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Writing and presenting results is important for any research work. Publishing is not enough; it should be written clearly and consistently for others to read and cite. We present a review of different techniques, for improving the writing in research papers intended for publishing. These include how to write the title, whether to use active or passive voice, how long sentences should be, how to present the uncertainty, understanding the International System of Units (SI) writing conventions, how to assign authorship, how to effectively use references, how to present the work, and how to publish it in a journal. This paper is a summary of the main and most recent recommendations in the scientific field on how to write a research paper to increase its impact.

The following review paper thoroughly enables the readers to emulate the flexural behavior and impact test method for textile reinforced cementitious composites (TRCCs). The flexural parameters of ECCs corresponding to type of fiber material, fiber volume fraction, fiber volume length and strength parameters were discussed. For the textile reinforcement, the parameters like type of weaving pattern, mesh size, roving tex, fiber volume fraction and number of layers were discussed. Based on the tensile strength of textile fiber material, carbon, AR-glass, aramid and basalt fiber mesh were used for strengthening, repair works, plastering and reinforcement for thin elements. And also, the impact test performance on cementitious concrete composites is addressed as well as methods adopted for finding impact performance are discussed from the numerous literatures. This paper provides summary about the flexural and impact performance of textile fibers introduced into the concrete composites in different mesh sizes and fiber volume fractions. Generally, inclusion of textile fibers gives much more performance in tensile, toughness, flexural, energy absorption capacity and ductility in ECC composites. Many of the researchers have tried with different fiber materials for producing ECC and TRC composites separately, but only few of them used both have been discussed. Finally, from the literatures mostly artificial fibers are used for making textile reinforced composites.

In recent times, it has been witnessed that wireless systems based on IoT-based have developed rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has proliferated into several different applications in real-time. The IoT and WSNs now have various critical and non-critical applications impacting nearly every area of our everyday life. WSN nodes are usually small and battery-driven machines. Thus, the energy effective data aggregation techniques that increase the lifespan of the network are highly significant. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. This paper reviews the literature with specific attention to aspects of wireless networking for the preservation of energy and aggregation of data.

Water is vital for the human existence. It has a fundamental role for the economic development and that of the human civilization due to its usage in various sectors namely industry and agriculture. This vital resource is known for its high fragility. Therefore, we have to find effective tools to protect it from pollution. Colorings are used in many industrial sections such as textile dye, paper, silk, and in many nutritional and beauty industries. Colorings are known to be toxic substances that are persistent in the environment; they need physico-chemical techniques for their degradation. Therefore, this study aims at valorizing the Algerian Clay in order to increase its retention capacities of the coloring Bemacid Yellow (BY) through the modification of the Clay character (sodic). Our materials have been characterized with FTIR, BET, and DRX. The BY absorption study on our bentonite has been verified by the study of certain parameters such as: contact time, pH solution, and the absorbent mass. These experiences showed that the Sodic Clay (SC) has a maximal capacity of adsorption at 160 mg/g. The study of the absorption kinetics showed that BY elimination follows the second order model with an intraparticular diffusion. In conclusion, the Algerian Clay presents very significant absorbent properties and may be used in the depollution of textile industries waste water.

In recent years, agricultural wastes cause serious pollution to the environment. Agricultural residues can be used to produce value-added products in order to control environmental pollution, therefore these wastes are a promising feedstock as it has many advantages such as wide range of source, low cost and renewable. Rice straw and lemongrass leaves which are the renewable sources for the production of many useful products such as xylose. The aim of this study is to compare the content of xylose produced from rice straw and lemongrass leaves, in order to obtain highest yield of xylose and to optimize the acid hydrolysis time, concentration of sulphuric acid, liquid–solid ratio and the power of the microwave towards the maximum extraction of xylose. Microwave assisted acidic hydrolysis method was used to extract xylose from rice straw and lemongrass leaves. The acid hydrolysis of both rice straw and lemongrass was performed at different time (1–5 min), acid concentration (1%–5%), liquid solid ratio (1:30, 2:30, 3:30, 4:30 and 5:30) and power (160 W, 320 W, 480 W, 640 W, 800 W). The results obtained indicate that the yield of xylose from rice straw (2.98 g/L) via the microwave assisted is slightly higher than lemongrass (2.91 g/L) with optimum conditions of 3 min of hydrolysis time, 2% of sulphuric acid concentration, 1:30 of liquid solid ratio and 320 W of microwave power. Meanwhile, the optimum conditions for lemongrass leaves are 4 min of hydrolysis time, 4% of sulphuric acid concentration, 2:30 of liquid solid ratio and 480 W of microwave power. Thus, rice straw has slightly higher capability in production of xylose compared to lemongrass.

The alternative and renewable sources of energy are very much essential to reduce the dependency on fossil fuels and also to control the environmental pollution. One of the important renewable sources which replace the fossil based fuels is biodiesel. The biodiesel is highly influenced by fatty acid composition. Hence, in the present work Salvodara persica nonedible seed oil (38% oil content) which is rich in saturated fatty acids (79.16%) is mixed with Leucas aspera nonedible seed oil (22.50% oil content), which is rich in unsaturated fatty acids (90.72%) in equal proportion (1:1). The mixed seed oil fatty acid profile is analyzed by GC-FID which shows that, it is exactly half of the fatty acid profile of individual seed oils (saturation 45.26% and unsaturation 54.74%). Bio-mixed oil is transesterified and the biodiesel produced is characterized for its fuel properties. The fuel properties of biodiesel produced from mixed seed oils is compared with biodiesel from individual seed oils, already existing biodiesels and biodiesel standards. It reveals that better quality biodiesel is produced by mixing seed oil strategy. Even the emission analysis shows that the emissions are reduced except NOx. This work reveals that it is one of the important criterions to improve the biodiesel quality by mixed seed oil strategy.

In the present work, effect injection timing (IT) on the modified dual fuel (DF) engine performance with effective utilization of biodiesel and gaseous fuel combinations is reported. Biodiesel prepared Ceiba Pentandra oil called Ceiba Pentandra oil methyl ester (CPNTOME) and its B20 blend (CPNTOME B20) are used as pilot injected fuels while the biogas (raw and purified) is used as the inducted fuel in the modified DF engine. Hence, the present research focus on the study of DF engine performance fuelled with liquid and gaseous fuel combinations. Meanwhile, the effect of IT on modified DF engine performance is investigated. Brake thermal efficiency (BTE), carbon monoxide (CO), hydrocarbon (HC) and smoke emissions were found to be less besides higher emissions of NOx were observed. Combustion parameters such as ignition delay (ID), and peak pressure (PP) are analysed. The DF engine operated on renewable fuel combinations in DF mode can cover the way for partial substitution of fossil fuel along with reduction in greenhouse gas emissions. Advancing the IT improves the DF engine performance and reduces the smoke, CO and NOx emissions.

The accurate prediction of wellbore thermal profile is essential to ensure the success of any drilling operation. Currently, the heat capacity values used for predicting the thermal behavior of oil and gas wells assume the heat capacity of the drilling which leads to errors. Thus, determining the heat capacities of drilling mud systems would aid to efficiently predict the thermal distribution of wells. In this study, the heat capacities of fresh and aged mud samples of 0.8 wt% xanthan gum water-based mud and 0.8 wt% polyanionic cellulose (PAC) water-based mud and their blend system (0.4 wt% xanthan gum + 0.4 wt% PAC) were measured using a differential scanning calorimetry (DSC) from 5 to 100 °C. The new and aged samples represent testing the mud as fresh samples and over time (1–3 days). The result shows that the specific heat capacity values of the drilling muds differ from the heat capacity of water (which is generally used for drilling-related heat transfer modeling and simulations). High heat capacity values were observed for the 0.8 wt% xanthan gum mud system compared with the 0.8 wt% PAC mud system. However, the blend of 0.4 wt% xanthan gum + 0.4 wt% PAC exhibited the highest heat capacity values. In both fresh and aged samples, the heat capacity of 0.8 wt% xanthan gum mud was in the range of 3.805–4.287 J/g.oC. While 0.8 wt% PAC mud and 0.4 wt% xanthan gum + 0.4 wt% PAC mud system were within 0.7635–1.1329 J/g.oC, and 4.134–4.324 J/g.oC, respectively. These experimental heat capacity values are recommended for accurate oil & gas well heat transfer behavior modeling and simulation predictions and related studies.

The global attention for the alternative fuel to decrease the dependence on carbon-biased fossil fuels and reduce the carbon footprint led to biodiesel production. Crude glycerol generated as a major by-product of the biodiesel industry. The vast amounts of crude glycerol pose an environmental threat. Valorisation of industrial waste is the big challenge to provide a significant economic advantage by the sustainable approach. Hence the crude glycerol needs to be valorised through the biotechnology process into value-added products and gives biodiesel producers more revenue. The biodiesel industrial by-product glycerol waste serves as raw material to convert into several products like citric acid, succinic acid, 1,3-propanediol, butanol, and ethanol. The present manuscript focuses on the significance of crude glycerol from the biodiesel industry as a stable feedstock that undergoes biotransformation to value-added products by microorganisms.

Enforcing admired machine learning approaches to huge data enhanced novel issues for researchers. Conventional libraries could not suitably fulfil the requirement of complex model with wide variety of data and system parameters. Therefore new methodologies are required performing the computation on more than one machine over distributed environment. Some distributed frameworks on huge data such as MapReduce and TensorFlow have been deployed to solve various machine learning problems in heterogeneous distributed environment. The objective of this paper is providing a wide variety of useful information about platforms, approaches, problems, datasets, and optimization approaches in distributed systems. So researchers have been used the beneficial information to develop new approaches for efficient machine learning. This paper also covers the various formats of data like structured, semi structured and unstructured big data. A brief review of previous works is also represented in text and tabular format to provide motivation to the researchers for developing new paradigm of distributed computing environment.

Solar energy, wind energy etc. are the main sustainable energy sources used around the world. These sustainable energy sources are used in day-to-day life in various forms for numerous purposes. One of the highly used sustainable energy sources is the solar energy which with the help of photovoltaic (PV) cells can be made use for the generation of electrical power. However, these sources generate less voltage values and thus it requires a high gain converter. In this paper, a modified Luo with flyback DC to DC converter is presented that assures high voltage gain, single switch, less stress across the components and less ripple current. Furthermore, the simulations along with the hardware prototype for the presented converter is carried out and presented in this paper.

The primary objective of university education is to provide students with the required information and skills to enable them to make a successful contribution to the national development effort. In order to evaluate the level of expertise and skill of students, this preparation includes frequent examination and appraisal in the form of tests. Manual exams are carried out by most universities; Post Graduate and Under Graduate in India. Due to multiple uses made of the outcomes of the students, these examinations have high stakes. Consequently, the experiments became subject to different types of malpractice. Test malpractice affects the success of students in exams and leaves the scores/grades earned inaccurate, hence a challenge to examination bodies and the nation's education system (India). These are all disturbing signs that pose a danger to the nation. Examination malpractice has eaten deep into the Indian culture of education. Therefore, the object of this paper is to explore the problem of examination malpractice in India. The paper covered relevant measures, procedure setting/implementation and suggestions for this action. And finally, a model is proposed to stop malpractices in the examination. This IoT based model is used for detecting any metallic object or object in a range more than normal IR proximity sensor range. The block diagram of proposed model and circuit diagram for that model discussed in detail. This proposed model may be used for any investigation in the universities examinations.

The Secured exams are one of the foremost troublesome challenges in impersonation Judgment. For any Educational Institution, conducting an online authenticated examination is a major task due to its diverse security components. The main objective of our paper is to provide maximum security and to avoid the malpractice due to impersonation which are happening during the conduct of the examinations. The main objective of our paper is to provide maximum security and to avoid the impersonation and it is related to malpractices which are happening during the conduct of the examinations. The methodology we have adapted deals with the automation process of all the manual verification processes which are done. This is done by the combination of IoT and Image Processing technology. The verification of image and fingerprint confirms the authenticity of the person. The final result is displayed in the IoT database which reduces man power and time consumption. This paper has proposed a technique which eliminates all the errors and illegal activities which happen at the initial stage of the examination process. This technique has proved to provide a better security rather than the manual checkup process.

Smart materials have played significant contribution towards the development of buildings. There are various smart materials which can change their properties and behavior under the influence of external catalyst or stimulus. Such materials are changing the face of traditional engineering materials. Smart materials can be utilized individually as well as it can be incorporated with existing materials to enhance their desirable properties. Smart materials are widespread across all the domains of human inventions due to its multidisciplinary applications. Some of these materials are relatively new while some are being used from a very long time. Hence it suffices to say that the recently developed smart materials still require research. The general classification of smart material is illustrated in this review paper. The paper intends to identify the various types of available smart materials and also illustrated the application of such materials in the various fields of civil engineering. Further, the potential benefits and downsides are also discussed along with their applicability under different conditions. Some innovative materials are also discussed which can bring potential revolution in the construction industry. The impact or the benefits related to sustainability also discussed in this review paper.

In the Internet of Things (IoT) the physical objects network is described as “things” linked to sensors, software and other technologies to connect and share information through the Internet with other devices and systems. The floods lead to large losses of life and property in several countries. However, the absence of sufficient technology results in further death and property losses as a result of flooding in developed countries. This is because of a lack of flood alert systems. The objective of this paper is to monitor the flood situation and provide a text message warning in the event of a threat. The main aim of this paper is to detect rising river water levels at a safe distance away from the railways and to allow the respective authorities to take appropriate measures by means of SMS. A sensor is a system that senses and reacts to certain physical data. The 8051 microcontrollers were produced by Intel in 1981. The microcontroller is 8-bit. It has 40 DIP pins, 4 kb of ROM stores and 128 bytes of RAM and two timers and 16 bits. It is equipped with 2 timers. There are four parallel 8-bit ports that are programmable and adjustable as necessary. The hardware device that uses GSM mobile technology for the provision of an information link with a remote network is a GSM modem or a GSM module. A float switch is a level sensor type, a fluid control system that is used inside a tank. A condenser is a tool that stores electrical energy in an electric field. It's a passive electronic feature with two terminals. A LED is a semiconductor light source that transmits light through the flowing current. Liquid Crystal Display stands for LCD. It is a thin, flat display device used in a variety of electronics. A digital electronic level shifter is a system used in the transformation of signs from one logic or voltage domain to another, also known as logic level shifter or voltage level Translation. A diode is an electronic component with a two-terminal that mainly conducts current in one direction, with low resistance and high resistance in one direction. The aim of this paper is to detect early Flood and give early warnings to Needy people.

In the recent years the utilization of cloud to store and process the big data is increased rapidly. Security is the major concern faced by the big data in cloud environment. This paper proposed the authentication scheme for preserving the security and privacy of the big data in cloud environment. This paper considered the medical big data processing from the telemedicine system as the big data. The medical big data consists of huge volumes of medical records which are stores in the cloud. In the proposed mechanism follows mutual authentication and performs encryption operation between user and cloud environment. The proposed authentication mechanism has less computational load compared to the existing algorithms.

This paper is a unique effort to resolve the scaling issues of machine learning using the multi-node environment of Big Data. For this purpose, it incorporates the concept of machine learning with big data. Machine learning is a branch of Artificial Intelligence which trains the computers to learn without being explicitly programmed. Machine learning works on the development of computer programs and software that work according to the input dataset. In machine learning, classification is used to identify the class of instances. In this the category or class or a group provides the new observation to which it belongs on the basis of the training dataset. Training datasets contain those instances whose class is already known. An algorithm that implements classification is known as a classifier. The main aim of this paper is to evaluate how different classifiers work on two different datasets, one is a real time dataset of MP Election nomination (2018), while the other is a standard Breast Cancer dataset, released by University of Wisconsin (1995). There are various classifiers available like Decision Trees, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Naive Bayes etc. Different classifiers work differently on different datasets, giving different accuracy according to the kind and the size of the dataset. So we have used different classifiers for our work. When we were applying machine learning classifier on a real-time dataset, i.e. MPSEC dataset, Accuracy goes down. Similarly, these classifiers apply to Standard Breast Cancer dataset then results show outstanding accuracy. This research work shows the exciting patterns to study the case of a real-time and standard dataset using different machine learning classifiers with a combination of big data. This research work also incorporates the concept of parallelization using a multi-nodes environment which is able to deal with scalability issues.

Road surface irregularities not only has an impact on consistency of road, but also affects driver safety, automobile mechanical stability, and fuel consumption. To order to evaluate road roughness and spot potholes, many solutions have been suggested to remotely track the road surface quality. Some of these methods use an embedded mobile accelerometer to take a crowd sensing view to feel the surface of road. Although crowd sensing has various advantages such as ubiquity and less cost, it has some sensitivity to the problems generated by structures made by human, actions of the driver, and characteristics of the road surface which cannot be identified road anomalies. For this purpose, this paper suggests method of deep learning which helps us (a) to identify the different types of surface of road and (b) to differentiate potholes automatically from destabilizations in the crowd sensing-based application sense arising from speed bumps or driver behavior. This paper study and implement different deep learning frameworks in particular: convolutionary neural networks, LSTM networks, and computational structures for reservoirs. The tests were performed using knowledge from the real world, and the results revealed good precision in solving both of the problems.

As a result of accidents or other injuries, bone fractures are becoming more common in our country. According to the India market survey study, fracture cases are becoming more common in Indian hospital records. The incidence rising by over 4.4 lakh in the last three decades and projected to reach over 6 lakh by 2020. The authors of this paper attempted to explain various forms of fracture detection techniques. This paper is folded into six halves. First, we will go through the introduction and data preparation step. Second, we discussed related work on fracture detection so far. Third, we look at different feature extraction methods that may be used to diagnose bone fractures. Fourth, we look at both traditional and deep learning-based methods for detecting bone fractures. Fifth, we looked at performance evolution approaches for determining the correctness of various algorithms. Sixth, we go through the many concerns and obstacles that researchers confront when working with fracture detection. The majority of authors are only concerned about whether the bone is broken or not, with very few concentrating on the classification of bone fractures. This paper aims to aid researchers in developing models that can automatically detect and classify fractures in human bones by providing a preliminary decision support system.

As the technology is scaled-down and with a decrease in oxide thickness, the effect of NBTI is a major reliability issue in semiconductor industries. NBTI is an aging phenomenon in which the PMOS transistor degraded over time. Deviation in the threshold voltage of PMOS transistor results from NBTI with stress time which affects various parameter of SRAM memory such as degradation in drain current, RSNM, HSNM and access time. NBTI induced aging effect on 6 T and 8 T SRAM cell have been analyzed in this paper for the stress time of 10 years. The reliability parameters of 6 T SRAM cell obtained from simulation result shows there is 6.53%, 23.86%, 3.36% and 13.42% change in Hold SNM (HSNM), Read SNM (RSNM), Write margin and standby leakage current respectively after 10 years. Similarly in 8 T SRAM cell HSNM, RSNM, Write margin and leakage current changes by 5.81%, 5.01%, 3.11%, and 13.24% respectively. This paper also presents the leakage current sensing technique based NBTI monitoring sensor using NMOS transistors only. The sensitivity of the sensor is 40 µV/nA and the linearity of the sensor is upto the practical leakage current range of SRAM cell. Thus we can conclude that the proposed sensor works fine with greater linearity and sensitivity.

Now-a-days, due to mental stress a major section of society is affected by depression. There may be several reasons for depression especially in adults. As different person has different symptoms and its identification is a major challenge. Most of the people feel shy to accept that they are suffering from depression while some people are unaware of their depressed mental health. The objective of this paper is to design and develop an effective tool or model to diagnose depression. In this work, a hybrid system is designed and simulated for detecting depression using EEG features as well as facial features as biological feature gives accurate diagnosis. In this paper a deep learning approach termed as bidirectional long short-term memory (BiLSTM) is proposed with feature fusion from EEG data as well as facial data. The result analysis shows comparative analysis with other existing model and shows effectiveness of the proposed model.

Malware propagation by adversaries has witnessed many issues across the globe. Often it is found that malware is released in different countries for monetary gains. With the proliferation of malware spreading activities, it is made possible that now we have malware patterns that are used for training machine learning models. Thus machine learning became indispensable for malware detection. The traditional machine learning models have limitations in performance as the training depth is limited. The emergence of deep learning models paved way for more training possibilities and improvement in detection accuracy with least false positives. This paper reviews literature on deep learning techniques that are used for malware detection. The deep learning methods used for malware detection include CNN, RNN, LSTM and auto encoders. LSTM is found to have memory in the cell to have better possibilities. Auto encoders are found to have better unsupervised approach with encoding and decoding to arrive at abnormalities (malware) detection. There are many contributions found using machine learning and deep learning towards Android malware detection. This paper provides knowledge that leads to further research in deep learning which is essential to improve the state of the art.

In this paper Telematics and employments of Telematics in vehicles is presented. Advancement is developing every year and is used by planners of the structure in various edges for the duration of regular day to day existence. Telematics system is making in different perspectives. Apparently it gives a lot of organizations in human lives. It makes life snappier and lessening setbacks in vehicle. Telematics data gives the reason to perceiving how far, how rapidly and underneath what conditions an individual drives, similarly as a foundation for logically propelled data showing improvement. This limit is developing new and innovative things that even more correctly esteem peril and attract beneficial new customers. By then, it explains how this structure works. It is like manner tends to the favored viewpoint and block of the system. This paper in like manner fuses the issues related to the telematics structure. Finally, it will see logical examination and wrap up this paper.

The concept of Cyber Physical System (CPS) is widely used in different industries across the globe. In fact, it is the holistic approach towards dealing with cyber space and physical environments that do have inter-dependencies. In the existing systems, there was a separate approach for security of the two worlds (cyber and physical). However, it does not provide necessary security when security is employed independently. The research in this paper identifies the need for integrated security for CPS. Besides it throws light into different security challenges associated with CPS and the countermeasures that existed based on machine learning and deep learning techniques that come under Artificial Intelligence (AI) and data science. From the review of literature, it is understood that data science perspective is suitable for protecting CPS with required adaptive strategy. This paper provides several useful insights related to security analysis of CPS using machine learning. It paves way for further investigation and realize a comprehensive security framework to protect CPS from internal and external cyber-attacks.

This paper targets to transmit industrial data from one sensor node to another sensor node, wirelessly and securely. However as the nodes have Internet access and are typically positioned in an unsupervised context with minimal individual intervention; security and seclusion are the prime challenges in Industrial Wireless Sensor Networks (IWSN). To use IWSN, a user must be able to get real time information straight from the source nodes and this is accomplished by Radio Frequency Identification (RFID). It yields a cost-effective solution of identification problems, but it frequently suffers from diverse safety and privacy difficulties. This paper resolves the issues of RFID systems using Global System for Mobile Communications (GSM) module that generates a One-Time Password (OTP) for accessing the data, and thereby ensures user's privacy and security in Industrial Wireless Sensor Network. Furthermore, eight evaluation criteria have been met by the proposed system surpassing the existing systems.

Over a last few decennary free space optical communication (FSO) have become more interesting alternative to the radio frequency for communication. The FSO is emanating technic that have been found the applications in different place of shorter & longer distance communication. The any technology, of FSO has been worked that much good in the several applications rather than other. In this paper we will examine FSO from different angles. The analyses this paper accompany of the technology has shown that FSO technologies inherent strengths and it lack the use of in ground cables which make it much expeditious & cheaper to installing, and the facts that will operates in the unlicensed spectrum, it will be removed & installed elsewhere and it has relatively high bandwidth. Specifically, this paper found that because FSO uses air as a communication medium, its performance & reliability are restricted by fog, rain, snow and dust. Maximum range of FSO may be in kilometers only. The applications that FSO technology seems most suited to is clear this pap rather and short distance connection establishment, like interrelation to broadband networks backbone & backbone links bet this papered building in the metropolitan area networks (MAN) or the campus area networks (CAN).

The first thing addressed in this paper is Plant fruit recognition and its feature extraction, it plays vital role in Agriculture. The point is to construct an exact, quick and solid framework utilizing CNN realities. Automatic fruit detection may reduce human efforts (counting number of fruits and manual identification). The second thing addressed in this paper is plant classification to avoid similarities in many plant fruits. Fruit classification may help fruit sellers to identify and to differentiate various kind of fruits having same similarities. The proposed framework has applied convolutional Neural Net (CNN) to the undertakings of distinguishing natural fruit pictures. In any case, deep learning has been shown as of late to be an extremely incredible picture identification procedure, and CNN is a best in class way to deal with deep learning.

Cotton detection is the localization and identification of the cotton in an image. It has a wide application in robot harvesting. Various modern algorithms use deep learning techniques for detection of fruits/flowers. As per the survey, the topics travelled include numerous algorithms used, and accuracy obtained on using those algorithms on their data set. The limitations and the advantages in each paper, are also discussed. This paper focuses on various fruit detection algorithms- the Faster RCNN, the RCNN, YOLO. Ultimately, a rigorous survey of many papers related to the detection of objects like fruits/flowers, analysis of the assets and faintness of each paper leads us to understanding the techniques and purpose of algorithms.

The excellent way to enhance the lives of the people with disabilities is technology. There have been several avenues of research that has opened the room after the advent of Artificial Intelligence. The Lives of people with disabilities can be improved by Artificial Intelligence. The smart devices have been supporting the people with sight loss and physical disabilities. Route planning software, ranging from robotic arms and other prosthetic limbs has made true advances which is a significant contribution done by Artificial Intelligence in the lives of persons with disability. The dependency of human life on technology is outreaching even to the Person with Disabilities. The prevailing technological trends have started becoming a current favorite amongst those who need assistance because of any form of Disability. While some artificially intelligent technology is still fairly new the potential is all there. The Paper discusses how Artificial Intelligence has helped in improving the living standards of Person with Disability, its future possibilities, potential legal issues concerning the implication of Artificial Intelligence in India. In this paper the authors want to put an emphasis on the ways by which life of persons with disability can be easier and swift in respect of their day-to-day activities by using modern technology namely Artificial Intelligence.

This paper deals with automatic clustering algorithm, which partitions a many of objects to get smaller sets (clusters). Where objects of the one set are related to each other rather than to those in the other sets. The number of clusters in automatic clustering don’t given priori, they determinates automatically, the number resulted of clusters exact closed to the numbers of the real dataset structure. This paper represents a stimulus for the current study to introduce an algorithm that automatically finds the number of clusters based on distance among vertices. The study is based on the hypothesis that the proposed algorithm is able to efficiently find the clustering partitions for the whole dataset.

This paper proposes a mapping approach of various parameters and utilized an optimized Remote Sensing and GIS strategy. A part of Andhra Pradesh, the Krishna delta of Krishna District is chosen as the study area for the evaluation process. The paper incorporated essential information such as field data, topographic maps, IRS satellite images and other collateral maps as inputs. Significant position factors affecting the availability of surface water in the area were established, and corresponding thematic data layers were developed. These data layers depict the study location's geological, hydrological and topographical conditions. For spatial analysis in GIS software, an objective measurement scheme for the considerations was created. The resulting chart of water flow classifies the region into four distinct zones based on their relative information. The maps were validated by correlating the flow of water with the specified relative variables available. This is consistent with the observed field instability state. The map's usefulness was also demonstrated by the strong statistical significance as determined separately for every map.

Blockchain has become one of the most developing technologies in our country. Blockchain technology is a decentralized, distributed ledger that records the sources of digital assets. Electronic Medical Record (EMR) accommodates patient’s medical history, which has been carried out by a Physician or Doctor in a particular clinic or hospital or Healthcare center. Whereas, Electronic Health Record (EHR) holds patient’s entire health history, including immunization plans, diagnosis, lab reports, inoculation schedule, etc.,. This feature empowers the usage of Health IT in a more secure way with the amendment of blockchain technology. These records are maintained smart contract between the patient and the healthcare sector. In both EMR and EHR, the patient is the owner of his own data. This paper incorporates the features of blockchain technology to provide a collaborative work on Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) and it concentrates highly in diabetes as it has a sharp magnification in current world. Though it is tedious to implement this EHR all over the country, this paper works for its implementation especially in the domain of diabetes.

Mobile Ad-hoc networks (MANET) is a collection of nodes of wireless connectivity. In Mobile Ad hoc Network (MANET) all the mobile nodes are requiring restricted battery capacity. Because of the changing nature of network the sudden communication link breakage happens. Hence the possibility of data loss is more. Efficient utilization of energy and lossless transmission are the important issues in MANET. This paper proposes a new Trust based Efficient Energy Balanced Less Loss Routing (TER) Protocol for MANETS. This protocol improves the network performance by properly choosing the intermediate forwarding nodes. The Source Node picks only single of its neighbors as its next hop node by using Efficient Parameter (EP). The EP is calculated from the residual energy, distance, Occupied Queue space, the velocity of that node. The node using maximum EP is nominated as the succeeding node. This protocol reduces the number of channeling messages and hence reduces the path-finding Overhead of the network. It lessens the chance of data loss and hence improves throughput and Packet Delivery Ratio. This protocol adds trust to the selected node. In this paper, we do the simulation in ns-2 simulator and the performance is measured by with performance parameters like Routing Overhead, Packet Delivery Ratio, Throughput, End to End Delay and Average energy consumption.

VRML (Virtual Reality Modeling Language) is a nonproprietary language used for scientific simulation and visualization. VRML alone do not have the capability of holding numerical values but with the help of slider design, the numeric values can be dynamically varied. The VRML Graphic User Interface (GUI) presented in this paper demonstrates dynamic simulation of Seed Grader Machine Parameters and its effect on final output. This paper describes how VRML capabilities can be utilized for simulating Seed Grader Machine Parameters in virtual Environment. The concept can be applied for variety of applications involving similar requirements.

Face recognition is one of the useful tasks and can be used for many applications as security systems, it is necessary to find effective and low complexity facial classifier methods. In this paper, we proposed a new one-dimensional CNN deep convolutional neural network (1D-DCNN) classifier was combined with linear discriminative analysis (LDA) techniques to produce a new face recognition methodology. The contribution of this paper is generated of one-dimensional face feature set by LDA from original image database to training of 1D-DCNN classifier, that it contributed in the improvement of facial recognition performance. The model has been tested on MCUT dataset consisted of 3755 images for 276 classes. The results of the implementation of face recognition were accuracy of 100%, precision of 100%, recall of 100%, and F-measure of 100%.

The main purpose of this paper is to observe the effect PV variation of solar temperature and irradiance on different conditions and on the inverter output for a grid-connected system. Majorly temperature& solar irradiation effects the performance of a grid connected inverter, also on the photo-voltaic (PV) electric system. The simulation based study was carried out in order to evaluate the variation of inverter output with the variation of solar temperature and irradiance with the variation in climate. The analysis of Grid-connected inverter and their performance at various seasons and conditions is investigated. Solar power plant for a year. In solar power plant efficiency of inverter is also considered to calculate overall losses so, the inverter efficiency and plant performance are considered in this paper using MAT Lab software. In summer season the inverter performed efficiency is decreased because of peak temperature value and slightly increased with the increase in irradiance.

Mobile Edge Computing (MEC), which is the main technology behind 5th Generation (5G) networks, is a nascent paradigm that meets the demands of IoT (Internet of Things) and localized computing. From an end-user point of view, it can be regarded as a refinement of cloud computing and Fog computing. The transfer of computational tasks to the closet MEC servers leads to energy efficiency and low latency. It is important to apply the most suitable policies for scheduling, especially when configuring different modules of an IoT application in MEC. In this paper, three algorithms, namely the heuristic Bald Eagle Search Optimisation [BESO] algorithm, Particle Swarm Optimization algorithm [PSO], and Genetic Algorithm [GA], are presented to carry out heuristic offloading of computational tasks with a view to improving the latency and performance of MEC. However, the most effective algorithm must be adopted to conduct these tasks. As a result, this paper attempts to find an algorithm that is most appropriate for MEC. To demonstrate this. the three algorithms were tested in the Long-Term Evolution [LTE] based Orthogonal frequency-division multiplexing [OFDM] network during a period when the edge nodes had no adequate resources. The performance and efficiency of the three algorithms, BESO, PSO and GA, were determined and compared. After generating the results, the comparative results were evaluated. In terms of offloading the computational tasks, the BESO algorithm was discovered to perform better, with greater energy efficiency and lower latency, than the other two algorithms.

Enhancement of reliability is critical in designing and planning of distribution systems that operate efficiently and with minimal interruption to customer loads, because they use various types of resources and technologies to meet energy demand such as distributed generation (DG). DG is expected to play an enhanced role in emerging power systems. One of the major issues in the planning stage for future distribution systems is the optimal design of multiple micro-grids (MMGs) in distribution systems. This paper describes a novel method for clustering a traditional passive distribution system into a cluster of Multiple Micro Grids (MMGs). Clustering a traditional distribution system into MMGs provides numerous merits for costumers and distribution system operators, including a local control strategy to minimise interaction between various MGs, local reactive power compensation to prevent fault propagation, minimise power losses, and ultimately improving system reliability. In each MG area, DGs and reactive power resources are deployed to reach the acceptable level of reliability. The proposed clustering procedure is based on a weighted graph partitioning approach, with the weights representing the apparent power of the lines. Various reliability tests were performed in order to determine the best location for DG penetration in the distribution system. The passive distribution system becomes an active system after DG penetration. As a result, planners must investigate the influence of DG on distribution system reliability. The aim of this paper is to investigate the impact of DG on distribution system reliability. The results on the test distribution system demonstrate the proposed method’s performance and effectiveness.

Recently, the social media podium used to share and post patient medical content, which significantly lead to mining medical content for pharmacovigilance, drug repositioning, and Healthcare sector. Paramedical advertisers extract trusted health information from the social media platform and correlate health care information with the user profile for recommending proper treatment. The mining of the patient's personal and medical information on social media makes the patient aware of the recent drug revolution. For predicting computational medical services, there is a need to Develop a centralized Social media-based pharmacovigilance activities virtual podium that identify drug and ADR associated with it. Build an informational community of homogenous E-patient over the virtual platform for building informational and emotional support more efficiently. In this paper, a comprehensive overview of the recommendation system and possible research gap associated with drug repository and ADR are presented based on recent research. Furthermore, this paper present clinical post pre-processing, clinical entity recognition, tweet pooling and expert rating framework to classify Disease-Drug-ADR intersection for recommending drug and ADR. Simultaneously this framework present comparative analysis of benchmark classifier to trained and label corpus of tweets for drug repositioning and ADR automatically.

An ad hoc network is a network made of movable nodes built on the fly and constructed dynamically when there is no longer an infrastructure or pre-built network on the scene. The nodes can also perform both end-to-end terminals and shape MANET roles, meaning they contribute to each route's configurations. The data must travel through several intermediate nodes during the route while routing a given flow from a source to a destination. The data should be larger than needed for just a single application use. The intermediate nodes forward data to the intended destination, taking into account the locations to which it must be sent to arrive correctly. The routing protocol in an Ad hoc network (human-made network) uses nodes to look at different device locations and procedures for any data path they choose before deciding on the route and ways in which they exchange data are viable. Anomaly, since the existing trust among the various nodes and dynamic topology, leaves the routing protocols susceptible to Denial of service attacks like a black hole, wormhole, Denial of service, and being non-integrated into a central infrastructure present a condition which MANETs are subject to such as Conventional networks typically work by injecting some control packets and tracking the target's movement once the user is already on the wireless. Still, in a MANET, the attacker acts before the mark has moved into the wireless medium. Maliciously exploiting various routing information has the potential to drive the system into a situation of more significant disorder, which will eventually lead to widespread network failure. This existing AODV attack, called the Blackhole attack, might have worked by purposefully withholding important routing information from end-users who could have benefitted from it, which is just what the adversary does in this task. In this scenario, the data packets are never delivered, and the system suffers a total data loss. The variety of detection and protection techniques employed against the blackhole attacker significantly lower the number of suspects. Therefore, this paper advocate for OSPFV [for wireless LANs] protocol integration with built-in security, where Threshold evaluation and cryptographic verification are employed. In this paper, two protocols: the blackhole attack and the proposed AODV-BS protocols, are simulated on different MANET models, and the two other network metrics are used: Network Packet Delivery Ratio and the normalized Out of Routing Overhead Utilization and Network Delay are calculated, and then their performance is studied to discover the result.

Interest in Social Entrepreneurship has been growing among non-profits and academicians over years. Discussions on these topics has been held among a variety of practitioners in a number of directions. This paper shed a light on the background and the present composition of discussions on these topics. The true strength of social entrepreneurs is their ability to recognise business failures that are holding society back, as well as their ability to tailor and execute solutions. These include clean water, access to renewable energy, financial inclusion, high-quality educational opportunities, and vital knowledge that enables life-giving agriculture to thrive. The scope and depth of the social problems at stake have grown in tandem with the field's maturation. We face a growing tide of crises, including teetering democracies, racial inequality, gender segregation, environmental disaster, population development, food insecurity, nationalism, and technological advancements that necessitate new skills and workforce training.” We are in danger of reversing the ground-breaking social achievements we have made. We also risk losing the knowledge of seasoned, but often discouraged, social entrepreneurs at a critical juncture in the social sector’s history. The aim of this paper is to identify the challenges and opportunities for social entrepreneurs in India.

This paper tries to identify the significant impact of various demographic factors of consumers on the preferences towards the selection of online banking in day to day life. For that the primary research was conducted by designing the structure questionnaire by asking different open ended and close ended questions to the consumer for the usage of online banking. The findings of the paper is that majority of the consumer belongs to the male having the basic educational qualification of graduation or post-graduation. There is significant difference among few demographic factors with the preferences of consumer towards the online banking. The statistical tools applied for these are frequency distribution, crosstab and chi square test.

This paper focuses on designing of Multi-Purpose Mechanical Machine. The research specializes in design and development of multi-purpose machine tool which is able to perform multiple operations like drilling, cutting and grinding simultaneously and can also performs one operation at a time as per the requirement. The functions of machine tools are to transform the raw substances with the help of machining operation to finished part with precise geometry, dimensions and surface quality. The call for is increasing to provide elements with high standard at minimal cost, the machine tools are required to have better machining, precision and speed. This research paper proposed a machine which can perform operations like drilling, cutting and grinding simultaneously which means that industrialist have no need to purchase different machines for different operational task.

In the current era, Artificial Intelligence is playing a major role in smart devices. Apart from incorporating intelligence in the devices, it has extended its range beyond that. Now new AI development tools such as automated machine learning (AutoML) approach are increasing rapidly because of its performance on smart devices. AutoML is used to design a machine learning model with self-adapting capability which can perform the task in less time with advanced skills. This approach does not need any explicit coding and the data science experts but this can build smart sensor codes that will get executed automatically on the microcontrollers embedded within the IoT devices. Due to these capabilities, the developers can build efficient smart devices with real-time responsiveness, security, and data privacy without the help of machine learning experts. In this paper, we will show how AutoML will be used to create smart de-vices. The objective of this paper is to develop the skills that will help to develop smart edge devices which include planning, design-ing, implementation, and the evaluation of the smart IoT applications.

Solar air heaters (SAHs) convert incident solar radiation to thermal energy on an absorber plate. Smooth absorber plates have a low convective heat transfer rate in traditional ducts. Artificial rib roughness is applied to the bottom sides of the absorber plate in order to improve heat transfer in SAHs. However, when the rib roughness is employed over the absorber plate of the airflow side, it also increases the pressure loss due to friction. The purpose of this work is to provide researchers with a scientific perspective on the geometry, structure of ribs, flow directions, and laminar zone in order to maximize heat transfer and reduce friction. This review article discusses different roughness parameters, various artificial roughness geometries, and thermohydraulic performance. Nusselt number and friction factor correlations have also been incorporated in this review article which is developed by various scientists based on their respective experimental results for various roughness parameters. The paper also provides a thorough analysis of current research, summarises prior developments, and speculates on potential future routes for developing new heat transfer systems. Furthermore, this review paper will assist researchers and academics in developing a sound understanding of the effects of roughness on the absorber plate.

Heat transfer enhancement is the essential region to protect energy, and furthermore, to accommodate a financial perspective is a concern. Heat transfer enhancement techniques are utilized into different mechanical applications as into heat exchanger, air conditioners, solar tubes, chemical reaction tube and refrigerating devices. The few passive types of equipment as twisted tapes, irregular components, wires inserts etc. are an efficacious technique to upgrade into heat transfer. Amongst the transcendent techniques utilizing in heat transfer for passive type is twisted tape inserts. The present research paper reviews the heat transfer enhancement region with twisted tapes into recent years. Past examinations on various kinds of twisted tapes (basis of review) were shown. Also, the paper unveiled the research for future on twisted tapes will more noteworthy improvement in heat exchanger device additionally gives scope for various altered methods for the new advancement of heat transfer improvement. [copyright information to be updated in production process]

The present for supporting in market any industries need to bring the balance among ecological, social viewpoints and efficient. Environmental friendly machining e.g. high pressure cooling, MQL machining, cryogenic machining, dry machining is one of the latest approach which is economical and also eco-friendly that improve machinability. In present article main focus on understanding of environmental friendly machining techniques and investigate various experiences from the past writing which can help the better appropriation of ecological friendly machining approach. For study and examination of environmental friendly machining approach, roughly 100 papers are chosen. They chose articles are ordered dependent on climate of machining, year, diaries, distributor, and ecological friendly machining approach measurements. After the literature survey it was observed that in metal cutting operation heat are generated in the cutting zone due to tool chip interface so quality of surface finish are poor and tool wear. In any manufacturing industries two factors play important role in term of quality e.g. surface roughness and tool wear so in this situation some techniques are need which is environmental friendly and also improve machinability. So finally, it was observed that environmental friendly machining techniques are cost effective and eco-friendly machining compared to other convention machining approach. The main motives of this paper that studies and encourage environmental friendly machining approach.

Friction stir welding (FSW) is a new solid-state welding technique used to join similar and dissimilar materials, especially aluminium alloys. Friction stir (FS) welding proposes numerous benefits compared to conventional fusion welding techniques and can be successfully applied in shipbuilding, aerospace, automobile, and railway industries. This paper gives a comprehensive review of the FSW process and its technical aspects. The effects of tool pin profiles, feed rate, tool tilt angle and welding speed, etc., and mechanical and metallurgical behavior of FS welded joints have been reviewed in this article. In addition, this paper covers the literature review of friction stir welding of similar and dissimilar aluminum alloys (AA), optimization techniques, welding defects, and fatigue behavior of FS welded joints. Furthermore, a summarized report of work done by many researchers in friction stir welding is also presented. Finally, it also identifies the future scope, applications, and research gap.

This paper aims to integrate Industry 4.0 technologies, precisely the Digital Twin concept and smart materials, with the Electric Vehicle (EV) industry, to complement the adoption of Electric Vehicles (EVs) in the Indian market. In the current scenario, the adoption of EVs in India faces challenges ranging from insufficient charging facilities to the difficulties related to the complicated connections inside an EV, making the repair and maintenance of these vehicles quite arduous and costly. This paper specifically deals with the complexities regarding the repair and maintenance of EVs and aims to ease the process to ensure a bright future for the EV industry in the Indian markets. Throughout this paper, different challenges regarding the repair and maintenance of EVs are discussed. The solutions to overcome these challenges with the help of various Industry 4.0 technologies and smart materials will pave the way for the smooth and efficient adoption of EVs in the Indian markets. The paper also discusses the significant research challenges associated with the study and its future scope in the industry. The findings of this study enabled us to recognize a variety of smart materials that the EV industry can use in their cars which will make the maintenance of EVs much more affordable by cutting down the heavy costs incurred in getting the dents and scratches repaired if the vehicle meets an accident. Also, the use of Digital Twin in the servicing and repair of EVs will make the process a lot more convenient by reducing the service time and related costs drastically. Further in our study, we have described in detail the working process of DT and smart materials in the EV industry to achieve the goals of this study.

Electronic Speckle Pattern Interference (ESPI) is a non-contact optical technique generally used for the measurement of surface deformations of various kinds. With the help of phase retrieval algorithms, phase of the deformed object is calculated in this method which can be then converted into displacement information. This paper demonstrates the use of ESPI method to measure the thickness of some nano-sized metallic thin films. Calculated thickness values are in good agreement with the values measured via Profilometer. The least thickness (z-axis) measured by this method is ~ 52 ± 3.1 nm. The z-axis resolution of the used method is at par with other expensive microscopy techniques like atomic force microscopy. Therefore, a low-cost alternative to expensive microscopy techniques is presented in this paper as far as z-axis is concern. Surface roughness of thin films can also be calculated using retrieved surface profiles and they are also found to be in the nanometer range.