bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transformation of Mobility Industry by Advanced Digital Technologies
Authors: Akash  , Kulkarni Prasanna 
Published: July 2021

Abstract
The world of mobility has always been a vanguard of scientific discovery. Artificial intelligence (AI) has already been used in motorsports, making the car more reliable and advanced. The digital transformation using AI and Machine learning in the automotive industry is fast, creating huge monetization opportunities. Autonomous cars, sophisticated self-control, safe, secure, and high-performance vehicles, and even flying cars may be the future of mobility. This research paper discusses how AI and Robotics will transform the mobility sector. It discusses the use of AI and Machine Learning (ML)in driverless cars. It also studies AI and Robotics’ convergence, creating a platform and ecosystems for human–robot partnerships, making humans free for more critical tasks. The digital technology disruption of the mobile industry is not free from challenges. The paper also discusses challenges, possible solutions, and how the transformation can benefit humanity.
Hybrid algorithm in identifying the plant disease and to suggest medical advice
Authors: Roopa D , Murukesh C , Rayavel P , Renukadevi B , Eswaran Malathi 
Published: July 2021

Abstract
Agriculture sector is the most important which is the reason for the life of every human being and every living objects in this world. The top tier problem of all formers in agriculture is to recognize the disease affected in a particular crop and their causes. Some disease spread easily and quick that may lead to disaster in the targeted production of that particular period. To overcome this problem, it would be best to recognize the diseases affected at initial stage. There are many systems exist for recognize the disease but these systems are concentrate only on the particular parts of the plant such as leaf, steam etc, and more over they did research using artificial neural networks (ANN) and some of the paper using support vector machines algorithm (SVMs). By using these algorithm, they can only detect whether it is affected or not and also they can concentrate only on the particular parts of the plant such as leafs. But this is not efficient. To overcome this demerit in our proposal we are aimed to concentrate on the entire plant for this purpose we are using the combination of following algorithms recurrent neural networks (RNNs), support vector machines (SVMs) and random forest algorithm. By using these algorithm, we can design a system which is more efficiently identify the plant’s diseases compare to the previous systems which is based on the image(plant) provided by the end user after identification process we provide complete information about the plant’s current condition and what type of pesticide should be used and how long it to used. In our system we are also plan to provide periodic intimation to the end user for the image of the affected plant for the purpose of tracking the diseases condition and provide complete agri assistant until the plant completely recovered from the disease. Our proposal mainly concentrates on the formers who are all not having sufficient knowledge about the plant disease, this system will save money and time of the formers.
Medical Quora Tagging using MATAR and LDA Algorithm
Authors: Elangovan G , Umamageswaran J , Indumathi G , Kalpana A V 
Published: July 2021

Abstract
The success of clustering or classification methods the detection of relevant textual formats is incredibly meaningful. The high dimensionality and irrelevance of textual materials was subjected to text records. Existing methods lack integration and are particularly vulnerable to original value. Metaheuristic algorithms are also applied to solve the challenges of standard classification algorithms. In this paper, an enhanced Latent Dirichlet Assignment clustering method & Inter Modeling for Tag Suggestion rating system is documented to boost correlation - based & identification efficiency to suggest labels with material modern web labels that promotes the exchange of medical information using unmonitored data through question-answering. For accurate tagging, Methods like POS marking, Hopping, Whistles& Stopping words are being used for speech recognition. The efficiency of the evolved architectures is compared to the standard methods, by using specificity of the recommendation, defining features, sensitivity, plain word and speed. The findings reveal that the classification and grouping scheme of the proposed structure succeeds traditional textual record approaches.
Design of Intelligent Control and Monitoring System for Agriculture Based on Renewable Energy and IoT
Authors: Ramasubramanian M , Rathish babu TKS , Anantha Krishna V , Syed Khasim 
Published: July 2021

Abstract
As the population rises, agriculture is becoming a big growth field worldwide. Improving production and farm efficiency without constant manual supervision to satisfy the rising demand for food is a major challenge in agriculture. Climate change is also a significant issue for the agriculture industry, aside from the growing population. The research aims to provide the Internet of Things (IoT) with an intelligent agriculture system. Intelligent farming that provides high precision crop management, helpful data collection and automatic farming techniques can be used. This work provides an intelligent framework to track ground moisture and temperature in agriculture. Without human interference, appropriate action is taken after analyzing sensed data dependent on these principles. Temperature & relative humidity are assessed and processed for further data analyses in the Thing Speak Database.
A Completely Distributed Blockchain Period Authentication Framework
Authors: Kavitha V , Geetha D , Karunkuzhali D , Manikandan G 
Published: July 2021

Abstract
The time capsule that would be opened in the future without third-party intervention was always a difficult issue. Although many researchers work on various systems, there are potential limitations, such as unreliable decryption period not entirely decentralised, which are difficult to estimate the needed data resources. In this post, we introduced a protocol and a safe cryptographic way to open a timely message in an advanced, decentralised environment to match in with several computing power conditions. The methodology also allows participants to gain extensive benefits of adding their computing resources, making our system more suited for applications in real life.
Study of the Usability Testing of E-Commerce Applications
Authors: Jain Samkit , Purandare Pradnya 
Published: July 2021

Abstract
Usability Testing determines the friendliness of the application to meet customers’ requirements. It must be adopted in the early stage of SDLC (Software Development Life Cycle) to avoid issues in a later stage. Today, E-commerce has revolutionized the mode of shopping, and thus, we need to focus on ways to improve the usability of e-commerce applications. We opted for survey questionnaires as our research methodology, along with testing websites using online tools based on the four parameters Connectivity, Readability, Accessibility, and Functional Performance. In the survey, the four most popular e-commerce applications were Amazon, Flipkart, Bigbasket, and Paytm. Out of which, Amazon was the most used e-commerce application by the consumers. Although, testing tools are depicted that applications need to improve in certain areas. From the analysis, we concluded that consumers are aware of the upcoming Jio-Mart, which can give tough competition to existing companies. Thus, all the existing e-commerce companies need to improve their web and mobile applications to retain their customer base.
A study of the cloud security attacks and threats
Authors: Sureshkumar V , Baranidharan B 
Published: July 2021

Abstract
Cloud storage has become an essential computational model today, providing flexible, defective resistant resources and dramatically reducing costs. However, there are major obstacles to protection issues in the wider adoption market. The multiformity of the cloud and the multi-location storing of data exacerbate these problems. The major issues to be discussed in protection are secrecy, authenticity, honesty, availability and auditability. Enabling more customers to switch to the cloud environment would allow for safe data and transfers from the service provider. Intrusion Detection Mechanisms, Cryptographic Methodology and Electronic Forensic Software are some of the assurance that a trustful service provider will have for the recuperation and collection of digital proof of intrusion activities. This paper provides a survey into some of the prevalent challenges, risks and ways to resolve these challenges in cloud platforms. We also discussed major cloud storage service for data storage and protection model.
Blockchain Confidence Protection and Cloud Chain Management Support
Authors: Nagalakshmi M , Saravanan K , Jabirullah Mohammad , Rajesh Kumar T 
Published: July 2021

Abstract
Verification and completeness are main challenges for today’s ever more diverse supply chains. Even with the ability to counter blockchain technologies by offering a trail of manipulation-resistant audit It does not address the confidence issue associated with the source chain activities also information related to a produce life cycle Information itself. Reputation mechanisms are a promising solution for this faith problem. Yet existing structures of credibility are Not ideal for supply chain applications based on blockchain as centred on restricted findings, lack of granularity and their overhead was not discussed and automation. We recommend the system as a three-layer faith in this job. Management platform is using a blockchain consortium tracking relationships between actors in the supply chain and Assign trust and prestige dynamically dependent on these interactions. Its novelty is based on a Model for credibility assessing product quality and the trust of individuals based on many observations funding for credibility qualities in supply chain incidents separating the member in the supply chain from the goods, enables brand credibility to be reserved for smart contracts for straightforward use by the same participant, Effective, secure, and automatic credibility scoring measurement, and the latency and throughput minimum overhead as compared to a straightforward supply chain model based on blockchain.
Artificial Intelligence in Cyber Security
Authors: Das Rammanohar , Sandhane Raghav 
Published: July 2021

Abstract
Without substantial automation, individuals cannot manage the complexity of operations and the scale of information to be utilized to secure cyberspace. Nonetheless, technology and software with traditional fixed implementations are difficult to build (hardwired decision-making logic) in order to successfully safeguard against security threats. This condition can be dealt with using machine simplicity and learning methods in AI. This paper provides a concise overview of AI implementations of various cybersecurity using artificial technologies and evaluates the prospects for expanding the cybersecurity capabilities by enhancing the defence mechanism. We may infer that valuable applications already exist after the review of current artificial intelligence software on cybersecurity. First of all, they are used to protect the periphery and many other cybersecurity areas with neural networks. On the other hand, it was clear that certain cybersecurity problems would only be overcome efficiently if artificial intelligence approaches are deployed. In strategic decision making, for example, comprehensive information is important, and logical decision assistance is one of the still unanswered cybersecurity issues.
Movie recommendation system using clustering mining with Python
Authors: Sadhasivam Jayakumar , Cera Juan Manuel , Deepa R , Satheshkumar K , Muthukumaran V , Satheesh Kumar S , Angeline Kavitha M 
Published: July 2021

Abstract
The research we are pursuing here is about building a system which we will use and mine a movie review database using concepts of statistics like Euclidean Distance algorithm. This will help us determine similar review according to the ratings. What we plan on achieving through this approach is to analyze the reviews to suggest a suitable critic according to one’s preferences and one’s taste. We will do this according to the review we select and our algorithm which we use upon it. This algorithm will be the key to find those similar reviews. We are going to use Python programming to program this experiment. We have chosen to pursue this because of not the reviewers being incorrect or correct but merely different according to their preferences and their lean towards a certain genre and we know that a reviewer should also match the taste of the person who want to feel benefited of the review itself. So, we plan to make system that could help with that.
Assessment of the Effects of Blockchain Based Protection in Network Performance
Authors: Murali Mohan Babu Y , Radhika K , Saravanan T R , Periasamy J K 
Published: July 2021

Abstract
Blockchain is popular as the distributed directory under Blockchain, which safeguards the transaction history in a completely linked network of peers. For double-dollar secrecy, a Bitcoin transaction is only called irrevocable after it’s on the blockchain, a chain comprising at least six mined blocks — shortly, “six confirmations.” In addition to the usage of blockchain technologies for securing funding, the Internet of Things and the ad hoc vehicle network are now paired with other new technologies (VANET). Nevertheless, these modern apps could have relatively poor Internet access because of the integration of handheld devices that may be placed outside network networks. This paper discusses the influence of latency on forking actions by blockchain and potential breaches of the above six transaction acceptance confirmations convention. We simplify the data structure of blockchain and prevent detailed calculations needed for proof of operating to speed up our simulations (Pow). By simulating, we prove that the convention of six confirmations is vulnerable to the latency of the peer-to-peer system besides also illustrate how easy it is disrupted by simpler mine-mining problems. It is not shocking that the lateness of the underlying peer-to-peer network has seriously impacted the tempo at which all nodes converge in the blockchain. It is also seen to the degree nodes with more regular Internet access will benefit unfairly from proof-of-work mining benefits. We therefore propose to track fairness with heterogeneous latency profiles across nodes, which can prohibit any nodes from being miners.
Some aspects of weak directoids and ortho complemented weak directoids
Authors: Sundarayya P , Ravi Kishore T 
Published: July 2021

Abstract
Directoids are introduced by Ivan Chajda.In this paper, a weak directoid is introduced. Some equivalent conditions to a weak directoid to be directoid are derived. Some properties of congruences on the commutative directoid and ortho complemented week directoids are derived.
A Data Centre Configurable Data Mining Document Management Information System
Authors: Gurusubramani S , Mouleeswaran S K , Srinivas Porandla , Aruna R 
Published: July 2021

Abstract
Data extraction is often a dynamic process that can be easily modelled as a workflow for data processing. When massive collections of data have to be evaluated and/or sophisticated data mining algorithms have to be performed, it can take very long to execute data analysis workflows. Effective technologies are also needed to incorporate flexible data collection workflows through the use of cloud-based storage platforms, where data is stored even more regularly. The paper attempts to show how cloud infrastructure is implemented to introduce an optimised framework in which scalable data analyzation workflows can be planned and performed. We explain how the Data Mining Cloud Architecture is built and applied and a data analytics method that incorporates visual workflow vocabulary, parallel to the Virtualized environment. DMCF is developed with a view to simplifying the creation of applications for data mining associated with generic system monitoring schemes that are not created especially for this area, in view of the specifications of actual data mining applications. The effects are a high-level environment that minimises the programming effort with an optimised visual workflow language, allowing the implementation of typical patterns meant to generate and execute data mining application in parallel simple to professional developers. The wall mounted of the workflow, device design and mechanisms of the DMCF are shown. We also address many DMCF-developed data mining business processes and the scalability achieved by running business processes in a cloud environment.
Random Forest-Based Method for Micro Grid System in Energy Consumption Prediction
Authors: Raj Murugananth Gopal , Pradip C , Saju N , Tresa Sangeetha S V 
Published: July 2021

Abstract
As a solution to mitigating rising energy needs, microgrids (MG) have arisen. But instead of microgrids are focused mainly on unconventional sources of energy. In their service, there is significant variability. Energy users will not know if their estimated load is long or short related to historical records. This paper aims to formulate a robust energy prediction of consumption in the microgrid system that uses random forest (RF) method theory as the mathematical framework. Effective MG energy forecast plays an essential role in power improvement MG efficacy. Comparing RF models with various parameter configurations and examining the parameters setting affects the model’s estimation efficiency.
Shift Learning by Integrating Image and Subsystem Training for Edge Detection in Medical Images
Authors: Ramapraba P. S. , Kishore Somala Rama , Jayanthi S. , Kumar R. 
Published: July 2021

Abstract
Several approaches for segmenting clinical data are focused on controlled vertex shade labelling. In particular, such strategies work well if the training set reflects the test pictures per chapter. However, issues can occur in the preparation and testing process, for instance, due to variations in scanners, procedures, otherwise patient classes, lead to distinct concentrations. In these situations, weighing images based on distribution similarities has shown a substantial improvement in inefficiency. To suggest that most of the training examples reflect the quiz information; it should not be similar to the deceiving information. Therefore, we examine the importance of kernel learning to weigh images to minimize discrepancies between training and test results. A local feature measurement scheme has been proposed to minimize the average distance between training and testing data that allows image weights and Kernel to be jointly optimized. Experiments on brain tissues, lesion of white material, and hippocampus division demonstrate because both kernel processing and image calculation boost the efficiency of heterogeneous data dramatically if used separately. MMD weighting here works similarly to the image weighting approaches previously proposed. The combination of image measurement and kernel processing, independently or jointly optimized, could result in a slight additional performance increase.
An Efficient Approach for Low Power RFID
Authors: Satyanarayana T , Lakshmi Devi N , SivaKumar Reddy B 
Published: July 2021

AbstractRFID communication systems are increasing drastically in recent days, but main drawbacks of present systems limiting the customer needs. The main feature should have by the communication system is security. Maximum present systems are providing security but whereas communication is not error prone hence security also went back foot, more over waiting time of tokens is more because of serial accessing, all the problems can de-addressed by our proposed method. In our proposed method parallel two stage operation called pre and post stages are introduces hence waiting time can be reduced. CRC error checking can be used for the error determination.
Design and Implementation of Wide Band for Biconical Antenna for X Band Applications
Authors: Swamidas Anitha , Sudhakar Alapti 
Published: July 2021

Abstract
This paper reflects architecture and pattern synthesis for biconical antenna. The Biconical Antenna Radiation Pattern is identical to the half wave dipole antenna. The ramp shaped Radiation pattern is obtained by varying different antenna parameters such as amplitude distribution, phase distribution, spacing between the element and number of elements. Biconical antennas (2-14) GHz operating range gives reflection co-efficient- 0.46; return loss- 10dB, VSWR- 2.5:1, Gain (2-4) dB with 50Q input impedance.Biconical antenna was designed using CST Microwave Studio.
Design of Compact Multiband Antenna
Authors: Gollapalli Avinash , Sandeep Kumar P , Rokkesh G , Nithish S , Hirawat Vishal , Rahul  
Published: July 2021

Abstract
This paper presents the design and analysis of a compact multi-band antenna employing multiple radiators, each radiator of different frequencies. The frequencies covered are 900MHz, 1800MHz, 2600MHz, 3500MHz, and 5500MHz. The height of each radiator is different for each frequency. The higher the frequency, the shorter the radiator. For example, the radiator’s height of 5500MHz is only 13mm, while 900MHz is 83mm. The ground plane is placed on the backside of the substrate. The main challenge is to fit all the radiators on the single substrate while keeping the substrate’s size constant, compact.
Synchronized Multi-Rate Test Pattern Generation Using Hybrid Twisted Ring Counter and LFSR for BIST Application
Authors: Sri Priya C M , Sarada V 
Published: July 2021

Abstract
This work presents a new approach for built-in test pattern generation (BIST) based on the variable rate clock-driven linear feedback shift register (LFSR) and twisted-ring counters (TRCs). The proposed technique operates on different operating clocks for generating deterministic test pattern sequence for the circuit under test (CUT). The test vectors generated by LFSR are transformed and reproduced with TRC results. The TRC design constitutes of only flip-flops, which be used as a potential alternative to improved seed storage and complex multi polynomial configurations in LFSR.
Moreover, the control logic is simplified with synchronized multi-rate clock generation and can be easily extended for any test cubes among multiple CUTs. The randomness characteristics of LFSR are retained with improved memory efficiency and considerable complexity reduction. Experimental results validate the proposed test pattern generator (TPG) over existing well-known LFSRs models, and its randomness is proved with ISCAS benchmark circuits.
Diffraction Based Image Synthesis
Authors: Vijay Kumar Gowda B N , Gauni Sabitha , Maik Vivek 
Published: July 2021

Abstract
In this paper, we explore the analysis of optical lens systems by using diffraction-based image synthesis. The resolution and strong contrast are the major characteristics of the image produced from the high-quality optical lens system. To check the image’s quality using the PSF, Image Simulation (IMS) method with a fast Fourier transform convolution algorithm. The Strehl ratio is a measure of the quality of optical image formation from the imaging system. It is well-defined as the ratio of peak focal irradiance to the diffraction-limited peak irradiance. The image quality assessment is done by code V up to scent percentage if the image quality is too good and the Strehl ratio is more. Image quality is worse than the Strehl ratio percentage is low. Finally compute with the sample optical lens system Strehl ratio between automatic optical lens design Strehl ratios in Code V.