bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Positional Attention-based Bidirectional RNN with 3D Convolutional Video Descriptors for Human Action Recognition
Authors: Srilakshmi N , Radha N 
Published: January 2021

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
Knowledge graph augmented advanced learning models for commonsense reasoning
Authors: Pothuri Akhilesh , Raghuram Veeramallu Hari Sai , Malik Pooja 
Published: January 2021

Abstract
Machine learning is the key solution to many AI issues, but learning models rely heavily on specific training data. While a Bayesian setup can be used to incorporate some learning patterns with previous knowledge, those patterns can not access any organized world knowledge on requirements. The primary objective is to enable human-capable machines in ordinary everyday circumstances to estimate and make presumptions. In this paper we propose to respond to such common sense issues through a textual inference system with external, organized common sense graphs for explanatory inferences. The framework is based on a schematic map as a pair of questions and answers, a linked subgraph from the semantine to the symbolic space of knowledge-based external information. It displays a schematic map with a new network graphic module for information knowledge and performance with graph representations. LSTMs and graphical networks with a hierarchical attention-based direction are the basis of our model. It is flexible and understandable from the intermediate attention scores, leading to confident results. We also achieved state-of-the-art reliability on CommonsenseQA, a broad database of common sense reasoning utilizing ConceptNet as the only external tool for BERT-based models.
Brief Study to Explore Trust and Security Challenges in Cloud Computing
Authors: Singh Amanpreet , Kaur Amandeep , Gupta Deepali 
Published: January 2021

Abstract
Cloud Computing is an online strategy for dynamic allocation of services by reducing the usage of hardware and software resources. Existing models of cloud computing include different components: end-user computers, contact networks, access control frameworks, and software infrastructures. The data and the cloud infrastructure must be protected from any known and unknown attacks on all cloud platforms to achieve comprehensive cloud security. Trust issues arise from uncertainty about certain actions that can be defined through the concept of fuzzy belief. Trust is faith in quality provided by the cloud service provider and third-party agent on consumers and vice-versa where quality can be considered in terms of functionality, security, privacy, data safety, and reliability on cloud resources. Although the members of a social network have a strong level of trust, this trust may not be adequate in certain cases and becomes an obstacle in the successful implementation of cloud services. For example, a storage facility where users are vulnerable to missing, infected, or corrupted data, while providers are at risk of unknown data being hosted from their network. The relationship between two business partners decays more rapidly relative to the trust of users in the social network of the cloud. This paper aims to address the trust issues in existing cloud paradigms and highlights the existing flaws which will be resolved in the future course of action.
Image Retrieval System-An Integrated Approach
Authors: Chugh Himani , Gupta Sheifali , Garg Meenu 
Published: January 2021

Abstract
With the development of multimedia technology, the usage of large image database becomes possible and is rapidly increasing. These can be used for the purpose of retrieving files, which works on the mechanism of image search. Different databases are available on different websites like Instagram, Facebook, Twitter, Flickr, and Picasa. This paper shows the advantage of content-based image retrieval system, as well as its key technologies. Comparing to the shortcoming, only certain feature are used in the traditional system. This paper presents a review on different techniques of image retrieval techniques which are based on color, texture and shape of images. It also focuses on the feature extraction and representation, several commonly used algorithms and different methods used for matching of images.
Ensemble classifier to support decisions on soil classification
Authors: Motia Sanjay , Reddy SRN 
Published: January 2021

Abstract
Soil performs a significant role in the agricultural ecosystem by supplying essential nutrients and a conducive environment for plants’ growth and crop yield. Inside the agribusiness space, the soil classification is a crucial work that gives good classification results for different soil types. The taxonomy provides an excellent rating for inherent soil elements. This work investigates the accuracy of three well-known classification models like K-Nearest Neighbor (k-NN), Naive Bayes (NB) and, Decision Tree (DT) using a publically available agricultural soil dataset. Post investigation, an Ensemble Classifier (EC) is proposed by fusing the above mentioned three classifiers. The experimental results indicate that EC has the highest accuracy of 84% in comparison to the NB (72.90%), k-NN (73.56%), and DT (80.84%). So it performs better than the other classifiers. The results infer that EC would be useful for accurate classification of soil types in the agricultural domain.
Comparative Study Of Efficient Data Hiding Techniques
Authors: kumar Lalan , Mishra Deepanjali , Kumar Ayush , Kumar Amit , Bajpai Ashish 
Published: January 2021

Abstract
Data security is one of the fundamental concerns when it comes to modern networking and information sharing as any breach or data loss can end up causing major losses in terms of credibility, personal data and/or law suites. This paper studies data encryption and decryption techniques using algorithms like DES algorithm, 3 DES algorithm, AES algorithm and RSA algorithm. Cryptography was introduced for data security while transmitting data. However, only cryptography cannot give a robust security approach; as a result, a disorganized message can still be snooped [9]. This leads to a desire for information security. There are several techniques available, among those have taken a few of them and will review them considering cryptography measures. We will analyse the algorithm mentioned above, and try to find out better among them.
Secure IoT via Blockchain
Authors: Garg Ruchi , Gupta Poonam , Kaur Amandeep 
Published: January 2021

Abstract
Block chain technology had gained popularity because of its use in crypto-currencies like bitcoin. Today uses of blockchain are growing in number of areas like banking, industries, health centers and even security of IOT. Moreover, the use of IOT is growing exponentially every year with its aim in 5G technologies like e- health, smart homes, distributed intelligence etc. but it faces challenges in security and privacy. The privacy of a user data is at a risk because of its (i.e. IoT) centralized client-server model. This centralized approach of the server poses a serious vulnerability to the data security. This data at the server attracts the attackers to enter into the network and invade through the data and schedule attacks or inject a malware. It indicates that the central architecture of IoT possess a compromised confidentiality, integrity, and security of data which disrupts its use as the widespread adoption of this technology. Therefore, it is essential to evade the hostile centralized server architecture for IoT to enhance its security. It implies a need for decentralized architecture to maintain the data. The data can be kept at the different users without any central control with the help of blocks as suggested by Blockchain. This paper addresses the various security issues in IOT and how block chain helps in solving these issues.
Analysis and visualization of the pandemics using Artificial Intelligence
Authors: Srivastava Shagun , Prithivi P P R , Srija K , Vaishnavi P. , Savitha H S S , Grover Alka , Saxena Mohit , Chandra Shivani , Saxena Ankur 
Published: January 2021

Abstract
Human race has faced many epidemics and pandemics in past. The trajectory reveals that there is a pandemic almost every century. Our generation has witnessed the outbreak of coronavirus disease (COVID-19) pandemic, which turned out to be largest pandemic ever. Viruses have affected global population in the past century can answer the questions of the post Corona trauma. It has been noticed that most of the epidemics and pandemics are inflicted by the influenza viruses. Scientists have elucidated the structures of the viruses that have infected earlier. The homology between them and COVID-19 can be studied in order to fight the virus. In this paper, journey of novel corona virus has been discussed from a simple flu to become a pandemic within a short span of time. All the factors including the major countries responsible for spreading the pandemic owing to their volume, speed of human travel and socio-economic reasons seems to be the reason according to the review. We also compared the structures of the epidemic and pandemic causing viruses. Some of the COVID-19 studies mentioned which used machine learning models, and then by the use of machine learning we analyzed characteristics of three diseases (H1N1, COVID-19, and SARS) to obtain the plots of the autocorrelation against the lag of the number of confirmed cases, recovered cases, and deaths of/caused by COVID-19.
Energy Aware Metaheuristic Approaches to Virtual Machine Migration in Cloud Computing
Authors: Gupta Gifty , Mangla Neeraj 
Published: January 2021

Abstract
In mandate to drop the power utilization, virtual machine migration is utilized for cloud computing. At present many algorithms are there, that diminishing the energy consumption with the assistance of virtual machine migration and power proficient planning of VMs. The target to migrate the virtual machine from one under-burden server to other over-burden server is to have ideal outcomes. Over-burden or Under-burden host detection strategy utilized as a part of proposed work is Inter Quartile Range. After detection of an Over-burden or Under-burden host, the following stage is to pick the specific virtual machines to relocate from one under-burden host to the over-burden host. VM choice arrangements Minimum Migration Time (MMT) along with various Meta heuristic optimizations are executed. For assessing proposed algorithm, CloudSim is used to assess and look at the execution of proposed algorithm. To analyze the effectiveness of planned system seven parameters the power utilization, standard deviation of host, standard deviation of virtual machine placement, standard deviation of virtual machine migration, SLA performance degradation due to migration, and total SLA violation are considered.
IoT Based an Eye-Ware to Assist in Ocular Communication
Authors: Verma Vishal , Gupta Deepali , Gupta Sheifali , Chauhan Harsha 
Published: January 2021

Abstract
This paper is completely focused on the difficulties faced by the paralyzed patients and the today technology help these patients to relief them. Currently the physical therapy, occupational therapy, respiratory therapy and the speech therapy are used to treat the patient who is suffers from paralysis. There is need of the device with this the paralyzed person convey his feelings like pain, hunger and loo. In this paper author purposed an electronic googles that can help the paralyzed person to convey his feelings using the eye blink pattern. The infrared sensor (IR) pair are used to detect the eye blinking pattern. When the IR rays strike to pupil it, large intensity of IR rays is reflected back. When the eye lashes are closed the less amount of IR radiation is reflected back. Two pair of infrared sensors are installed on the both glasses of the google. The blink pattern is pre-programmed in the microcontroller. ESP8266 microcontroller is used to process the all data coming from IR sensor. When the blink pattern is detected the message is sent to the care taker through internet connection.
NER based Biomedical Entities Association Extraction Using Transfer Learning Technique
Authors: Kumar Ashutosh , Sharaff Aakanksha 
Published: January 2021

AbstractDeciphering the interaction of gene-disease is a key step in the development of therapeutic disease control strategies. The identification of the gene-disease associations by experimental methods, like genome-wide association studies and linking analyses, may be costly and time-consuming. Consequently, various silico methods were developed with different approaches to predict associations between gene and disease. A deep neural network based on attention mechanism has been proposed for the prediction of semantic linkage between biomedical terms. The proposed method uses a paradigm based on transfer learning, which enhances the prediction performance considerably. The main key feature of the proposed approach is, it is not dependent on any handcrafted features on sentence information. Sentence information is converted into embedding vectors. Extensive evaluations show that the state-of-the-art domain of the proposed approach achieves competitive performance, although no domains specific biomedical features are needed. The proposed methodology for the gene-disease association based on the deep learning approach, shows promising results of 78.10% of F-score on Genetic Association Databases (GAD) corpora.