bluuebunny/crossref_metadata_2025 huggingface dataset:
Missing shots: has the possibility of shooting wolves been lacking for 20 years in France
Authors: Meuret M. , Moulin C.-H. , Bonnet O. , Garde L. , Nozières-Petit M.-O. , Lescureux N.
Published: January 2021

Wolves were exterminated in France in the late 19th and early 20th centuries. Therefore, livestock breeders and herders were unprepared when wolves arrived from Italy in 1993, the year after France committed to the European Union (EU) to protect wolves. Today, ~580 wolves, whose numbers are growing exponentially, are present in over one-third of France. During the last 10 years, livestock deaths from wolves have grown linearly from 3215 in 2009 to 12451 in 2019, despite France implementing extensive damage protection measures since 2004, including reinforced human presence, livestock guard dogs, secured pasture fencing and electrified night pens. The failure to prevent damage is clear. Wolves enter mosaic landscapes where grazing livestock are abundant and easy prey. Wolves are intelligent and opportunistic. As a strictly protected species, it seems they no longer associate livestock with humans and humans with danger. Half of the successful attacks now occur during the day, notwithstanding the presence of dogs and humans. Considering the high costs of unsatisfactory protection, France recently modified its wolf management policy. In addition to non-lethal means of protection, breeders that have suffered several attacks by wolves are now permitted, by derogation to the law, to defensively shoot wolves. Based upon evidence from other countries, we suggest re-establishing a reciprocal relationship with wolves. Breeders and herders should be allowed to shoot wolves to defend their herds against wolf attacks, not after several successful predation events. Defence shooting would also upgrade the efficiency of non-lethal means, as warning signals for wolves to respect. Rather than passive coexistence, we need to embrace a dynamic and ever-evolving process of coadaptation between humans and wolves, relying on the adaptive capacities of both.
Self-organized biotectonics of termite nests
Authors: Heyde Alexander , Guo Lijie , Jost Christian , Theraulaz Guy , Mahadevan L.
Published: January 2021

Significance

Termite nests are a remarkable example of functional self-organization that show how structure and function emerge on multiple length and time scales in ecophysiology. To understand the process by which this arises, we document the labyrinthine architecture within the subterranean nests of the African termite
Apicotermes lamani
and develop a simple mathematical model that relies on the physical and biological interactions between termites, pheromones, and mud in the nest. Our model explains the formation of parallel floors connected by linear and helical ramps, consistent with observations of real nests. In describing this multiagent system, we elucidate principles of physical and behavioral coupling with relevance to swarm intelligence and architectural design.
Surface boulder banding indicates Martian debris-covered glaciers formed over multiple glaciations
Authors: Levy Joseph S. , Fassett Caleb I. , Holt John W. , Parsons Reid , Cipolli Will , Goudge Timothy A. , Tebolt Michelle , Kuentz Lily , Johnson Jessica , Ishraque Fairuz , Cvijanovich Bronson , Armstrong Ian
Published: January 2021

Significance

Significant debate exists whether the global population of Martian debris-covered glacier deposits formed continuously over the past 300 to 800 Ma, or whether they formed during punctuated episodes of ice accumulation during obliquity maxima (lasting ∼10–100 ka). We show that, like ancient debris-covered glaciers on Earth, boulder banding on Martian glacial deposits indicates multiple episodes of ice accumulation and advance. In our analysis, glacial periods are followed by ice removal from the glacier accumulation zone, forming debris bands. We report a median of five to six glacial/interglacial transitions recorded on Martian debris-covered glaciers, suggesting the cadence of glaciation on Mars is set by orbital forcing over tens to hundreds of Ma, not individual ∼120 ka obliquity cycles.
Experienced well-being rises with income, even above $75,000 per year
Authors: Killingsworth Matthew A.
Published: January 2021

Significance

Past research has found that experienced well-being does not increase above incomes of $75,000/y. This finding has been the focus of substantial attention from researchers and the general public, yet is based on a dataset with a measure of experienced well-being that may or may not be indicative of actual emotional experience (retrospective, dichotomous reports). Here, over one million real-time reports of experienced well-being from a large US sample show evidence that experienced well-being rises linearly with log income, with an equally steep slope above $80,000 as below it. This suggests that higher incomes may still have potential to improve people’s day-to-day well-being, rather than having already reached a plateau for many people in wealthy countries.
Dynamics of soliton and mixed lump-soliton waves to a generalized Bogoyavlensky-Konopelchenko equation
Authors: Ismael Hajar F , Ma Wen-Xiu , Bulut Hasan
Published: January 2021

We study dynamics of soliton waves, lump solutions and interaction solutions to a (2+1)-dimensional generalized Bogoyavlensky-Konopelchenko equation, which possesses a Hirota bilinear form. Multi-soliton solutions, one-M-lump solutions, and physical interactions between solitons and 1-M-lump solutions are presented. By using a positive quadratic function, lump solutions and their interaction solutions with kink and solitary waves are also generated. To show dynamical properties and physical behaviors of the resulting solutions, 3D-plots and contour plots at different times are made and analyzed.
From modeling of political opinion formation to two-spin statistical physics model
Authors: Sznajd Jozef
Published: January 2021

A two-spin system based on the idea used to formulate the dynamical model simulating the process of opinion spreading in a society, where people adopt one of the four social attitudes (2005 Physica A
351 593) is studied. The model is made of chains with two species of spins S and V at each site coupled by an insite interaction

M

̃

0

and interacting in different way with nearest (NN) and next-nearest (NNN) neighbors. The one-dimensional model with only NN interaction of the S spins and both NN and NNN interactions of the V spins (ACLS model) is analyzed. Using the linear renormalization group (LRG) the dependences of the specific heat and correlation functions on temperature and

M

̃

0

are calculated. The roles of the insite and NNN interactions are considered. The usefulness of the ACLS model to define stable states of the dynamical model describing opinion spreading in a society, which members represent four different attitudes to the level of freedom in two areas: personal and economic is discussed. It is pointed out that the difficulties in organizing modern societies can result from an internal conflict between these two areas.
Quantum phenomena in nanostructures
Authors: Dev B N
Published: January 2021

Quantum phenomena in nanostructures, especially epitaxial structures, are utilized for the fabrication of quantum devices. Here we discuss self organized growth of epitaxial quantum structures on silicon and their electronic structures, such as electronic density of states, and electronic transport in such systems. We discuss nanodots and nanowires, or an equivalent of zero-dimensional (0D) and one-dimensional (1D) structures in more details, and in general structures in all dimensional cross-over regimes. Further arrangements of quantum dots (QDs) in particular structures offers to form quantum bits and quantum dot molecules (QDMs). This could lead to exploration of new physics and new quantum devices.
Effect of sintering temperature on structural and magnetic properties of Ni0.6Zn0.4Fe2O4 ferrite: synthesized from nanocrystalline powders
Authors: Ali M A , Uddin M M , Khan M N I , Chowdhury F-U-Z , Saha D K , Hoque S M , Liba S I , Akhter S
Published: January 2021

The effect of sintering temperatures (Ts
) on the structural and magnetic properties of Ni0.6Zn0.4Fe2O4(NZFO) ferrites synthesized by conventional double sintering method has been reported. The samples are sintered at 1200, 1250 and 1300 °C. The X-ray diffraction (XRD) analysis reveals the formation of a single phase cubic spinel structure of the sample. The magnetic parameters such as saturation magnetization, Ms
; coercive field, Hc
; remanent magnetization, Mr
and Bohr magneton, µ
B
are determined and compared with reported values. The obtained values are found to be 71.94 emu/gm and 1.2 Oe for Ms
and Hc
, respectively at Ts
= 1300°C sample. Curie temperature (Tc
) for various Ts
has also been calculated. It is noteworthy that the sample with a very low Hc
could be used in transformer core and inductor applications.
A circular microstrip patch antenna to operate in dual band for wireless communications
Authors: Hossain Sheikh Dobir , Abdus Sobahan K M , Al-Amin Md
Published: January 2021

A dual band circular micro-strip patch antenna is designed and simulated to obtain electronic circuit miniaturization of an antenna in high speed wireless local area networks (IEEE 802.11a standard). The proposed antenna contains a substrate layer (FR-4 lossy) with a dielectric constant of 4.9 and there is a circular patch on the upper layer of the substrate. The coaxial probe feed is used to excite the desired antenna which reduces the spurious radiation and hence obtained good efficiency. Also the cavity model is used for larger bandwidth while maintaining the lower size of the antenna. An ‘E’ shaped slot is introduced in the radiating patch to obtain dual band resonance frequency with maximum current distribution on the surface. Finally the simulated results using Computer Simulation Technology (CST) microwave studio 2009 in this design are compared with manual computation results and are found to be suitable for WLAN applications.
Quantum magneto transport properties of nanostructure multi quantum wells short wave Infrared detectors
Authors: Benaadad Merieme , Nafidi Abdelhakim , Melkoud Samir , Barkissy Driss , Benchtaber Nassima
Published: January 2021

In this paper, we investigated the band structure and quantum magneto transport properties of the In0.53Ga0.47As(d1=100Å)/InP(d2=70Å) type I multi quantum wells (MQWs) at low temperature. These studies were based on the envelope function and effective mass formalisms. We calculated the effect of d1, d2, the band valence offset and temperature on the band gap and the cut-off wavelength of detection. The result of the computed density of states and the position of Fermi level indicate that this sample is quasi two-dimensional system with n-type conductivity. The calculated evolution of the cutoff wavelength with temperature predicts this MQWs can be used as a short-infrared detector. Furthermore, we interpreted theoretically the photoluminescence, the Shubnikov de Haas (SdH) and quantum Hall effects observed by Pusep et al.
A new unsupervised learning-based process for extraction of knowledge’s and improving anomalies detection
Authors: Lasbahani A. , Taoussi C.
Published: January 2021

Lately, the world confronts day by day the birth of typically big data in various fields such as health, telecom, electronic sales, communication, service provision, industry, e-learning and e-commerce. This emergence was a brick and a major reason behind the realization of immense changes in the processes of manipulation and processing of data as the system of storage, processing, analysis and visualization of data. From an application point of view, the manipulation of such data requires the implication of certain numbers of usage standards such as those related to security, credibility and optimization of data. However, it seems so difficult to ensure the security of massive and gigantic data coming from a variety of sources, and especially in real time, because there is an almost total absence of methodologies ensuring the processing and protection of a volume of data automatically. In fact, our work takes stock of a new process allowing the secure handling of big data. This is about getting more out of machine learning, security patterns and decision trees. More specifically, our approach aims to design machines able of securing large data in real time while making them learn from a mapping of security patterns (learning data). This training will allow machines to develop in terms of security, which will allow them to improve their intelligence related to the detection of anomalies caused in the data during the data exploitation operation.
Non-smooth classification model based on new smoothing technique
Authors: Lyaqini S. , Nachaoui M. , Quafafou M.
Published: January 2021

This work describes a framework for solving support vector machine with kernel (SVMK). Recently, it has been proved that the use of non-smooth loss function for supervised learning problem gives more efficient results [1]. This gives the idea of solving the SVMK problem based on hinge loss function. However, the hinge loss function is non-differentiable (we can’t use the standard optimization methods to minimize the empirical risk). To overcome this difficulty, a special smoothing technique for the hinge loss is proposed. Thus, the obtained smooth problem combined with Tikhonov regularization is solved using a stochastic gradient descent method. Finally, some numerical experiments on academic and real-life datasets are presented to show the efficiency of the proposed approach.
A new synergistic approach for crop discrimination in a semi-arid region using Sentinel-2 time series and the multiple combination of machine learning classifiers
Authors: Moumni A , Oujaoura M , Ezzahar J , Lahrouni A
Published: January 2021

Accurate monitoring of agricultural lands and crop types is a crucial tool for sustainable food production. Therefore, to provide reliable and updated crop maps, the improvement of satellite image classification approaches is essential. In this context, machine learning algorithms present a potential tool for efficient and effective classification of remotely sensed data. The main strengths of machine learning algorithms are the capacity to handle data of high dimensionality, and mapping classes characterized by strong complex dynamics. The main objective of this work was to develop a new synergistic approach for crop discrimination in the semi-arid region of Chichaoua province, located in the Marrakesh-Safi region, Morocco, using high spatio-temporal resolution imagery and a multiple combination of machine learning classifiers. This approach was developed based on 10m spatial resolution open access Sentinel-2 (S2) images and machine learning algorithms. The atmospherically corrected S2 images were accessed through the Theia Land Data Center. Reference dataset was collected from a field survey carried out during the 2018 agricultural season in order to train the classifiers. Artificial Neural Networks, Support Vector Machine, K-Nearest Neighbors, Bagged Trees, Naive Bayes, Discriminant Analysis and Decision Trees classifiers were trained over the study area and the accuracy metrics, mainly Overall Accuracy (OA) and Kappa coefficient (K), were assessed. The trained models were single classifiers to build the ensemble classifier system. The obtained results showed high OA and K values up to 96% and 0.95 respectively, achieved by the developed approach. Therefore, based on these results, the approach we developed using the combination of multiple classifiers has a significant impact on crop classification quality.
A new Approach of Documents Indexing Using subject modelling and Summarization
Authors: Ouadif L. , El Ayachi R. , Biniz M.
Published: January 2021

Document indexing is a field of research in Natural Language Processing (NLP) that has been rapidly evolving for 70 years. It is an operation that focuses on the synthetic representation of a document according to a model in order to facilitate their subsequent use. This work is concerned with document indexing. Two points are addressed. This work is concerned with document indexing, we are trying to accelerate the indexing process of large document datasets, two points are addressed. The first one concerns the development of a document indexing system using the system's operating process based on three phases namely pre-processing, weighting, and subject modelling. The second point concerns the proposal for a new system that integrates a new developed automatic summary subsystem, the goal of this point is to minimize indexing time.
Statistical analysis of the rocks mass using data mining technique: a study case in Morocco
Authors: Ghazdali O. , Guemouria A. , Tagma T. , Rziki S. , Moustadraf J.
Published: January 2021

Data mining is a very important element that has been developing and evolving for years in the analysis and study of the stability of the underground gallery. More advanced methods, essentially empirical and statistical analysis are used to describe the rock mass of the OUMJRANE mine, in order to determine adequate support to be implemented in its underground gallery.
Modeling Study of Red LED Spectral Characteristics
Authors: Peng Dongsheng , Liu Kailong
Published: January 2021

LED is widely used in various fields as a new style of light sources. Among the various characteristics of the LED, photoelectric characteristics are often the most intuitive and practical. This paper will discuss the photoelectric characteristics of red LED. A study on the relationship between the forward current and luminescence spectrum, peak wavelength, main wavelength, color purity, luminous flux, luminous efficiency is presented. The experimental results show that the spectrum, peak wavelength and main wavelength of the red LED will shift red with the increase of the forward current, and the color purity will remain at a high level during the period. In addition, the luminous flux and light intensity increase with the increase of forward current, the luminous flux from 0.5 lm to 3.5 lm, but the luminous efficiency decreases with the increase of forward current. from 40 lm/w to 32 lm/w.
Forecasting Framework Using Hybrid Modeling and Support Vector Regression
Authors: Xia Lingjuan , Liu Xueyong , Wang Lixian
Published: January 2021

Time series forecasting for financial market has increasingly attracted the interests of investors and academic researchers. In recent years, some hybrid models have been constructed to improve the predictions since some methods cannot extract useful information from stock time series with noise to conduct prediction. In this research, a prediction framework is proposed to forecast the stock market behavior using the methods of wavelet coherence, multiscale decomposition and support vector regression (SVR). First, a combined method is applied to the raw data and remove noise to get useful information. Then, a SVR model is applied to improve the prediction performance of multidimensional nonlinear data. Furthermore, the comparison experiments were performed with both Shanghai Composite Index and Dow Jones Index to examine the effectiveness of the framework. The results indicate that the proposed framework performs better than other advanced models.
Research on Classification Model based on Neighborhood Rough Set and Evidence Theory
Authors: Zhang Yuzhuo , Wang Yunqiong
Published: January 2021

The forward greedy numerical attribute reduction algorithm based on the neighborhood rough set is used to reduce the continuous numerical evaluation metrics of the health condition of complex equipment. This eliminates the risk of data loss and the additional processing time, due to avoiding discretization of continuous numerical evaluation metrics. Furthermore, the reduced evaluation decision table is processed to construct the basic probability assignment function (BBAS). Finally, multiple evaluation metrics are fused by Dempster’s rule of combination to get the health condition grade, and the relationship between evaluation metrics and health condition grade is mined further. The theoretical analysis and experimental results show that the proposed model is effective and efficient for classification.
Modeling and Simulation Research of Heavy-duty AGV Tracking Control System Based on Magnetic Navigation
Authors: Zhou Xinmin , Jin Lingli , Liu Yuyan
Published: January 2021

This paper studies tracking control problem which is applied on the 16-wheel heavy-duty AGV(Automated Guided Vehicle). Firstly, the trajectory detection scheme is designed based on magnetic navigation sensors. Secondly, the 16-wheel heavy-duty AGV control model is simplified as a two-wheel AGV model according to Ackermann steering principle, and fuzzy trajectory control regulation is established based on car driving experience. Finally, a trajectory control simulation system for circular/straight trajectories is built in MATLAB Simulink to verify the effectiveness on trajectory control.
Simulation and Design of DC Sensor Based on Tunnel Magnetoresistance
Authors: Chen Zechun , Shi Hong , Zhao Cong , Chen Yue
Published: January 2021

In order to measure the DC current measurement, a method of current measurement and interference error elimination based on tunnel magnetoresistance (TMR) DC sensor is established. Firstly, the basic principle of TMR sensor is described, and its open-loop transfer function is derived, and the parameter variables related to its open-loop gain are analyzed; an adaptive filtering algorithm based on least mean squares (LMS) is proposed to detect the magnetic induction intensity. Taking three magnetic sensors as an example, the magnetic field estimation value under this condition is calculated. Finally, the correctness and effectiveness of the design method are verified by simulation.
Dynamic Property Simulation Research of Hybrid Electric Vehicle based on ADVISOR
Authors: Zhang Li
Published: January 2021

Energy conservation and environmental protection are two major themes in today’s world. Simulation analysis can save a lot of experimental costs in the production of cars. To improve the dynamic property of hybrid electric vehicles, a vehicle simulation model and battery simulation model were established in the ADVISOR to match and select the engine and motor parameters, and the performance of hybrid electric vehicles such as maximum speed, 0-100km acceleration time and SOC change of battery system was simulated and analyzed. The results show that the improved hybrid performance can meet the requirements, the vehicle’s power performance and fuel economy was improved.
Research on Application of LSTM-QDN in Intelligent Air Combat Simulation
Authors: Hu Dongyuan , Zuo Jialiang , Zheng Wanze , Zhang Ze , Zhao Yu , Zhang Qiang
Published: January 2021

Aiming at the problem of the lack of intelligence of virtual machine opponents in the human-machine confrontation semi-physical simulation environment, it is proposed to apply the deep reinforcement learning method into tactical making-decision for building an AI virtual pilot with self-confrontation and self-learning ability. First, flight dynamics and kinematics are used to build basic flight models in the simulation environment, and a missile attack area is established for weapon model; Second, inspired by the framework of interaction between the agent and the environment in reinforcement learning, a tactical decision architecture for flight agent based on the one-to-one tactical confrontation process is organized. Finally, the improved DQN method is used to fit the value function in the continuous state space, and the network training is completed by means of agent self-antagonism and human-machine confrontation. the well-trained AI model can undertake the role of virtual opponents in human-machine confrontation environment, and shows a certain degree of intelligence in the confrontation process with pilots.
Analysis of the Impact of Typical Severe Thermal Hydraulic Condition on the Evolution of Droplet Characteristics in a Spray: the Numerical Simulation
Authors: Huang X. Q. , Zhang T. B. , Malet J.
Published: January 2021

Numerical simulation has been widely applied in the research of spray systems during a severe nuclear plant accident. In order to improve the predictive accuracy of the calculation, the impact of the thermal hydraulic conditions on the development of the spray have been examined by the researchers. The current study presents the influence of the initial droplet distribution at the injection point of a full cone spray. By comparing the velocity of the droplets of different sizes with the experimental results, a reasonable initial droplet size distribution is suggested. Discussions are also performed to explain the divergence between the experiments and the numerical simulation.
Design and Application of Distributed Computing Environment Based on Data Drive
Authors: Zhu Zhenguo , Bi Chao , Zheng Rong , Zheng Song
Published: January 2021

This paper proposes a distributed computing environment based on data-driven mechanism, which can be deployed on different levels of Industrial Internet, designs and implements an Industrial Internet architecture with high reusability and rapid deployment. This architecture has the consistency of the computing environment in the cloud and terminal control layer, which is beneficial to the realization of remote management and collaborative control of Industrial Internet. Data driven technology enables heterogeneous industrial equipment to achieve interconnection and interworking at a low cost. The multi-engine computing environment constructed by it is not only an effective method to solve the application design and development of complex systems, but also a key technology to realize edge computing and fog computing. This paper designed a corresponding test bed platform, test results show that the technology can not only provide connectivity of heterogeneous data source and efficient technical support, and to ensure that the distributed industrial equipment real-time and synchronization of data transmission, but also has the strong edge computing capabilities to cope with the constantly generated data stream, and reducing the pressure of the cloud server storage, computing.
Visualization Technology Framework of Industrial Cloud Computing
Authors: Lu Zhe , Chen Zhicheng , Zheng Rong , Liu Chaoru , Wu Xiaolin , Chen Songbin , Zhang Wang , Zheng Song
Published: January 2021

Industrial cloud computing is considered to be an essential technology for future industrial enterprises. Under the influence of Industry 4.0 and Industrial Internet concepts, industrial cloud computing related technologies are becoming a hot topic in recent research. How to observe and analyze the intermediate and final results of industrial cloud computing is the first problem to be solved in this research field. This paper proposes a new industrial PaaS (Platform-as-a-Service) architecture, which has a very important relationship with the visualization of industrial cloud computing, and it deploys a large number of structurally symmetric Data Engine computing units. Configuration and reusable methods are used in this paper to form a visualization technical framework and establish a data mapping relationship with the Data Engine, which significantly improves the efficiency of visualization application development. Finally, we validated the technology using an industrial internet test bed. The experimental results show that this visualization framework technology can provide industrial users with an advanced industrial application development environment, and effective realtime monitoring of industrial cloud computing through the internet.
Optimization Research on Abnormal Diagnosis of Transformer Voiceprint Recognition based on Improved Wasserstein GAN
Authors: Zhu Kejia , Ma Huiyuan , Wang Jianing , Yu Changshu , Guo Chunlin , Xiao Ce
Published: January 2021

Transformer is an important infrastructure equipment of power system, and fault monitoring is of great significance to its operation and maintenance, which has received wide attention and much research. However, the existing methods at home and abroad are based on model analysis for detection and diagnosis techniques, and their application is limited by the maturity and applicability of expert knowledge. In recent years, deep learning artificial intelligence has made important breakthroughs, however, the relevant research is still mainly focused on the field of speech, and intelligent diagnostic techniques on machine voice patterns have just begun. Therefore, this article adopts an unsupervised learning anomaly detection method based on WGAN-GP to diagnose the electrical and mechanical anomalies of transformer equipment. The experimental results show that the method can effectively identify transformer anomalies and provide an idea for artificial intelligence-based machine voice pattern recognition.
Optimization and Application of High Power Thulium-Doped Fiber Laser
Authors: Zhang Ning , Chen Ming , Sun Huijuan , Yang Yansong , Chen Tingting , Chen Xiaodan , Xiao Mingming , Chen Bingjun , Luo Yang
Published: January 2021

In recent years, with the continuous progress of laser technology, fiber laser has been developed rapidly. Compared with the traditional laser, fiber laser has the advantages of good beam quality, small volume, easy integration, high luminous efficiency, strong anti-interference ability and good heat dissipation. As a kind of fiber laser, thulium doped fiber laser not only inherits the above advantages, but also has the rich energy level structure and spectral characteristics of thulium ion, which determines that thulium doped fiber laser plays an important role in the research field of long wavelength fiber laser. This paper discusses the influence of the length and concentration of doped fiber on the output of high power laser system. For a given laser system, all parameters have an optimal value. In order to make the high power thulium doped fiber laser work in the best state, we need to carefully select these parameters. Based on the rate equation, the relationship between the dopant concentration and the length of the optical fiber is given in theory. The experimental results show that the optimal fiber length is different for different pump power. The higher the pump power, the larger the optimal fiber length. The experimental results have guiding significance for the manufacturing technology of high power fiber lasers.
Optimization and Transmit Power Control Based on Deep Learning with Inaccurate Channel Information in Underlay Cognitive Radio Network
Authors: Zong Xuekai , Zhu Xiaomei , Zhu Aichun , Byrne John Oliver
Published: January 2021

In cognitive radio network(CRN), power control often faces the complicated iterations and large calculations, resulting in poor real time performance of the system. In this paper, a deep learning-based power control is proposed for CRNs, where the secondary users (SUs) can share the same channel of primary users (PUs) without causing excessive interference to the communication of PU. In the novel scheme, the DNN model is used to treat the input and output of the power control algorithm as unknown non-linear mappings and fit them, which determines the proportion of transmit power allocated to each SU, considering the interference caused to the PU. With this scheme, the maximization of the SUs sum-rate can be achieved. Furthermore, due to some errors in the practical samples of channel information, an auto-encoder is used to compress the channel coefficient through an encoder and reconstruct them through a decoder before DNN training. The simulations results show that the power control method using a combination of auto-encoding and DNN can improve the real-time performance of the system. And the sum-rate of SU is improved while the interference caused to the PU can be regulated even with the inaccurate channel information.
Analysis of various health parameters for early and efficient prediction of sepsis
Authors: Parashar Abhishek , Mohan Yukti , Rathee Neeru
Published: January 2021

Sepsis is a major health issue causing mortality, morbidity and health care financial crisis to people around the globe. To resolve this issue, many researchers and clinical practitioners have worked hard to predict the onset of sepsis using various parameters of patients. The proposed work is an attempt of authors to analyse the various parameters (8 vital parameters, 26 laboratory or clinical parameters, and 6 demographic parameters regarding hospital stay) given in Physionet Challenge dataset so as to devise the best features for early and efficient prediction of sepsis. Authors have also addressed another important issue of missing values of some parameters of some patients by applying Gaussian Mixture Model to estimate the missing value in pre-processing steps. The pre-processed data is then fed to Extreme Gradient Boosting algorithm (XGBoost), which is a state of the art performer algorithm for prediction purposes in data analysis field. The experimental results show that by real time monitoring of data from cloud, sepsis can be predicted 6 hours prior to the onset of sepsis with an accuracy score of 0.994 and AUC score of 0.867. It is also observed that demographic parameters play a vital role in sepsis prediction. The Since the parameters used for early prediction can be easily acquired with the help of sensors, the proposed approach proves its potential for development of mobile and website applications for patient monitoring, real-time prediction of sepsis and generation of appropriate alert system.
Deep learning perspectives a review
Authors: Jithendra Uppu , Mittal Usha
Published: January 2021

Deep learning is a very successful field in the area of machine learning. This has made tremendous achievements in a variety of fields, such as voice recognition. Computer vision and natural language perception. Large data with today ‘s vast data give large possibilities and transformative opportunities to different industries and face unprecedented challenges in using information and data. As the figures rise, profound learning goes on. It is important to develop analytical tools for big data. This paper discusses some of the perspectives of profound learning.
Input Data Characterization Using Machine Learning and Deep Learning
Authors: Shukla Rati , Kumar Vinod , Yadav Vikash , Rahul Mayur
Published: January 2021

Seeing the current trends in Information technology, a large volume of heterogeneous data is produced widely across the world by means of social media sites such as Facebook, Instagram, Google plus, etc. and electronic gadgets used by humans for instance sensors. The generated data act as garbage and makes no sense until they are categorized. A strong need of data analytics or data filtering came into existence to filter the data into concrete categories. The generated data contain enormous features with huge dimensions. The high dimension data must be reduced to low dimension data to avoid curse of dimensionality and to build a better machine learning (ML) model.

A ML model is built to perform the classification task that result into labeling of the data. In the first phase of work, a framework has proposed to process the data having features belonging to text and images using ML and DL algorithms. In the second phase of work, we have shown data related behavior using ML and DL framework. The proposed work is generic in sense of FS techniques and FE techniques using ML classifiers and DL classifiers. After carefully implementing the algorithms on the datasets we have evaluated the results taking accuracy as our performance metric. After carefully analyzing the accuracy it can be concluded that DL algorithms give better results than ML algorithms on both text and image dataset.
Identifying and sensing emotional quotient weightage in the outcome using Speech Dialogue
Authors: Yadav Rajat , Mishra Anurag
Published: January 2021

Emotion is a very important part of any interaction whether it is human-human or human- computer interaction as an emotion conveys the essence of the interaction. Decision making is also influenced by the emotional state of a person. If we can capture emotions from a human-computer interaction then it will be very beneficial for the computer to learn from that interaction. It can be efficiently used by an artificial intelligence system to understand and give response accordingly. The method proposed in this paper uses a machine learning based approach to detect emotion from the text obtained from the speech dialogue.
A comparative study of classifier algorithms for Twitter’s sentiment based spam detection
Authors: Kumar Santosh , Kumar Ravi , Haider Mohammad , Dubey Anil
Published: January 2021

In today’s time Social media is a vital part of everybody’s life. People are expressing their feeling and emotions on social media platforms. Emotions are associated with the daily life experiences of everyone. Twitter is one of the mostly used social media platform. From analysis on social media, we can predict the mental status of users. In this paper a comparative study of user’s posts on Social media has been done. The approach is based on relevant keywords. Sentiment analysis and classification of emotions is done using Bayes Network Classifier, Naive Bayes Classifier, Logistic Regression, Simple Logistic, SMO, J48 pruned tree, and Random Forest. Finally, accuracy of classification is evaluated by different classifiers.
Deep Positional Attention-based Bidirectional RNN with 3D Convolutional Video Descriptors for Human Action Recognition
Authors: Srilakshmi N , Radha N
Published: January 2021

This article presents the Joints and Trajectory-pooled 3D-Deep Positional Attention-based Bidirectional Recurrent convolutional Descriptors (JTPADBRD) for recognizing the human activities from video sequences. At first, the video is partitioned into clips and these clips are given as input of a two-stream Convolutional 3D (C3D) network in which the attention stream is used for extracting the body joints locations and the feature stream is used for extracting the trajectory points including spatiotemporal features. Then, the extracted features of each clip is needed to aggregate for creating the video descriptor. Therefore, the pooled feature vectors in all the clips within the video sequence are aggregated to a video descriptor. This aggregation is performed by using the PABRNN that concatenates all the pooled feature vectors related to the body joints and trajectory points in a single frame. Thus, the convolutional feature vector representations of all the clips belonging to one video sequence are aggregated to be a descriptor of the video using Recurrent Neural Network (RNN)-based pooling. Besides, these two streams are multiplied with the bilinear product and end-to-end trainable via class labels. Further, the activations of fully connected layers and their spatiotemporal variances are aggregated to create the final video descriptor. Then, these video descriptors are given to the Support Vector Machine (SVM) for recognizing the human behaviors in videos. At last, the experimental outcomes exhibit the considerable improvement in Recognition Accuracy (RA) of the JTDPABRD is approximately 99.4% achieved on the Penn Action dataset as compared to the existing methods.
Approaches of Artificial Intelligence and Machine Learning in Smart Cities: Critical Review
Authors: Varshney Harshit , Khan Rizwan A. , Khan Uzair , Verma Rajat
Published: January 2021

Smart cities are aiming to develop a management system for growing urban cities, improve the economy, energy consumption, and living standards of their citizens. Information and communication technology (ICT) has a much more important place in decision making, policy design, and implementation of modern techniques to develop smart cities. This review aims primarily to investigate the role of artificial intelligence (AI) and machine learning (ML) in the development of smart cities. This survey leads to the systematic interpretation of current patterns in ICT-related information flow publications as well as to the identification of the usual technologies used to facilitate this communication. In this paper, we represent the detailed presentation of AI & ML in the intelligent transport system and the prediction of mix design and mechanical properties of concrete.
E-Rickshaws Management for Small Scale Farmers using Big Data-Apache Spark
Authors: Tyagi Nipun , Khan Rijwan , Chauhan Nikita , Singhal Ayushi , Ojha Jaya
Published: January 2021

E-Rickshaw provides environment friendly, in-expensive, time-effective, suitable mode of transportation and plays a vital role in transportation in cities. E-Rickshaw lacks regularization, frequency and management, which increases the burden on the E-Rickshaw management system. Paper aims at developing a real-time RFID based E-Rickshaw Management System for small scale farmers, which focuses on scheduling the e-rickshaw according to passenger demand in a real time environment. The main focus of this system is to evolve user friendly, flexible and convenient E-Rickshaw management system for small scale farmers that would cater the needs of vegetables and fruits and reduce the traffic congestion in the city. This Real Time-Based System is helpful, precise, secure and flexible. The system will use Global Positioning System (GPS) and Radio Frequency Identification Tags (RFID) to get the location of E-Rickshaw and information about passengers and drivers. The system will be working on Real Time with a merged function of RFID and GPS which will sustain the detailed information about farmers and E-Rickshaw.
Microsatellite Instability in Gastrointestinal Cancer Using Deep Learning: A Review
Authors: Dahiya Neelam , Gupta Sheifali , Garg Meenu
Published: January 2021

Currently, the health management is a foremost concern all over the world. Particularly in India, due to lack of physical activity, overweight, stress and unhealthy eating habits, various diseases like heart disease, diabetes and cancer disease are increasing at a very rapid rate. The common problem which is affecting all age generation is a gastrointestinal disorder which includes various conditions like colitis, colon polyps, perianal infection and cancer. Microsatellite Instability (MSI) in gastrointestinal (GI) cancer is the second leading cause of death worldwide. However, this cancer can frequently be asymptomatic during the early stages and stay undetected until the later stages of tumor advancement. Deep Learning has the capacity to detect MSI in gastrointestinal cancer at an early stage, which is very helpful for the patients. There are number of research papers, focusing on different techniques used for detection of MSI in gastrointestinal caner. In this paper, we focus on the recent trends of deep learning methods in this field. The main goal of this review paper is to provide a detailed discussion from technological perspective. This article discusses various challenges, molecular subtypes, pathologist approach and computer aided approach for gastrointestinal cancer detection. Also, this article brings into light the existing literature and state of art with their contribution in different aspects of detection of microsatellite instability in gastrointestinal tract with the help of deep learning techniques.
Autonomous cars: Recent developments, challenges, and possible solutions
Authors: Singh Sehajbir , Saini Baljit Singh
Published: January 2021

The development of production automobiles involved vehicles that were propelled by internal combustion engines. Improvements in the internal combustion technology continue to take place even today. However, electric vehicles are slowly making way into the bigger picture. Development in autonomous vehicle technology is also gathering pace. Although there has been remarkable progress in this domain, much needs to be accomplished. Adoption of autonomous vehicle technology has multiple benefits. Autonomous car companies have spent large amount of resources on the development of autonomous vehicle technology, with an aim to fully commercialize the technology. Several issues cause hindrance to the achievement of this goal. These issues comprise technical, non-technical and legal challenges. The future of the technology is assuring and ambitious, however, the challenges must be overcome.
A Review on Artificial Intelligence Techniques for Disease Recognition in Plants
Authors: Singh Taranjeet , Kumar Krishna , Bedi SS
Published: January 2021

Disease detection in crops is one of major task that every farmer practice and takes necessary action for eradicating them as they are harmful to not only crops but also to farmers, consumers, and environment too. Quality and safety of agricultural products is one of major concern in today’s scenario. In earlier times farmers consults experts or use their own experience for identification of diseases in their crops but now days intelligent techniques are slowly replacing the monitoring of crops as they are more reliable, accurate, fast and economical in comparison to earlier techniques. This paper discusses few techniques based on machine learning and image processing that were presented by researchers all over the world for recognition of diseases in crops, later discussions are presented that can be helpful for improvements in this domain. This study would help other researchers and practitioners to survey various techniques used for the process of disease detection in plants and limitations of current systems.
A Survey on Network Intrusion System Attacks Classification Using Machine Learning Techniques
Authors: Deepa V. , Radha N.
Published: January 2021

Wireless Local Area Network (WLAN) security management is now being confronted by rapid expansion in wireless network errors, flaws and assaults. In recent times, as computers are used extensively through network and application creation on numerous platforms, attention is provided to network security. This definition includes security vulnerabilities in both complicated and costly operating programs. Intrusion is also seen as a method of breaching security, completeness and availability. Intrusion Detection System (IDS) is an essential method for the identification of network security vulnerabilities and abnormalities. A variety of significant work has been carried out on intrusion detection technologies often seen as premature not as a complete method for countering intrusion. It has also become a most challenging and priority tasks for security experts and network administrators. Hence, it cannot be replaced by more secure systems. Data mining used for IDS can effectively identify intrusion and the identified intrusion values are used to predict further intrusion in future. This paper presents a detailed review of literature about how data mining techniques were utilized for intrusion detection. First, intrusion detection on various benchmark and real-time datasets by data mining techniques are studied in detail. Then, comparative study is conducted with their merits and demerits for identifying the challenges in those techniques and then this paper is concluded with suggestions of solutions for enhancing the efficiency of intrusion detection in the network.
Exploring adversarial attacks against malware classifiers in the backdoor poisoning attack
Authors: Reddy Gopaldinne Sanjeev , Lakshmi Sripada Manasa
Published: January 2021

Machine learning is becoming the latest trend by making growing use of it in various fields of application. Along with that, the invasions designed to evade these systems has also evolved apparently. The adversarial attacks are becoming the major threats for developing threats to these models. Current training pipelines for the classification of malware based on machine learning (ML) depend on crowdsourced threat feeds which expose a normal point of injection. We research the vulnerability of ML malware classifications to backdoor poisoning attack for the first time, concentrating explicitly on stimulating “clean label” attacks where attackers donate monitor the mechanism of sample classification. In this paper, we reviewed various attacks based on machine learning models and their working strategies. We also discussed the threat models and backdoor attacks on malware classifiers.