Miracle-dz/UniFAR-FTUs huggingface dataset:
In vitro Antioxidant and Anticoagulant Activities of Sulphated Polysaccharides from Brown Seaweed (Turbinaria ornata) (Turner) J.Agardh

Objective: The aim of this study was to investigate In vitr o antioxidant and anticoagulant activities of the sulfated polysaccharides from brown seaweed Turbinaria ornata Methods: the sulphated polysaccharides extracted from T. ornata was tested for chemical analysis, determination of total antioxidant activity, Reducing power (FRAP) assay, DPPH radical-scavenging activity and blood coagulation assay. Results: The sulphated polysaccharides showed high levels of carbohydrate (53.15 %) and low sulphate content of 24.6 %. Similarly , T. ornata revealed high in total antioxidant of 47.49 mg GAE/g and a high percentage of 81.21% in DPPH radical scavenging activity. Whereas FRAP assay showed maximum of 42.78 mg GAE/g. The anticoagulant assay, the maximum APTT activity recorded at 1 125 sec from 1000 µg/ml. In the PT activity, the maximum of 81 sec recoded at 1000 µg/ml. Where the TT assays showed maximum of 320 sec at 1000 µg/ml. Conclusion: The antioxidant capacities of the T. Ornata sulphated polysaccharides showed potential rich sources of natural antioxidants and anticoagulants. Further studies are necessary for isolation and characterization of the active molecules, which can be used to treat various oxidative stress-related diseases.
PathExtractor: A Path-Semantic extraction Algorithm for Mobility Prediction

In recent years, mobility prediction has attracted much attention. Prediction methods include two steps, extracting spatial-temporal features to convert the trajectory to location sequences and constructing a model to make further predictions. Traditional methods often define the location as grids or points of interest (POIs) in mining spatial-temporal features. But these methods may not perform well in prediction because of losing detailed information of trajectories. Thus, a novel location semantics is necessary to compress detailed trajectories. In this paper, a path semantics extraction method, PathExtractor was proposed to extract typical paths and build path sequences, which contains complete information of trajectories. Furthermore, to verify that path sequences can effectively express movement patterns, the prediction is performed by constructing a recurrent neural network model. Finally, in order to evaluate the application value of path semantics, path similarity is used as performance indicator, and experiments prove the accuracy of path prediction and geographical precision higher than others.
Random Caching Strategy in HetNets with Random Discontinuous Transmission

In this paper, we jointly explore random caching and cooperative transmission in heterogenous networks (HetNets) with random discontinuous transmission (DTX). We consider a realistic scenario where joint transmission is not always available and assume two cases depending on whether joint transmission is available. With the help of stochastic geometry, a tractable expression for the average successful transmission probability (STP) is obtained. We then formulate the STP optimization problem to find the optimal caching policy. In addition, we analyze the STP under random DTX. Compared with several existing caching policies in the previous works, we show that the optimal caching policy indeed achieves a significant performance gain.
Joint Quality Selection and Caching for SVC Video Services in Heterogeneous Networks

Edge caching has been regarded as an effective way to relieve backhaul pressure as well as reduce service delivery delay. In this paper, we attempt to improve user’s quality of experience (QoE) of video services in cache-enabled heterogeneous networks (HetNets). Scalable video coding (SVC) based video services are considered and hence the proper video quality can be selected for each user according to its channel conditions. Intuitively, caching video layers that cannot be delivered to users leads to the waste of limited cache resource. Based on this observation, we formulate the joint video quality selection and caching problem, with the goal to maximize user’s QoE. Furthermore, we propose a two-fold dynamic programming algorithm to approach the optimal video quality selection and caching strategies. Finally, the simulation results demonstrate that the proposed strategies can utilize the limited cache space more effectively and achieve more QoE gains compared to existing caching strategies.
Machine Learning in Capital Markets: Decision Support System for Outcome Analysis

Decision support systems using Artificial Intelligence in the context of financial services include different application ranging from investment advice to financial trading. The analysis of order flow provides many challenges that can be addressed by Machine Learning (ML) techniques in order to determine an optimal dynamic trading strategy. The first step in this direction is represented by the outcome analysis of order flow: the model should identify strong predictors that determine a positive/negative outcome. The aim of this work is the proposal of a closed-loop ML approach based on decision tree (DT) model to perform outcome analysis on financial trading data. The overall approach is integrated in a Decision Support System for Outcome Analysis (DSS-OA). Taking into account the model complexity, the DT algorithm enables to generate explanations that allow the user to understand (i) how this outcome is reached (decision rules) and (ii) the most discriminative outcome predictors (feature importance). The closed-loop approach allows the users to interact directly with the proposed DSS-OA by retraining the algorithm with the goal to a finer-grained outcome analysis. The experimental results and comparisons demonstrated high-interpretability and predictive performance of the proposed DSS-OA by providing a valid and fast system for outcome analysis on financial trading data. Moreover, the Proof of Concept evaluation demonstrated the impact of the proposed DSS-OA in the outcome analysis scenario.
Collaborative Learning Model for Cyberattack Detection Systems in IoT Industry 4.0

Although the development of IoT Industry 4.0 has brought breakthrough achievements in many sectors, e.g., manufacturing, healthcare, and agriculture, it also raises many security issues to human beings due to a huge of emerging cybersecurity threats recently. In this paper, we propose a novel collaborative learning-based intrusion detection system which can be efficiently implemented in IoT Industry 4.0. In the system under consideration, we develop smart “filters” which can be deployed at the IoT gateways to promptly detect and prevent cyberattacks. In particular, each filter uses the collected data in its network to train its cyberattack detection model based on the deep learning algorithm. After that, the trained model will be shared with other IoT gateways to improve the accuracy in detecting intrusions in the whole system. In this way, not only the detection accuracy is improved, but our proposed system also can significantly reduce the information disclosure as well as network traffic in exchanging data among the IoT gateways. Through thorough simulations on real datasets, we show that the performance obtained by our proposed method can outperform those of the conventional machine learning methods.
Access Characteristic-based Cache Replacement Policy in an SSD

The flash translation layer (FTL) of a modern solid-state drive (SSD) employs the address translation because a host system and a flash memory have different granularity units. For this reason, a large piece of data is broken into several pages, which contain partial-pages. Writing these partial-pages induces lots of read-modify-write operations that increase write amplification. If there is a locality among consecutive write accesses, delaying the cache eviction of partial pages can help reduce the number of flash writes. However, conventional cache replacement policies, which are not based on access characteristics, are not easy to take advantage of the delayed cache eviction. Therefore, in this paper, we propose the access characteristic-based cache replacement policy to reduce the number of cache evictions due to partial-page updates. First, the policy distinguishes eviction priority by data size because the access frequency of cache lines varies depending on the requested data size. Second, the policy manages eviction priority by receiving the data access characteristic, such as reusability, from the host. The proposed policy reduces the number of flash writes due to cache eviction by 10.6 % and 9.4 % on average, respectively, compared to FIFO and LRU policies.
Alzheimer’s Disease Detection using Machine Learning Techniques in 3D MR Images

This study proposes a new method for the detection of Alzheimer’s Disease (AD) using first-order statistical features in 3D brain Magnetic Resonance(MR) images. Alzheimer’s disease is a neurodegenerative disorder that affects elderly people. This is a progressive disease and early detection and classification of AD can majorly help in controlling the disease. Recent studies use voxel-based brain MR image feature extraction techniques along with machine learning algorithms for this purpose. Grey and white matter of the brain gets affected and damaged due to AD and so studying these both prove to be more effective in predicting the disease. The proposed work uses 3D structural brain MR images to separate the white and grey matter MR images, extract 2D slices in the coronal, sagittal and axial directions and select the key slices from them for performing feature extraction on them. Feature extraction is applied on top of these slices to calculate the first-order statistical features and the prominent feature vectors generated by PCA are selected for further study. In the classification phase, different classifiers take the selected features as its input to predict the classes AD (Alzheimer’s Disease) or HC (Healthy Control) based on the observations in the validation set. Experimental results show that the accuracy of 90.9 % compared to other techniques.
Research on the Impact of Electric Vehicles on Power Grid Operation Risks and Frequency Emergency Control

In this paper, IEEE 33 bus system is used as the test system, and the effects of electric vehicle charging load with different penetration rates on power grid operation risks are simulated from bus voltage, network loss and frequency parameters. Based on the System Frequency Response (SFR) model the dynamic frequency analysis method is studied. Then, the framework base on aggregator for electric vehicles to participate in frequency emergency control of power grid and the calculation method of the capacity of electric vehicles those can be dispatched are proposed in this paper. Finally, a high-power vacancy event based on IEEE3 9 bus system is constructed and verified by the algorithm in this paper. The result shows that the electric vehicles participation in power grid frequency emergency control can effectively reduce the amount of load shedding.
AN INVESTIGATION OF THE AGROCLIMATIC POTENTIAL OF THE FOREST–SAVANNA TRANSITION ZONE OF NIGERIA FOR THE CULTIVATION OF SORGHUM

The agroclimatic potential of the forest–savanna transition zone of Nigeria for the cultivation of sorghum (Sorghum bicolor) was investigated. Five phenological stages of the crop formed the basic unit of time for the investigation. Selected agroclimatological thermal and moisture indices were measured daily and processed into ten-day (decadal) averages for the 1993 and 1994 cropping seasons. The thermal indices, temperature and photoperiods were optimal for sorghum growth but, on the basis of the distribution of rainfall and actual water availability in relation to the crop's water consumption, the moisture indices were above optimum. However by relating effective water availability to the indices of moisture adequacy for the growth of the crop, it was found that the agroclimatological moisture indices were optimum during the moisture sensitive stages. A comparison of the vegetative growth and grain yield of sorghum in the study area and elsewhere in the savanna region confirmed the agroclimatic potential of the forest–savanna transition zone for sorghum production.
Online Product Grading using Sentimental Analysis with SVM

This paper implements a system that provides a user an authenticated score of any online product computed with the help of the NLP technique called Sentiment Analysis and Opinion Mining. Sentimental Analysis is the anatomization of the judgment of a user on a particular product. It scans through the sentences in search of keywords that deliver definite emotions. In our system, we have used a Support Vector Machine (SVM) classifier to generate a fine-grained Sentimental Analysis report. This classifier helps us to categorize the reviews collected from the product’s website URL into predefined categories to affirm the user’s thought process and generate a rating from 1 to 5 (1 being the lowest, 5 being the highest). The system makes use of this technique to gather all the accrued reviews and convert the textual meaning into a meaningful average grade. This process makes it possible for any prospective customer to have a definitive conclusion on the review of the product.
Biological Diversity of Peru: Determining Priority Areas for Conservation

Abstract The distributions of both the organismal and ecological diversity of Peru were evaluated through cartographic analyses in relationship to Peru's national system of protected areas. Also identified and mapped were areas so poorly known that they represent conservation information gaps, areas that cannot currently be evaluated, but which should not be overlooked. These methods revealed that the protected area system in Peru did not adequately protect either organismal or ecological diversity. In the short term, inclusion of unprotected priority areas in the national system is the best way to improve biological conservation. Over long time periods, it is also important to make decisions about the priority of areas that are information gaps. This study provides a useful point of comparison with other countries that are at different stages in the task of assembling biodiversity information. It was clear that i) the identified priority areas were important for national-level planning; ii) the drier and nonforested ecosystems seldom have been included in conservation efforts; iii) because degradation processes will constantly change, the conservation status of a particular area will also change and should not be confounded in the setting of priorities with the value of the biological diversity present; and iv) the identification of information gaps is the most transparent method for keeping decision-makers advised as to the limits of scientific knowledge on the distribution of biological diversity.
A Secure Session Key Negotiation Scheme in WPA2-PSK Networks

Wi-Fi Protected Access II Pre-Shared Key (WPA2-PSK) is a hot way to wireless security in public Wi-Fi networks. It works on a pre-configured passphrase shared with all stations in the same Wi-Fi network. Session keys (e.g., Pairwise Transient Key, PTK) between stations and the access point (AP) are derived from the passphrase. The WPA2-PSK networks can authenticate external stations, however, they fail to guarantee confidential communication if internal attackers own the passphrase in the network since all stations derive their PTK using the same passphrase. To prevent internal stations from eavesdropping the PTK, a secure session key negotiation scheme in WPA2-PSK Networks (SSKNS) is proposed. We introduce a temporary session key (TSK), which is encrypted using elliptic curve cryptography (ECC) and exchanged securely between the station and the AP in the Wi-Fi association process. Through AES algorithm with TSK, the station encrypts its own nonce used to generate the unique PTK in the 4-way process. Our scheme neither modifies the legacy process related to PTK generation nor adds plethoric overhead on excessive protection of all messages. Security analysis and simulations performed in NS-3 demonstrate that by consuming a few computation overheads, SSKNS can effectively provide security level, compared with the existing schemes.
Edge Caching Replacement Optimization for D2D Wireless Networks via Weighted Distributed DQN

Duplicated download has been a big problem that affects the users’ quality of service/experience (QoS/QoE) of current mobile networks. Edge caching and Device-to-Device communication are two promising technologies to release the pressure of repeated traffic downloading from the cloud. There are many researches about the edge caching policy. However, these researches have some limitations in the real scenarios. Traditional methods are lacking the self-adaptive ability in the dynamic environment and privacy issues will occur in centralized learning methods. In this paper, based on the virtue of Deep Q-Network (DQN), we propose a weighted distributed DQN model (WDDQN) to solve the cache replacement problem. Our model enables collaboratively to learn a shared predictive model. Trace-driven simulation results show that our proposed model outperforms some classical and state-of-the-art schemes.
An Energy-Efficient Mixed-Task Paradigm in Resource Allocation for Fog Computing

We study the energy efficiency problem for a fog computing system with multiple users and fog nodes. In order to handle various kinds of tasks at the same time, a mixed-task paradigm is proposed to combine both binary offloading and partial offloading. Then, a mixed-task resource allocation problem with the consideration of computation and communication resources is formulated as a mixed integer nonlinear programming problem (MINLP), which cannot be handled by the traditional relaxation algorithm due to the joint design of binary and partial offloading. To solve this problem efficiently, we first adopt a replacement-based method to transform the problem, and then design an augmented Lagrange method (ALM)-based resource allocation scheme. To further accelerate the solution procedure, a novel optimization technique, AMSGrad, is applied to the designed scheme. The performance of the proposed scheme is demonstrated by simulation results.
Modeling, Analysis, and Design of Resonant Proportional Stationary Frame Current Controller for Grid-Tide Three Phase Controller

The increasing penetration of distributed energy resources (DER) in a power system network call for the advanced control strategy for accurate operation of micro-grid under both islanded and grid-tied operation mode. The dc-ac converter based DER generally employed convention PI controller and designed in the d-q rotating frame of reference. In this work, the stationary αβ PR controller is used for the grid-tied mode of operation. For accurate power-sharing, the PQ controller is implemented for parallel-connected DGs to feed the available power to the grid side. PR controller exhibits superior performance as compared to the PI regulator, and it eliminates the steady-state error, improved dynamic response, and mitigates the grid harmonics quite effectively and efficiently. To validate the performance of the proposed system, extensive real-time simulations are carried out by using the MATLAB/Simulink environment. Results illustrate the performance of the PR controller is better than a conventional PI controller. The THD of output current in the grid-connected Mode is less than IEEE recommend standard.
Data Mining for Early Gastric Cancer Etiological Factors from Diet-Lifestyle Characteristics

Gastric cancer is predominantly caused by demographic-diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (EGC) factors from diet and lifestyle characteristics of Mizo-ethnicity using supervised machine learning algorithms. For this study, 80 cases and 160 controls are selected and a dataset containing 11 features that are core risk factors for the gastric cancer have been chosen for data mining. The learning curves show Naive Bayes, Logistic Regression and Multilayer perceptron are the best fit classification algorithms for our dataset. Data models are constructed and evaluated using: brier score, accuracy, precision_recall curves for cases (patients) and controls (healthy individuals), and false positives. The data interpretation shows Naive Bayes has the highest classification results having an accuracy of 90%, with the lowest Brier score of 0.1, and a false positive rate of 3% as compared to other models. Logistic regression classifier shows equally good performances with setback in brier_score and false positives. This study found that extra salt, tuibur, smoking and alcohol are the non_invasive etiological factors for gastric cancer in Mizoram population as predicted by the Naive Bayes algorithm. This knowledge will be helpful for initiating early screening and to educate the public about the risk of dietary and lifestyle factors in high risk population with unique habits.
Malaria Parasite Detection Using Deep Learning : (Beneficial to humankind)

Malaria is one of the deadliest diseases across the globe. This is caused by the bite of female Anopheles mosquito that transmits the Plasmodium parasites. Some current malaria detection techniques include manual microscopic examination and RDT. These approaches are vulnerable to human mistakes. Early detection of malaria can help in reducing the death rates across the globe. Deep Learning can emerge as a highly beneficial solution in the diagnosis of disease. This model gives a faster and cheaper method for detecting plasmodium parasites. The custom convolutional neural network is primarily designed to distinguish between healthy and infected blood samples. The proposed model consists of three convolutional layers and fully connected layers each. The neural network presented is a cascade of several convolutional layers having multiple filters present in layers, which yields the exceptionally good accuracy as per the available resources. The model is trained and later several blood sample images are fed to test the accuracy of the designed system. The CNN classifier has performed exceptionally well under limited computational resources giving an accuracy of 95%. Blood smear sample analysis can also aid in the detection of certain other illnesses and the application of deep learning models will help in the greater good of humankind.
SSA optimized 2DOF-PID Controller for Mitigation of Frequency and Tie-Line Power Deviations in a Two-Area Power System with Integration of EV

This paper aims at proposing the Salp Swarm Algorithm (SSA) optimized 2-degree of freedom proportional-integral-derivative (2-DOF PID) controller for mitigation of frequency and tie-line power deviations in a two-area power system integrated with electrical vehicle (EV). The proposed approach shows its superiority over the conventional PID controller in respect of performance measures like settling time, peak overshoot etc. under step and random load perturbations. Further, an investigation is also carried out to examine the impact of the integration of electric vehicle. Simulation results indicate the positive impact of EV on the frequency and tie-line power regulation.