Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Siamese Postclassification Fusion Network for Semantic Change Detection

Semantic change detection (SCD) aims to recognize land cover transitions from remote sensing images of the given scene acquired at different times. The semantic change maps produced by SCD can provide not only the locations of changes but also the detailed change types (e.g., “from-to” change type). This exhaustive change information plays a significant role in various applications. Postclassification methods with multitemporal remote sensing images have been widely used in SCD. However, many existing methods suffer from the accumulation of misclassification errors. In this article, a deep Siamese postclassification fusion network (PCFN) is proposed to address this problem. PCFN is composed of the Siamese classification network (SCN) for land cover mapping (LCM) and soft fusion network (SFN) for postclassification SCD, respectively. In PCFN, SCN is designed to effectively integrate the temporal correlation between two images by processing the joint features, further improving classification performance. Then, SFN is constructed to determine changes and identify the specific change types through automatic soft fusion. SFN fuses the multitemporal LCM features generated by SCN, then adaptively maps them to the decision space for soft fusion during network training, and predicts the final semantic change maps. Extensive experimental results on two challenging SCD datasets demonstrate that our method can alleviate the error accumulation effectively by combining temporal correlation integration and soft fusion, and achieve promising performance superior to the other state-of-the-art methods in SCD.
Diagnosis of cardiovascular disorder by CT images using Machine learning technique

Cardiac imaging plays a predominant role in the diagnosis of cardio vascular disorders. The main aim of this project is to diagnosis the cardiac disorders using CT imaging along with a machine learning technique (Artificial neural network). Image processing techniques such as pre-processing, segmentation and classification are using for processing the image. Here segmentation and classification of the CT image plays an important role to diagnose the disorder, for segmentation ANN is being used and for classification SVM is employed both comes under the machine learning techniques. The implementation of machine learning techniques emerges as the artificial intelligence tool that will be of service to diagnosis of cardiovascular diseases. By constructing different algorithms for each process we can obtain précised and automated output. So that, the output of the experiment helps the clinician to diagnose the cardiac disorders more clearly and can be moved to further treatment
Detection of Parkinson's Disease Using Voice Changes and Hand-tremor

Parkinson's (PD) is a degenerative, progressive condition that disturbs nerve cells in the basal ganglia and the locus niger, which are deep areas of the brain. The neurotransmitter dopamine is produced by nerve cells in the complicated body part, and it is responsible for conveying messages that plan and govern bodily action. In certain people, the dopamine-producing nerve cells in the nucleus niger begin to migrate away. PD symptoms such as trembling, weak muscles, stiffness, and balance issues appear after 80% of dopamine is eliminated. Detection of PD from rapidly given cues, like as speech and handwriting, is crucial to various social behaviors, including as fast imitation and empathy. With technological improvements and, as a result, the prominence of audio collecting devices in everyday life, dependable models that transform audio data into a screening aid for healthcare providers might possibly deliver diagnoses that are both cheaper and more accurate. This experiment uses two modalities to identify Parkinson's disease. Voice and handwriting are the two modalities. Audio processing and machine learning methods in Matlab are used to identify PD via speech. The characteristics are derived from the audio of people with Parkinson's disease who have a local binary pattern. For classification, machine learning techniques are utilized. Machine learning classifiers are used in the classification process using neural networks, and embedded systems are used to identify PD via handwriting. The microcontroller is used to see the disease with the help of a gyroscope sensor. The principle of angle measurement is determined by calculating the angle of free vibration of the hanging mass with respect to the gyroscope casing. As a result, if the vibration passed the edge, the person is suffering from this disease.
Experimental study of energy harvesting in UHF band

A huge progress of down-sizing technology together with trend of decreasing power consumption and, on the other hand, increasing efficiency of electronics give the opportunity to design and to implement the energy harvesters as main power sources. This paper refers to the energy that can be harvested from electromagnetic field in the unlicensed frequency bands. The paper contains description of the most popular techniques and transducers that can be applied in energy harvesting domain. The overview of current research and commercial solutions was performed for bands in ultra-high frequency range, which are unlicensed and where transmission is not limited by administrative arrangements. During the experiments with Powercast’s receiver, the same bands as sources of electromagnetic field were taken into account. This power source is used for conducting radio-communication process and excess energy could be used for powering the extra electronic circuits. The paper presents elaborated prototype of energy harvesting system and the measurements of power harvested in ultra-high frequency range. The evaluation of RF energy harvesters for powering ultra-low power (ULP) electronic devices was performed based on survey and results of the experiments.
DDoSLSTM: Detection of Distributed Denial of Service Attacks on IoT Devices using LSTM Model

Distributed Denial of Service (DDoS) attack is a persistent complication in the network's security. These attacks have been detected by many machine learning algorithms and feature selection methods. This paper chose the Recurrent Neural Network based long short-term memory model that works on time series data and handles long time-dependent inputs, thereby detecting DDoS attacks. In our paper, we focused primarily on increasing the classification performance of the LSTM model. Multi-layer LSTM model has been used for binary and multiclass data and maximum accuracy attained is 99.46% (1- Layer LSTM with Binary data) followed by 99.16% for 2-Layer LSTM with Multiclass Grouped data. The proposed DDoSLSTM model outperforms other state-of-the-art techniques, including deep neural network (DNN), RNN, CNN, Transformers.
Early Depression Prediction and Estimation with EEG Signals using Machine Learning Algorithm

Depression is a mental disorder that causes feelings of unhappiness and loss of interest. Depression is afflicting a significant portion of the world's kids and adults at the present time. Falls in the early detection of depression or falls in the timely analysis of a depressed individual might create significant complications. Depression can cause serious issues. It is one of the most common causes of suicidal behaviour. But unfortunately, our culture still refuses to recognise depression as a physical condition, resulting in a significant percentage of sad people being unidentified and untreated. In this research, we examined machine learning classifiers that use sociological and psychological data to determine whether a person is sad or not.
Mapping the Surface Water Using Convolution Neural Networks for Remote Sensing Technology

Surface water mapping is important in a range of remote sensing studies, including predicting water availability, analyzing its change over time, and estimating floods and droughts. Using image from actually active Landsat operations, a surface and groundwater map from any specific area can be generated as frequently as every 8 days. Conventional Landsat water indices necessitate the use of hand - picked input parameters that varies based on the sector being observed and the meteorological conditions. They also suffer from a high number of failures, which are caused primarily by ice and snow, as well as topography and weather shadowing being misinterpreted as water. To solve these problems, methods for generating large water maps often depend on auxiliary information and expert systems based on complex rules. Instead, we use a data-driven, comprehensive approach to mapping the surface water. development of Landsat-trained convolutional neural network is done. Deep-Water Map, reads the features of water sources in data collected from around the world. Using only Landsat bands as inputs, the The competent model illustrates water originating from territory, frost, glaciers, fog, and shadows.
The Benefits of a Networked Distributed Surveillance Architecture Using Both Cooperative and Non-Cooperative Sensing Systems

This paper explores benefits of incorporating low-cost reliable non-cooperative sensors into cooperative sensing systems configured in a distributed network. It is proposed that this type of architecture can be used to enhance the surveillance mission of users such as the FAA, the DOD, and the DHS. This paper will specifically consider a passive coherent location (PCL) system as the non-cooperative sensor. Some of the benefits of using PCL systems compared to the use of traditional primary surveillance radars (PSRs) are also presented. A top-level system description is also introduced to illustrate how L3Harris’s current infrastructure and communications network provides a value-added proposition to its users. Data collected and analyzed from a single Hensoldt Twinvis® passive radar located at L3Harris’s Herndon, VA location is presented to characterize the performance of a single Hensoldt TwInvis® passive radar against the Automatic Dependent Surveillance – Broadcast (ADS-B) baseline.
Evaluation of Air Route Network Congestion Based on Node Importance

There are some key waypoints in the air route network, which play an important role in controlling the whole network, and the effective identification of these important nodes is of great significance to relieve air traffic congestion and improve the efficiency of route operation. Based on the complex network theory, combined with network indexes and node flow concentration, this paper evaluates the node importance through the combination weighting evaluation model of the CRITIC-entropy weight method. On this basis, the simulation of route network congestion identification is carried out, and the results are compared with the traditional importance evaluation method. The results of air route network in Eastern China show that the important nodes identified by the combined weighting evaluation model not only have a large centrality, but also bear a larger traffic load. Based on the combination weighting evaluation model, the accuracy of identifying the importance of route network nodes is higher, and which can identify the easily congested nodes of air route network in time and judge the congestion status. This has a positive effect on preventing node failure and reducing route network congestion.
A Detailed Review of the IoT with Detection of Sinkhole Attacks in RPL based network

The “Internet of Things” (IoT) is internetworking of physical devices known as 'things', algorithms, equipment and techniques that allow communication with another device, equipment and software over the network. And with the advancement in data communication, every device must be connected via the Internet. For this purpose, we use resource-constrained sensor nodes for collecting data from homes, offices, hospitals, industries and data centers. But various vulnerabilities may ruin the functioning of the sensor nodes. Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized, secure routing protocol designed for the 6LoWPAN IoT network. It's a proactive routing protocol that works on the destination-oriented topology to perform safe routing. The Sinkhole is a networking attack that destroys the topology of the RPL protocol as the attacker node changes the route of all the traffic in the IoT network. In this paper, we have given a survey of Sinkhole attacks in IoT and proposed different methods for preventing and detecting these attacks in a low-power-based IoT network.
PLC BASED ELEVATOR CONTROL SYSTEM

In this paper, we presents programmable logic controller based elevator control system. We all are know, now a days, Elevator is an important transport medium in multi-storage building for vertical transportation. Preliminarily, traditional elevator control systems are works under relay logic, which contains more number of connections [2]. Hence it has become more difficult to debug the failures in the system and also it is highly feasible to alternate the connections. Therefore, here we use programmable logic controller to build the elevator model. To control the motion of the elevator we use DC geared motor. Here, we use buttons to give signals to the elevator to come to the desired floor. Here we selected Ladder diagram programming. Because, it is easy to program the PLC. The better use of PLC is due it's flexibility, speed, reliability, security, and it is easy in correcting errors.
A study on model fidelity for model predictive control-based obstacle avoidance in high-speed autonomous ground vehicles

ABSTRACT This paper investigates the level of model fidelity needed in order for a model predictive control (MPC)-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles even when the vehicle is close to its dynamic limits. The context of this work is large autonomous ground vehicles that manoeuvre at high speed within unknown, unstructured, flat environments and have significant vehicle dynamics-related constraints. Five different representations of vehicle dynamics models are considered: four variations of the two degrees-of-freedom (DoF) representation as lower fidelity models and a fourteen DoF representation with combined-slip Magic Formula tyre model as a higher fidelity model. It is concluded that the two DoF representation that accounts for tyre nonlinearities and longitudinal load transfer is necessary for the MPC-based obstacle avoidance algorithm in order to operate the vehicle at its limits within an environment that includes large obstacles. For less challenging environments, however, the two DoF representation with linear tyre model and constant axle loads is sufficient.
Evolution of thermal performance curves: A meta‐analysis of selection experiments

Temperatures are increasing due to global changes, putting biodiversity at risk. Organisms are faced with a limited set of options to cope with this situation: adapt, disperse or die. We here focus on the first possibility, more specifically, on evolutionary adaptations to temperature. Ectotherms are usually characterized by a hump-shaped relationship between fitness and temperature, a non-linear reaction norm that is referred to as thermal performance curve (TPC). To understand and predict impacts of global change, we need to know whether and how such TPCs evolve. Therefore, we performed a systematic literature search and a statistical meta-analysis focusing on experimental evolution and artificial selection studies. This focus allows us to directly quantify relative fitness responses to temperature selection by calculating fitness differences between TPCs from ancestral and derived populations after thermal selection. Out of 7561 publications screened, we found 47 studies corresponding to our search criteria representing taxa across the tree of life, from bacteria, to plants and vertebrates. We show that, independently of species identity, the studies we found report a positive response to temperature selection. Considering entire TPC shapes, adaptation to higher temperatures traded off with fitness at lower temperatures, leading to niche shifts. Effects were generally stronger in unicellular organisms. By contrast, we do not find statistical support for the often discussed “Hotter is better” hypothesis. While our meta-analysis provides evidence for adaptive potential of TPCs across organisms, it also highlights that more experimental work is needed, especially for under-represented taxa, such as plants and non-model systems.
Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI

Background DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific pipeline for joint data processing. Results We propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy results were obtained with tree ensembles, reaching above 95% for Parkinson’s disease. Dimensionality reduction can substantially decrease the number of features, without detriment to the classification accuracy. The best imputation methods achieve almost the same classification accuracy for data with missing values as for the original data. Explainable artificial intelligence approaches have allowed us to explain model predictions from both populational and individual perspectives. Conclusions We propose a methodologically valid and comprehensive approach to the classification of healthy individuals and patients with various diseases based on whole blood DNA methylation data using Parkinson’s disease and schizophrenia as examples. The proposed algorithm works better for the former pathology, characterized by a complex set of symptoms. It allows to solve data harmonization problems for meta-analysis of many different datasets, impute missing values, and build classification models of small dimensionality.
On tumoural growth and treatment under cellular dedifferentiation

Differentiated cancer cells may regain stem cell characteristics; however, the effects of such a cellular dedifferentiation on tumoural growth and treatment are currently understudied. Thus, we here extend a mathematical model of cancer stem cell (CSC) driven tumour growth to also include dedifferentiation. We show that dedifferentiation increases the likelihood of tumorigenenis and the speed of tumoural growth, both modulated by the proliferative potential of the non-stem cancer cells (NSCCs). We demonstrate that dedifferentiation also may lead to treatment evasion, especially when a treatment solely targets CSCs. Conversely, targeting both CSCs and NSCCs in parallel is shown to be more robust to dedifferentiation. Despite dedifferentiation, perturbing CSC-related parameters continues to exert the largest relative effect on tumoural growth; however, we show the existence of synergies between specific CSC- and NSCC-directed treatments which cause superadditive reductions of tumoural growth. Overall, our study demonstrates various effects of dedifferentiation on growth and treatment of tumoural lesions, and we anticipate our results to be helpful in guiding future molecular and clinical research on limiting tumoural growth in vivo.
Meta-Analysis of Transcriptomes in Insects Showing Density-Dependent Polyphenism

With increasing public data, a statistical analysis approach called meta-analysis, which combines transcriptome results obtained from multiple studies, has succeeded in providing novel insights into targeted biological processes. Locusts and aphids are representative of insect groups that exhibit density-dependent plasticity. Although the physiological mechanisms underlying density-dependent polyphenism have been identified in aphids and locusts, the underlying molecular mechanisms remain largely unknown. In this study, we performed a meta-analysis of public transcriptomes to gain additional insights into the molecular underpinning of density-dependent plasticity. We collected RNA sequencing data of aphids and locusts from public databases and detected differentially expressed genes (DEGs) between crowded and isolated conditions. Gene set enrichment analysis was performed to reveal the characteristics of the DEGs. DNA replication (GO:0006260), DNA metabolic processes (GO:0006259), and mitotic cell cycle (GO:0000278) were enriched in response to crowded conditions. To date, these processes have scarcely been the focus of research. The importance of the oxidative stress response and neurological system modifications under isolated conditions has been highlighted. These biological processes, clarified by meta-analysis, are thought to play key roles in the regulation of density-dependent plasticity. Simple Summary Population density can be an environmental cue to induce modification of insect morphology, physiology, and behavior. This phenomenon is called density-dependent plasticity. Aphids and locusts exhibit textbook examples of density-dependent plasticity but there is a lack of integrative understanding for insect density-dependent plasticity. To address this problem, we combined public gene expression data obtained from multiple studies and re-analyzed them (this process is called meta-analysis). The present study provides additional insight into the regulatory mechanisms of density-dependent plasticity, demonstrating the effectiveness of meta-analyses of public transcriptomes.
Genome Assembly and Analysis of the Flavonoid and Phenylpropanoid Biosynthetic Pathways in Fingerroot Ginger (Boesenbergia rotunda)

Boesenbergia rotunda (Zingiberaceae), is a high-value culinary and ethno-medicinal plant of Southeast Asia. The rhizomes of this herb have high flavanone and chalcone content. Here we report genome analysis of B. rotunda together with a complete genome sequence as a hybrid assembly. B. rotunda has an estimated genome size of 2.4 Gb which was assembled as 27,491 contigs with N50 size of 12.386 Mb. The highly heterozygous genome encodes 71,072 protein-coding genes and has 72% repeat content, with class I TEs occupying ∼67% of the assembled genome. Fluorescence In Situ Hybridization of the 18 chromosome pairs at metaphase showed six sites of 45S rDNA and two sites of 5S rDNA. SSR analysis identified 238,441 gSSRs and 4,604 EST-SSRs with 49 SSR markers common among related species. Genome-wide methylation percentages ranged from 73% CpG, 36% CHG and 34% CHH in leaf to 53% CpG, 18% CHG and 25% CHH in embryogenic callus. Panduratin A biosynthetic unigenes were most highly expressed in watery callus. B rotunda has a relatively large genome with high heterozygosity and TE content. This assembly and data (PRJNA71294) comprise a source for further research on the functional genomics of B. rotunda, the evolution of the ginger plant family and the potential genetic selection or improvement of gingers.