bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel two-stage Lung nodule detection in medical images based on deep learning
Authors: Ahmed zwaid Jassim Mohammed , Murad Masrah Azrifah Azmi , Khalid Fatimah binti , Manshor Noridayu 
Published: May 2023

Abstract
In order to solve the problems of low detection rate and high false positive of nodules in lung CT images by traditional computer-aided diagnosis systems. This paper introduces the real-time detection algorithm of medical images using the deep learning method. According to the three-dimensional nature of lung CT images, 3DFasterR-CNN is used to extract features to detect candidate nodules; then, the 3D convolutional neural network is used to remove false positive nodules. That saves the operation of filtering and difference map construction. The detection process mainly includes four steps: building a deep neural network, constructing suitable samples, training the network, and detecting. The results show that the algorithm has a good effect on the real-time change detection of medical images and can improve detection accuracy and efficiency. The average FROC value was 82.8%, compared with the traditional diagnosis and treatment. The recognition rate of the method has been significantly improved. The model can be used to assist doctors in diagnosing lung nodules and has a certain clinical application value.
Medical Image Analysis of Lung Tumor Diagnosis Based on Generalized Fuzzy C-Means Clustering Algorithm
Authors: zwaid Jassim Mohammed Ahmed , Murad Masrah Azrifah Azmi , Khalid Fatimah binti , Manshor Noridayu , Al-Jumaily Abdulmajeed 
Published: May 2023

Abstract
In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Generalized fuzzy c - means clustering algorithm was proposed. Brief introduce lung CT imaging to diagnose lung tumors requires a huge workload, which is often accompanied by long-term image reading and subjective evaluation. The experimental data comes from 15 patient samples which include two groups of samples are selected as test objects. The result shows the positive rate is high, resulting in misdiagnosis. In addition, great success of deep learning technology in the field of computer vision has made it possible to realize computer-aided diagnosis and screening of lung cancer.
A Non-Intrusive Load Decomposition Method Based on Generative Adversarial Networks and Asynchronous Federated Learning
Authors: Wang Dan , Fan Kaiyu 
Published: May 2023

Abstract
The non-intrusive load decomposition method is an essential approach of appreciating the water requirements of consumers. Traditional techniques of load breakdown have a number of drawbacks, including a poor level of water identification precision. In order to do this, a model for load decomposition based on generative adversarial networks has been developed. By applying a convolutional autoencoder to the total power noise, the generative network constructs the power breakdown sequence of the given home appliance. The discriminant network is then utilized to determine whether or not the created sequence is legitimate. In order to achieve a more natural and authentic sequence, the participants compete against one another. In light of the shortcomings of the centralized model training method, the network model implementation scheme based on asynchronous federated learning is utilized to replace the traditional convolution in order to realize the model’s lightweight nature. This allows the model to be applied to terminal devices such as smart water meters, and it is carried out in a cloud-edge collaborative manner. In addition, the model is carried out in a manner that is collaborative between the cloud and the edge. Training not only eases the strain of communication transmission but also protects the privacy of users and the integrity of their data.
Roller Bearing Fault Diagnosis Using Deep Transfer Learning and Adaptive Weighting
Authors: Liu Yan , Fan Kaiyu 
Published: May 2023

Abstract
A fault diagnosis approach for roller bearings utilizing deep transfer learning and adaptive weighting is suggested to address the issue that extra fault state samples in the target domain data of roller bearings impair the fault diagnostic accuracy. CNN-LSTM is a network model proposed by Lecun et al., which has good performance in image processing and image processing. It can effectively apply predictive local perception of time series and weight sharing of CNN, which can greatly reduce the number of networks and improve the efficiency of model learning. The method first establishes a feature extraction module, and uses a deep convolutional neural network to map bearing samples to a high-dimensional feature space. Secondly, uses the transfer learning concept to design a weighted domain discriminator, and adaptively weights the samples; and finally, through the confrontation in the feature space, the bearing samples are classified. Training to increase the domain similarity of the healthy state samples shared by the target domain and the source domain. Then measuring the similarity between these samples based on the sample weight size, and setting a threshold to label the additional fault state samples of the target domains as unknown. The suggested technique is validated using gearbox bearing data, roller bearing data from Case Western Reserve University, and locomotive wheel bearing data. The diagnostic accuracy of the samples is less than 80%, suggesting that the suggested approach can successfully overcome the effects of extra fault state samples and diagnose roller bearing faults.
Dynamic collaborative optimization coverage algorithm of UAV based on trajectory prediction in Internet of Vehicles
Authors: Zheng Maoxing , Hai Tao 
Published: May 2023

Abstract
This research presents a dynamic pre-deployment strategy based on vehicle trajectory prediction information to address the issues of coverage gaps of base stations and local traffic congestion in urban vehicle networking. Under the architecture of distributed federated learning and blockchain, numerous UAVs equipped with edge computing servers eliminate the central aggregation node and use an enhanced Raft method to train a unified Seq2Seq-GRU trajectory prediction model. In the training cycle, the scheme selects the nodes to perform parameter aggregation and model update based on the quantity of data given. Second, based on the model’s prediction findings, this study offers an enhanced virtual force guide deployment method that directs the UAV to dynamically deploy across each virtual force in order to increase the vehicle’s access rate and communication quality.
Three-phase fault analysis of power system transient stability based on TCSC controller using FPA for its location
Authors: Otuo-Acheampong Duku , Rashed Ghamgeen Izat , Mensah Akwasi Amoh , Haider Hussain 
Published: May 2023

Abstract
Preventing the power system from becoming overloaded and under stress to the point where it is on the verge of reaching the limits of its transient stability due to high power consumption. This paper proposed the dynamic security assessment for transient stability using Thyristor controlled series capacitor (TCSC) devices controller. The stability criteria are based on the severity indices for analyzing the fault in the power system. The index uses the time domain analysis simulation, making it easier and simpler to implement for the system operators to better prepare for potential disturbances by anticipating how power systems will behave. The optimal location and parameters setting of the TCSC was determined by Flower Pollination Algorithm (FPA) due to its higher convergence rate. The proposed index is tested on the IEEE 14-bus system and further discusses the effect of a three-phase short circuit fault, different fault locations, and fault clearing method. An analysis is done to determine how the short circuit faults on lines affect the transient voltage stability of the load bus and generator bus in the system. The findings show that by employing TCSC, the voltage drop was acceptable improved and stability margins were greatly increased.
Vibration Signal for Bearing Fault Detection using Random Forest
Authors: Abedin Tarek , Koh S. P. , Yaw Chong Tak , Phing Chen Chai , Tiong Sieh Kiong , Tan Jian Ding , Ali Kharudin , Kadirgama K. , Benedict F. 
Published: May 2023

Abstract
Based on the chosen properties of an induction motor, a random forest (RF) classifier, a machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct bearing states was used to evaluate the suggested methodology. The primary objective of this research is to evaluate the bearing defect detection accuracy of the RF classifier. First, run four loops that cycle over each feature of the data frame corresponding to the daytime index to determine the bearing states. There were 465 repetitions of the inner race fault and the roller element fault in test 1, 218 repetitions of the outer race fault in test 2, and 6324 repetitions of the outer race in test 3. Secondly, the task is to find the data for the typical bearing data procedure to differentiate between normal and erroneous data. Out of 3 tests, (22-23) % normal data was obtained since every bearing beginning to degrade usually exhibits some form of a spike in many locations, or the bearing is not operating at its optimum speed. Thirdly, to display and comprehend the data in a 2D and 3D environment, Principal Component Analysis (PCA) is performed. Fourth, the RF algorithm classifier recognized the data frame’s actual predictions, which were 99% correct for normal bearings, 97% accurate for outer races, 94% accurate for inner races, and 97% accurate for roller element faults. It is thus concluded that the proposed algorithm is capable to identify the bearing faults.
UAV-based edge computing system used in energy-saving offloading method
Authors: Abdulazeez Hussein Amer , Shaker Ahmed Maher 
Published: May 2023

Abstract
This paper proposes an offloading strategy based on unmanned aerial vehicle (UAV) managed computer resources to provide dependable communication and intensive processing power in complicated terrain. In order to reduce compute and offload user terminal energy consumption, a UAV-assisted edge computing model is built, taking into account the computing requirements of user terminals, the delay restrictions of computing jobs, and the energy limits of UAVs. The trajectory can be used to reduce the user terminal’s energy usage in the allotted amount of time. The simulation results demonstrate that the suggested approach is appropriate for various channel circumstances, allowing the user terminal to accomplish the work while outperforming existing benchmark systems in terms of energy usage. Optimizing the flight trajectory of the UAV will have a substantial impact when the signal-to-noise ratio SNR=20dB. The results demonstrate that, at T=5s, the UAV’s flying trajectory optimization strategy reduces energy consumption by 56.26%.
Novel cartographer using an OAK-D smart camera for indoor robots location and navigation
Authors: Han Yunpeng , Ali Mokhtarzadeh Amir , Xiao Shaozhang 
Published: May 2023

Abstract
In recent years, service robots have been widely used in people’s daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cloud matching. Then a software system is realized by integrating each module. The experiment proves that the system is practical and effective, and has practical value.
Probabilistic Analysis of RC Buildings Based on Incremental Dynamic Analysis
Authors: Mathews Merin , Jayalekshmi B R , Venkataramana Katta 
Published: May 2023

Abstract
Earthquake is a natural disaster that can induce immense damage to properties and human lives. Fragility curve, defined as the probability of reaching or exceeding a specific damage state under an earthquake excitation, provides a prediction of damage that may occur during an earthquake. Incremental Dynamic Analysis Curve (IDA curve) developed from Incremental Dynamic analysis can be considered as the initial step of fragility curve development. The present study summarises the development of IDA curve for a RC building considering maximum roof displacement and storey drift as Damage Measure (DM) and peak ground acceleration as Intensity Measure (IM). The fragility curves developed using incremental dynamic analysis provide an overview of the probability of exceedance of damage limits in an existing building when acted upon by different levels of earthquake excitations and the performance level can be improved by incorporating appropriate design changes.
Mechanical and Microstructure Properties of Plain and Blended Cement Structural Lightweight Aggregate Concrete Using Sintered Fly Ash Aggregates
Authors: Dhemla Pankaj , Somani Prakash , Satankar Raj Kumar , Swami B L 
Published: May 2023

Abstract
Infrastructure and industry have grown dramatically as a result of globalization and urbanization. However, there are certain challenges, such as meeting infrastructural needs that require a large amount of extraction of natural aggregates and disposal of waste generated as a result of industrialization. Both of these challenges have an adverse impact on the environment. Deforestation, erosion, contamination of nearby streams, and increase in noise, dust, and emissions are all consequences of aggregate extraction. The industrial waste can contaminate groundwater, lakes, streams, rivers and coastal waterways and harm the land and neighboring water bodies. Hence these challenges must be addressed as a priority for environmental sustainability. The current study developed structural lightweight aggregate concrete (SLWAC) using sintered fly ash aggregate (SFA). The investigation was done with the use of different binders such as Portland Pozzolana Cement (PPC), Ordinary Portland Cement (OPC), and Micro Fibre Cement (MFC). The present study explores the suitable binder to produce SLWAC at different water-cement ratios of 0.4, a0.44, and 0.48. The performance evaluation of SLWAC was investigated by mechanical behavior like compressive strength, split tensile, flexural strength, and impact test. Microstructure analysis is also included in this study to validate the mechanical experiment results. SLWAC made from OPC shows more mechanical strength than PPC and MFC, due to faster hydration and early strength gain. SLWAC made from microfiber cement, has more strength as compared to PPC. This could be due to fibers in MFC, acting as a reinforcing agent, which reduces micro cracks. The microstructure of SLWAC shows that the wall effect does not exist in SLWAC compared to normal aggregate concrete. It has a uniform and continuous microstructure of hydration products and partially penetrates the SFA. It could be associated with the superior interfacial transition zone having a lower w/c ratio because of the higher absorption of SFA. The findings show that all developed SLWAC can be used in structural members like beams, columns, and slabs.
A Comparative Study on Dynamic Response of Buildings Resting on Coir and Rubber mat Reinforced Soil Bed
Authors: Sreya M V , Jayalekshmi B R , Venkataramana Katta 
Published: May 2023

Abstract
Geotechnical seismic isolation has emerged as an efficient technique for mitigating the severe effects of earthquakes by providing smooth synthetic liners beneath foundations or between soil layers for dissipating seismic energy through sliding. This study investigates the efficacy of using a rubber mat and a natural coir mat as reinforcement materials within the soil to act as a seismic soil-isolation medium. A three-dimensional finite element simulation of five-storey buildings resting on raft foundations in soft soil with and without the soil-isolation mechanism has been performed. The reinforced soil-structure system was exposed to two different earthquake motions, such as the ground motions corresponding to the elastic design spectrum for Zone III as per the Indian standard code (IS 1893 (Part 1): 2016) and the Northridge earthquake (1994). The proposed study deals with the analysis of dynamic responses of buildings when the soil is reinforced with a coir mat and rubber mat under earthquake motions. The findings show that the seismic responses of low-rise buildings are significantly reduced by a novel technique proposed in this work to reinforce the soil with isolation materials in their mat form to reduce the seismic responses under earthquake loads.
Characterization of rhizobacteria secondary metabolites on maize (Zea mays) in marginal land
Authors: Ikhwan A , Septia ED , Sholihah A 
Published: May 2023

Abstract
Maize is one of the strategic food crops and has economic value and could be developed because of its position as the main carbohydrate and protein source after rice. This could be a reference for us to increase maize cultivation, seeing the role of maize is quite large, and the availability of maize is less stable every year. maize can grow optimally on productive land and marginal land, but at this time more productive land is used for rice production. One alternative for maize production is on marginal land especially dryland. The main problem in dryland is limited water. One way to overcome this is by inoculating Rhizobacteria which can increase the growth and resistance of maize to drought. Rhizobacteria possess tremendous potential for modulating the physiological response to water deprivation, thus ensuring plant survival under such stressful conditions. The research aimed to find rhizobacteria have secondary metabolite for resistance to drought. Rhizobacteria exploration and secondary metabolite isolation was conducted at University of Muhammadiyah Malang. The research results have obtained 10 isolates capable of synthesizing extracellular secondary metabolites: osmoprotectants, phytohormones, and organopesticides that can be used by plants. The results of the analysis by GS-MS (Gas Chromatography-Mass Spectrometry) rhizobacteria showed that three compounds were synthesized. The osmoprotectants compound synthesize ware found namely: (a) (2E)-3,7-dimethylocta-2,6-dienal 16.66%;(b) (E)-hept-2-enal12.55%;(c)2-methylsulfanylethanamine 5.34%; (d) 1,3,5-trimethoxybenzene 4.42%. It is also capable of synthesizing phytohormones: (a) cinnolino [4,3-b] quinoxaline 6.00%; (b) 1-methyl-2-phenylindole 5.90%; (c) 1H-Indole 3.64%; (d) N-2-Fluorenylperfluorpropionamid 3.02%. These rhizobacteria were also able to synthesize: (a) 2-chloro-4-methoxy phenol 11.32%; (b) (E)-non-2-enal 9.87%; (c) 4-chloro-2,6-dipyridin-2-ylpyridine 7.07%; (d) 2-(4-chlorophenyl) hexane nitrile 6.45%. The Osmprotectant compounds aim to protect cells from drought conditions, phytohormones to stimulate plant growth and organopesticides to overcome pests and diseases.
Corn (Zea mays L.) productivity in acid dry land used zigzag planting method
Authors: Meithasari D , Endriani  , Wibawa W , Suretno N D , Tambunan R D 
Published: May 2023

Abstract
Zigzag planting method technology was an alternative technology to increase plant population without affecting plant growth and development. The purpose of this study was to determine the productivity of corn with different planting methods, such as the zigzag and the linear planting method applied by farmers on acid dry land in the Lampung area. The research was conducted at the Natar Experimental Station in Negara Ratu Village, Natar District, South Lampung Regency, Indonesia from March to June 2020. The study used a two-factor randomized block design, namely the hybrid corn varieties (NK Sumo, NK 22 Jumbo, and NK Super) and the planting methods (zigzag and linear planting methods). Observations were made on ten plants per sampling plot, which were determined randomly. The results showed that the zigzag planting method produces a higher yield (8.16 tons/ha) compared to the linear planting method (4-5 tons/ha grain yield). Moreover, the zigzag planting method increased the plant population by 80% without disturbing plant growth and development. The zigzag planting method was able to increase corn production by 30-40%.
The utilization of apical rooted cuttings for the seed production of potato varieties for processing
Authors: Handayani T , Kusmana  , Sahat J P , Pertiwi M D , Waryat  
Published: May 2023

Abstract
Potato development, especially processing varieties, is constrained by the availability of seeds. Acceleration of seed supply can be done by planting apical rooted cuttings directly in the field to obtain seeds equivalent to extension seeds class. Field activity has been carried out in Brebes Regency, Central Java, in 2021, to study the opportunities for using apical rooted cuttings in the potato seed production. Apical rooted cuttings from seven potato varieties for processing purposes (i.e. AR 08, Spudy, Ventury Agrihorti, Golden Agrihorti, Medians, and Atlantik M) were transplanted in a completely randomized design with 4 replications. Plant growth and tuber production characters were influenced by genotypic factor, indicated by the variation of those characters between varieties. The tuber number per plant ranged from 8.53 (Medians) to 22.80 (Spudy). Meanwhile, the tuber weight per plant was between 215.13 g (Golden Agrihorti) to 702.67 g (Spudy). In this study, Spudy showed the highest growth performance and tuber production compared to the other six varieties. In general, plants grown from apical rooted cuttings produced tuber which is appropriate for seed purposes. It is hoped that the apical rooted cuttings can support the sustainability of potato production, and furthermore, support the domestic potato-based industry.