Miracle-dz/UniFAR-FTUs huggingface dataset:
ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%; Naive Bayes: 83%.
An Enhanced Approach Of RDF Graph Data In-Memory Processing For Social Networks With Performance Analysis

— Ever-growing linked open data proliferate in size and number giving rise to “Big data problems”. Consequently, there is a rapid increase inf semantic data and services. It is significant to handle and provide more solutions to very large semantic data graphs are very significant. As the MapReduce algorithms are iterative and less effective, there is a need to go for in-memory processing. Ain of this paper is providing an enhanced RDF graph data processing approach with improved performance includes representation of RDF as a Directed labeled Multigraph, convert that RDF Graph as property Graph to have modeling workarounds (intermediate nodes), Reification and Singleton Property. Build this initial graph in high-performance analytic system SPARK with Graph X library using SCALA language to provide higher-order functions. So that functions and data structures can be stored in distributed memory and use SPARQL for query processing. The objective is of this paper is to propose an enhanced RDF Graph in-memory processing method that quickens the accessing of RDF Graph data, improves response times, and reduces the execution of time of RDF Graph data processing of this Social Network data.
Transcription Restores Dna Repair to Heterochromatin, Determining Regional Mutation Rates in Cancer Genomes Repository Citation Transcription Restores Dna Repair to Heterochromatin, Determining Regional Mutation Rates in Cancer Genomes Transcription Restores Dna Repair to Heterochromatin, Determinin

Transcription restores DNA repair to heterochromatin, determining regional mutation rates in cancer genomes" (2014). Comments This is an open access article under the CC BY license Graphical Abstract Highlights Regional genomic mutational rates reflect differential access to DNA repair Transcription restores DNA repair access to tightly packaged chromatin We model gene mutation rate based on transcription level and chromatin state In Brief Zheng et al. report that variable mutation densities within cancer genomes result from differential access of DNA repair machinery , imposed by chromatin state. By showing that transcription restores DNA repair to tightly packaged DNA, their study reveals natural differences in expression level as a potentially important modulator of oncogene mutation rate. SUMMARY Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcrip-tionally silent genomic regions of cutaneous squa-mous cell carcinomas (cSCCs) arising in an XPC
Improved underground utilities asset management – assessing the impact of the UK utility survey standard (PAS128)

Buried utilities (e.g. gas, water, electricity, drainage), many installed decades ago, are vital urban support systems. Their locations are often not well documented, yet accurate location greatly helps replacement, rehabilitation and maintenance of existing services and is crucial for installation of new utilities: it minimises utility strikes / third party damage and the huge associated costs of delayed / additional streetworks. Several international standards and specifications aim to provide guidance on utility surveys to increase client confidence in their accuracy and repeatability. The most recent specification is PAS (Publicly Available Specification) 128 in the UK for underground utility detection, verification and location. This paper investigates the impact of PAS128 by comparing trial site survey results from different utility survey specialists with each other and with the vertical and horizontal accuracies at trial excavations. While the overall detection level was very good for all companies, the research revealed that the definition of quality level QLB1 needs to be reconsidered as it was not achieved in many places with respect to the vertical accuracy. It is recommended that future revisions of PAS128 differentiate between accuracy and confidence since detection by multiple sensing technologies increases the confidence, but not the accuracy as suggested by PAS128
A MODIFIED SVM ALGORITHM TO ENHANCE THE CANCER CLASSIFICATION

The survival rate of breast cancer patients has increased due to the advancements in the treatment of the disease. These include the use of newer and more effective drugs. There are various types of breast cancer, which can be treated through different methods. Currently, there are numerous studies that are focused on developing a better understanding of this disease. In order to improve the classification of breast cancer, a new machine learning algorithm is proposed. This method uses support vector machine learning to enhance the performance of existing models. The proposed model can rectify the inconsistencies in the existing breast cancer dataset and improve its performance. It can also create a high-quality Wisconsin Diagnostic Breast Cancer (WDBC) data set. The proposed model can then predict the likelihood of a patient developing breast cancer. It can also diagnose the patient based on the data collected. The researchers were able to test the proposed model against several machine learning models. They were able to achieve high accuracy levels.
IMPROVING FACE RECOGNITION IN LOW ILLUMINATION CONDITION USING COMBINATION OF IMAGE ENHANCEMENT AND FACE RECOGNITION METHODS

Some face recognition models are constantly challenged by low illumination. However, we can address this issue by employing a low illumination image enhancement filter on the face recognition model. This study compares four low illumination image enhancement methods: MIRNet, Adaptive Gamma Correction, RetinexNet, and Retinex. SSIM evaluates these four models using the LOL dataset. The assessment findings are applied to both the ArcFace model and Facenet for face recognition, and they are evaluated with a combination of the Essex Faces96 and Essex Grimace datasets. The final model based on our suggested approaches is a combination of MIRNet and ArcFace models and MIRNet and Facenet models. MIRNet and Facenet combination perform better in higher illumination percentages, whereas MIRNet and ArcFace combination performs better in lower illumination percentages. At both 20 and 10% illumination percentage, the combined approach of low illumination image enhancement MIRNet method and Facenet model obtained Rank-1 Accuracy of 0.96 and 0.94. While the combination of MIRNet and ArcFace achieved a 0.84 Rank-1 Accuracy for a 05% illumination percentage.
Landscape-Based Models that Predict July Thermal Characteristics of Lower Michigan Rivers

Water temperature is one of the most important environmental factors affecting the physiology, life-history, and distribution patterns of stream biota. However, the ability to assess the influence of temperature on observed patterns of species distribution and abundance is hampered by a lack of site-specific temperature data collected across relatively broad geographic regions. In this paper, we present 2 models that can be used to estimate July thermal characteristics using landscape attributes for streams in the Lower Peninsula of Michigan. We used multiple linear regression to construct models that predict July average weekly maximum and minimum stream temperatures from catchment- and local-scale landscape variables. Water temperature data were collected at 171 sites representing 12 major watersheds in Lower Michigan. Catchment- and local-scale landscape variables were obtained from existing maps using a Geographic Information System (GIS). The best models predicting July thermal characteristics explained from 59-60 % of the spatial variation in stream temperatures. When outlier sites were removed, these models explained from 70-81 % of the spatial variation in stream temperatures. These models suggested that channel morphology, ground water accrual, and riparian forest cover were important variables controlling temperatures in Lower Michigan rivers. The results of this study illustrate the importance of catchment- and local-scale attributes in controlling instream habitat, especially in heterogeneous landscapes. These models provide a cost-effective method to generate broad-scale temperature information. Such information can provide baseline data environmental impact assessment and can be used to guide management decisions.
BUILDING A NEURAL NETWORK TO ASSESS THE LEVEL OF OPERATIONAL RISKS OF A CREDIT INSTITUTION

This article considers the issues of managing the operational risks of a credit institution arising in the process of using information technologies. To manage operational risks associated with the use of IT in banks, the authors propose methods based on the use of artificial neural networks. The decisive stage in operational risk management is the collection and intellectual analysis of data. At this stage, hazards become risks that can be implemented into the logic of managerial decision-making. Optimal IT risk management involves online monitoring of numerous parameters that affect the possibility of risks and determine their consequences. The risks that banks face using IT solutions extend to third-party IT providers that many banks rely on for cloud storage and other services. These systems can slow down or fail, preventing customers from accessing ATMs or mobile apps. Even the speed of a technological change creates operational risk. In connection with the above, the automation of operational risk management based on the use of intelligent technologies is one of the most urgent tasks for credit institutions. The authors suggest that the networks can be trained on statistical data specific to each institution to enable accurate forecasting and event analysis using complex neural network technologies. The study identified models that demonstrated accurate results (it is necessary to take into account some limitations stated by the authors) on the training set formed by experts, leading to an expected increase in forecast accuracy by at least 40% upon implementation. The practical recommendations offered in the study have the potential to improve risk management practices and enhance the efficiency of credit organizations.
Multi-Antenna NOMA for Computation Ofﬂoading in Multiuser Mobile Edge Computing Systems

This paper studies a multiuser mobile edge computing (MEC) system, in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation ofﬂoading, such that different users can simultaneously ofﬂoad their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancellation (SIC) to efﬁciently decode all users’ ofﬂoaded tasks for remote execution. In particular, we pursue energy-efﬁcient MEC designs by considering two cases with partial and binary ofﬂoading, respectively. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS’s decoding order for SIC. For the case with partial ofﬂoading, the weighted sum-energy minimization is a convex optimization problem, for which an efﬁcient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary ofﬂoading, the weighted sum-energy minimization corresponds to a mixed Boolean convex problem that is generally more difﬁcult to be solved. We ﬁrst use the branch-and-bound (BnB) method to obtain the globally optimal solution, and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to ﬁnd suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation ofﬂoading design signiﬁcantly improves the energy efﬁciency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that
Opleiding Informatica Variant Algebra based Star Allele Calling

Personal genetic variation has a significant impact on the metabolism of drugs. This leads to harmful side effects and reduced effectiveness for some people. Genetic screening is recommended for several drugs, but this is not yet common practice. Problems with current methods are that they often require high-quality sequencing data and that it is not always clear how a method arrives at its conclusions. Here, we propose a novel method of finding star allele classifications for individuals based on their genetic sequence. This method achieves perfect accuracy on a benchmark dataset with phased sequencing data. The method also achieves an accuracy of 94.7% on lower-quality data with incomplete phasing information. Further, we propose a method of visualisation of the call that makes the conclusion more explainable.
An Improved Classification Model For Identifying The Phishing Attacks

— An attack known as phishing is a malicious scheme that is used to steal private information from users by tricking them into attacking via a fake website that has been designed to imitate and look very similar to an actual website. The perpetrator of the attack will steal the user's private information, including their username, password, and personal identification number (PIN), and then use that information to make fraudulent transactions. The credentials of the information holder, as well as any money they may have, will be seized. The phishing website and the legitimate website will have a high degree of understandable similarity, which will allow an attacker to steal the user's credentials from the legitimate website. There are a variety of methods available, including blacklisting, whitelisting, heuristics, and machine learning, which can be utilised in order to detect phishing attempts. Learning machines are used these days, and it has been proven that they are more effective. The phishing website's source code features, as well as its URL features and picture features, are all extracted by the proposed approach. In order to obtain the reduced features, the characteristics that have been extracted are input into the algorithm for ant colony optimization. The decreased features are then provided to the Naive Bayes classifier once more in order to determine whether the website in question is authentic or phished.
Application of Radiogenic Isotopes to Ore Deposit Research and Exploration

Lead (Pb) isotope compositions of sulfide minerals coupled with rocks associated with an ore deposit provide critical constraints on the source of metals and fluid pathways in a fossil hydrothermal system (Heyl et al., 1966; Stacey et al., 1968; Gulson, 1986; Sanford, 1992). Lead isotope compositions of sulfide minerals also provide chronologic information, either absolute or relative, for ore deposition (for example, Carr et al., 1995) and can also be used as an exploration tool during prospect evaluation (Gulson, 1986; Young, 1995). These varied applications of Pb isotopes to achieve an understanding of the ore genesis process are too diverse to be adequately discussed in a single overview chapter. Instead, this chapter focuses attention on what Pb isotopes tell us about (1) the sources of Pb nd other metals in ore deposits, (2) the interaction between hydrothermal fluids and wall rocks, (3) the influence of basement rocks and tectonic setting on Pb sources in ore deposits in magmatic arcs, and (4) the application f crustal-scale Pb isotope variations to an understanding of regional controls on ore deposition Before Pb isotopes pertinent to understanding ore genesis can be examined, we must review some basic principles of Pb isotope geochemistry (Fig. 1). Elegant discussions of U-Th-Pb geochemistry are presented by Doe (1970), Faure (1977), Zartman and Haines (1988), Garipy and Dupr (1991), and Dickin (1995). The following discussion is simplified from these sources. Three isotopes, 208Pb, 207Pb, and 206Pb, are partly the radiogenic daughter products from the radioactive decay of one isotope of thorium (232Th 208Pb*) and two isotopes of uranium (238U 206Pb* and 235U 207Pb*). (Note that an asterisk (*) after an isotope denotes that it is the product of radioactive decay of a parent isotope over time and is not the total abundance of the isotope in a sample. ) The abundance of radiogenic isotopes has grown since the earth formed some 4.56 billion years ago (Fig. 1), building upon an initial concentration. The fourth isotope of Pb, 204Pb, is stable and has no long-lived parent isotope nordoes it decay to another isotope. Time-integrated growth of radiogenic Pb isotopes from an arbitrary starting time, t1, to an ending time, t1, in an environment where there has been no migration of U, Th, and their daughter products, is described by standard decay equations: These equations simply show that the measured present-day Pb isotope composition is equal to the sum of the initial
Intelligent Recognition of Mixture Control Chart Pattern Based on Quadratic Feature Extraction and SVM with AMPSO

ABSTRACT Zhang, M. ; Cheng, W. , and Guo, P., 2015. Intelligent recognition of mixture control chart pattern based on quadratic feature extraction and SVM with AMPSO. Take control charts as the principal statistical process control tools. The recognition accuracy of abnormal control chart patterns (CCPs) directly influences the quality control of production process. (Most of the existing studies focus on the basic recognition of abnormal CCPs, but the observed process data could be of mixture patterns in real world, which consists of two or three basic patterns. This paper introduces a hybrid intelligent model for recognizing the mixture control chart pattern that includes three main aspects: feature extraction, classifier and parameter optimization. In feature extraction, statistical and sharp features of observation data are used as data input and principal component analysis as the second feature extraction to get the effective data of the classifier. A multi-class support vector machine (SVM) is applied for recognizing the mixture patterns. Finally, adaptive mutation particle swam optimization is used to optimize the SVM classifier by searching the best values of the parameters of SVM kernel function. The simulation results show the proposed algorithm with little features has better recognition accuracy compared with other methods.
XCP-i: eXplicit Control Protocol pour l'interconnexion de réseaux haut-débit hétérogènes

eXplicit Control Protocol (XCP) est un protocole de transport qui controle efficacement l'evolution de la fenetre de congestion de l'emetteur, evitant ainsi la phase de slow-start et d'evitement de congestion. XCP necessite cependant la collaboration de tous les routeurs sur le chemin de la source vers le recepteur, ce qui est pratiquement impossible a realiser en realite, sinon ses performances peuvent etre beaucoup moins bonnes que celles de TCP. Cette forte dependance de XCP en des routeurs specialises limite considerablement l'interet de deployer des routeurs XCP sur une portion du reseau. Nous proposons dans cet article une extension de XCP, appelee XCP-i, qui permet d'interconnecter des nuages XCP avec des nuages non-XCP sans perdre le benefice du controle precis de XCP sur la fenetre de congestion. Les resultats de simulation sur des topologies correspondant typiquement a des scenarios de deploiement incremental montrent que les performances de XCP-i sont bien superieures a celles de TCP sur des liens a haut-debit.
Tributary Plunging in an Urban Lake (Onondaga Lake): Drivers, Signatures, and Implications 1

Abstract: A combination of long‐term fixed‐frequency and robotic monitoring information for a polluted urban lake, Onondaga Lake, New York, and two of its tributaries is used to resolve the propensity for, and occurrences of, tributary plunging. Cooler temperatures (T) and higher salinity (S) are primarily responsible for the elevated density and plunging of one of these polluted streams for the summer through early fall interval. In‐lake transport of this plunging tributary, which receives inputs from combined sewer overflows (CSOs), is tracked by its high S during dry weather, its high turbidity (Tn) with associated lower S (dilution with rainwater) following runoff events, and by its characteristic ionic composition. These signatures are documented extending from the creek mouth, through a connecting navigation channel, through the inflow zone of the lake, and into metalimnetic depths of pelagic portions of the lake. The entry of this polluted tributary below the depth interval(s) of primary production and contact recreation has important implications for the ongoing major rehabilitation program for this lake. The plunging phenomenon diminishes the benefits previously expected for related features of the lake’s water quality from ongoing management efforts to abate CSO inputs and reduce nonpoint nutrient loading from the tributary. Previously this tributary tended to instead enter the upper layers of the lake during the operation of an adjoining soda ash manufacturing facility (closure in 1986), as a result of high lake S caused by the industry’s ionic waste discharge.
THE ACCURACY OF MAPE MODEL ON CNN TO CLASSIFICATION OF MOVING OBJECT

Neural Network which is the adoption of the performance of the human brain based on various types of data that process into information and knowledge. There are many algorithms in neural networks such as Convolutional Neural Network (CNN). CNN is an algorithm that is able to provide knowledge based on image data and moving images. For example, moving images are traffic activities consisting of various vehicles and vehicle numbers. So there needs to be a classification in recognizing vehicle numbers from moving media for data center purposes in smart cities. Success in classification is of course the value of accuracy. However, CNN, which generally uses ordinary accuracy techniques, needs to be tested with other accuracy techniques such as Mean Absolute Percentage Error (MAPE) in order to obtain optimal accuracy models. From the accuracy model based on the usual formula, the highest 92% results from frame 2 while the MAPE accuracy model achieves 100% the highest from frame 4. And the last, his shows that CNN will be more optimal in moving image classification with the MAPE accuracy model.