clustering (AGP-DBSCAN) that aims at improving the density-based clustering by generating different candidate parameters. With these candidates, we will be able to handle both uniform density and varied density datasets. The results of experiments also look promising for different clustering datasets.
MANAGING IRRIGATION NEEDS BASED ON SMART DECISIONS USING MACHINE LEARNING
Authors:   Government College of Technology, Coimbatore, India, R Bhavani , Thambi Ajoe ,   Government College of Technology, Coimbatore, India
Published: October 2024

Optimized utilization of water for agriculture is a big challenge in today’s world. Internet of Things (IoT) based solutions along with machine learning techniques help in achieving effective utilization of waters in farming landspace. This paper presents sensor-based acquisition of soil moisture, temperature and humidity from the farm. Data are then stored in the server and clustered into two groups. Next machine learning based classification models like Naïve Bayes (NB), K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) are applied to decide irrigation need. The performance measures of the classification models show that K-NN classifier performs better than the other two classification models considered in this study.
BANGLA HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTION NEURAL NETWORK
Authors:   Bhilai Institute of Technology, India, De Shankha , Rawal Arpana ,   Bhilai Institute of Technology, India
Published: October 2024

Since, last one-decade, numerous deep learning models have been designed to resolve handwritten character recognition task in languages, namely, English, Chinese, Arabic, Japanese and Russian. Recognition of Bengali handwritten character from document image datasets is undoubtedly an open challenging task. Due to the advancement of neural network, many models have been developed and it is improving performance. The LeNet is a pioneering work in the field handwritten document image recognition specially hand written digits from the images by using CNN. This paper focuses on designing a convolution neural network with refinements on layers and its parameter tuning for Bengali character recognition system for classification of 50 different fonts. Our revised CNN model outperforms on some existing approach and shows font-recognition accuracy of 98.46%.
ANALYSIS AND REVIEW ON FEATURE SELECTION AND CLASSIFICATION METHODS ON CERVICAL CANCER
Authors:   Rathnavel Subramaniam College of Arts and Science, India, Kuruvilla Anjali , B Jayanthi ,   Rathnavel Subramaniam College of Arts and Science, India
Published: October 2024

Cervical cancer is one of the most widely recognized gynecologic malignancies on the world and it is demanding since this malignant growth happens with no signs. As per World Health Organization (WHO), cervical cancer is the fourth most recurrent disease which is higher death rate that influenced women everywhere in the world. It has demonstrated that early discovery of any cancer when followed up with suitable diagnosis and treatment can expand the patient survival rate. But the existing techniques have problem with imbalanced dataset and feature selection-based classification accuracy. To conquer the previously mentioned issues, the existing strategies are analyzed different procedures of data mining and feature selection techniques which can be applied to bring out hidden information from the cervical cancer dataset. In this review, classification process and feature selection-based classification are performed to improve the given cervical cancer dataset accuracy significantly. In the classification process, the imbalanced data and redundant features are not handled effectively. Hence the feature selection-based classification is required to improve the cervical cancer classification accuracy. This survey is also analyzed the merits and shortcomings of each method applied to application. The comparative analysis is done using various classification techniques like Support Vector Machine (SVM), K Nearest Neighbor (KNN), Convolution Neural Network (CNN) and Synthetic Minority Oversampling Technique + Random Forest with Recursive Feature Elimination (SMOTE+RFE+RF) approach. The experimental result shows that the SMOTE+RFE+RF approach provides better performance in terms of higher accuracy, specificity, Positive Predicted Accuracy (PPA) and Negative Predicted Accuracy (NPA) and sensitivity rather than the other existing methods.
ANALYSIS ON DIFFERENT METHODS FOR SIGN LANGUAGE IDENTIFICATION
Authors:   P K R Arts College for Women, India, M Prema , P M Gomathi ,   P K R Arts College for Women, India
Published: October 2024

Sign Language (SL) is the most organized and structured type of hand/arm gesture in the communicative hand/arm gesture taxonomies. The ability of machines to comprehend human actions and meanings has numerous uses. SL identification is one area of focus. SL is employed by the deaf and hard-of-hearing communities to communicate. Hearing-impaired persons communicate via visual indicators instead of vocal communication and sound patterns. SL also uses facial expressions and body postures as a medium of communication. Pattern matching, computer vision, natural language processing are the key factors in SL identification. This study reviews state-of-the-art methodologies employed in current SL identification research, comparing the various algorithms at each stage. Discuss the challenges and limitations of gesture identification research in general, as well as SL identification in particular. Overall, this paper gives a thorough introduction to the topic of automated SL identification, paving the way for future research.