In this paper, the energy and power density of vehicles, energy-storing devices, and motors have been revisited. Hybrid propulsion systems like amphibious vehicles are considered along with normal propulsion modes. Power density reflects the amount of power delivered, and energy density reflects the range of systems. Power and energy densities of various transportation such as Electrically powered vehicles (EVs) Internal Combustion Engine (ICE) vehicles, Hybrid Vehicles (HVs), underwater vehicles, airplanes, ships, and hybrid mode transportation systems, are analyzed. The energy and power density of multi-mode propulsion are also calculated to explore the possibility of converting existing automobiles to multimode transportation systems as a part of retrofitting in the future. Power density matching for energy sources, ICE, motor, and coupling can lead to better operation of the propulsion system. This concept is parallel to the maximum power transfer theorem. Power density matching of all components associated with power flow can lead to better stability, lesser vibrations, and reduced maintenance of the system components. It reduces the need for frequent replacements of power components due to power stress. Energy density matching of all power flow components is equally important. A detailed study by considering various vehicles is presented in this paper.

In recent years, VoIP technology has become very popular among internet users as it allows the users to transfer their voice & video over an IP network. Its advantage is that it is cost-effective, works over traditional telephone networks, and is also compatible with the public switched telephone network. It is based on P2P architecture and allows users to communicate with each other in real-time through audio or video conferencing over the network. It consumes a lot of network bandwidth. That is why, the classification of VoIP traffic is important from the viewpoint of monitoring, prioritizing, or blocking such traffic by the ISPs or network administrators. Traditional classification techniques such as port-based and payload-based are ineffective since modern VoIP applications make use of random port numbers, encryption, or proprietary protocol to obfuscate their traffic. In this paper, we focus on classifying VoIP (video) traffic by utilizing a 2-step classification process. The 1st step uses a packet-level process, where P2P-port based technique is utilized to classify VoIP traffic. The traffic which remains un-classified as VoIP is then fed to the 2nd step classification process (i.e. flow-level process), which combines proposed heuristic rules (with a unique packet size distribution of VoIP traffic) and statistical-based technique to classify VoIP traffic. The experiments have been conducted on real traffic traces using offline datasets and results show that the proposed technique not only achieves high classification accuracy of over 98.6% but also works with both TCP & UDP protocols and is not affected even if traffic is encrypted.

This paper presents a comparison between the different adaptive algorithms for designing a cost-effective Anti-Noise Control (ANC) system. Typically, the ANC systems use linear FIR-adaptive filter Filtered- Least Mean Square (FxLMS) configuration. FxLMS is straightforward in hardware implementation but, its efficiency substantially degrades in case time-varying and non-linear acoustic environment and the probability that it will converge to local minima. Meanwhile, the implementation of Trigonometric Functional-Linear Adaptive Neural Network (FLANN) enhances ANC performance for non-linear noise signals. However, at the same time, it increases the complexity of hardware implementation and is unable to solve the problem of local minima convergence. Whereas evolutionary algorithms -Genetic Algorithm (GA) and metaheuristic algorithm Particle Swarm Optimization (PSO) increase the robustness and stability in non-linear, and the time-varying acoustic environment with absolute zero probability to converge to local minima. This paper briefly discusses on implementation of FxLMS, FLANN, PSO, and GA-based ANC systems. Further, simulation compares Mean Square Error, BER, and PSNR to provide computational efficiency of these algorithms.

Tropheryma whipplei causes severe malady termed as Whipple’s disease, a multisystemic lethal problem and we still require modified best regimens. To treat it successfully, 3 medications were distinguished in this investigation by using in-silico methods. 2-amino7fluoro5oxo5Hchromeno[2,3b]pyridine3carboxamide(2APC), Nicotinamide mononucleotide (NMN), and Riboflavin Monophosphate (RFMP) were seen as putative medications. 2APC and NMN restrain DNA Ligase catalytic activity for Tropheryma whipplei and compelling in impeding genomic copying and repairing mechanisms, RFMP shows the inhibitory impact on Chorismate synthase that drives hindrance in metabolic biosynthesis of amino acids. Our investigation used modern advanced in-silico assemblies. BLAST, CDART, CD-HIT were utilized to choose target catalytic biomolecules of a bacterium. Phyre2, dependent on HMM calculation, was applied to discover the best auxiliary models of chosen biocatalysts. AutoDock- Vina assembly was utilized for molecular docking and scoring restricting energies of these medications with catalytic proteins of the bacterium. 2 APC and NMN hindering DNA Ligase show - 8.3 and - 8.2 kcal/mol individually while RFMP represses Chorismate synthase - 7.3 kcal/mol binding energy. Sub-atomic re-enactment or simulative mechanistic analysis gives further approval to concluding 2APC as impeccable inhibitory medication having remedial activity against T. whipplei. This escalated and novel examination is simple, quick, and valuable in anticipating drugs by incorporating computational insights in medicinal sciences.

To standardize the active power flow in the interlinked power systems, the controller must be crafted in such a way that it would improve the system's stability. With this objective, the paper is dedicated to the hybrid flower pollination algorithm: a metaheuristic optimization algorithm (hFPA) applicable to optimize the parameters of the PI and PID structure-controller incorporated in the two-area power systems. The projected algorithm is compared with the articles published where the PI and PID controller structure parameters upgraded with hFPA are compared with the enhanced strength Pareto differential evolution and grey wolf optimization algorithms to demonstrate its robustness for a vast span of system parameters and varying load situations. The superiority of the technique has been presented in respect of peak undershoot, settling time, peak overshoot, tie-line power, frequency divergence, etc.

Diabetes mellitus is a deadly disease that affects people all over the globe. An early prediction of diabetes is very beneficial as it can be controlled before the onset of the disease. Various data mining classification techniques have proven fruitful in the early detection and prediction of multiple diseases like heart attack, depression, kidney-related diseases, and many more. This paper discusses and compares various data mining techniques for the prediction of Diabetes Mellitus. Also, three widely used data mining techniques via Artificial Neural Networks (ANN), K-nearest neighbor (KNN), and Support Vector Machine (SVM) have been implemented in Matlab and the results are compared based on accuracy, recall, true negative rate, and precision.

Since the inception of Bring Your Own device (BYOD) a series of the study conducted in various segments of BYOD. The exponential growth of the adoption of BYOD technology increased the demand for research. As a result, academic researchers created a good amount of abstracts with new techniques and methods. This study article offers a systematic study of existing techniques and development on the BYOD cybersecurity ecosystem. The primary goal of this systematic review is to identify the existing research specifically on BYOD Cyber forensic ecosystem and grouping the techniques developed in various areas and summarized the findings. Post analyzing 8519 articles this study identifies the potential 18 research which contributes to enhancing the cybersecurity forensic ecosystem. Limitations of existing research are also identified which organizations need to mitigate to build a cyber secured Forensic BYOD environment. Finally concluded that BYOD Cyber secured ecosystem is indeed needed for the organization for enabling BYOD services so that due to the impact of cyberattacks in the BYOD environment business ecosystem does not get fragmented.

Dengue viral illness is communicated to humans through the bite of female Aedes aegypti mosquitoes. This disease may become lethal in numerous patients. The availability of an efficacious vaccine makes alarm for public healthcare. The dengue virus multiplied inside the host framework involved many host-viral protein interactions. This immunoinformatics study was designed for the prediction of immune epitopes for T cell-mediated immunity. The epitopes are anticipated from the most connecting viral protein with humans. We utilized a couple of epitope mapping tools for the determination of immunodominant epitope for vaccine design. The physical interaction between epitope ligand and receptor MHC class I alleles was analyzed in the molecular docking study. This study was concluded that two epitopes (‘SRAIWYMWL’ and ‘FLEFEALGF’) are suitable for the designing of an efficacious multi-epitope vaccine. The clinical validation is considered necessary for the final confirmation of vaccine potency.

In this paper, we review some detailed facts and information about dark energy & dark matter and try to examine their existence and nature. We also seek to extend their influence to understand various transformations of cosmological periods that have taken place, the evidence of which can be found in the chronology of the universe, along with the significance of dark energy and dark matter. We study the present-case scenario regarding the evolution of the universe concerning the Friedmann Equations, derived from Einstein’s field equations, along with some important cosmological parameters. This analysis also underlines an effort to obtain a clear and crisp picture towards which the universe is heading with time. We also review the various cosmological eras found in the chronology of the universe, which was dominated by radiation, matter, and dark energy respectively, and derive a conclusion of what the universe might have to attain during its evolutionary course.

The following review is concerned with the working and development of the magnetic field around the solar system. It combines the various theoretical facts that have been extracted to date emphasizing how the magnetic field is present in the solar system and how it plays a crucial role in protecting it as well as other constituents of the solar system comprising planets. Since the magnetic field is all-pervasive throughout the universe, it can be considered to be a crucial element for the development of any planet or cluster or other celestial and intergalactic entities as well. Through this theoretical review, we will be able to discern the role of the magnetic field in a solar body, whereas through observational data we will be able to understand the practical orientation of the respective magnetic field in different planets throughout the solar system.

Proper development and growth of crops had always been a major concern and challenge in Agriculture. Proper crop development assures good quality of crops and also bumper harvest. Humans may not always identity all plant diseases accurately at all stages having an automated system for crop disease identification and detection can be a great help for a tiller. This thought inspired me to perform the proposed research work. VGG-16 based learning model achieved an accuracy of 98.74%, ResNet-50 based transfer achieved an accuracy of 98.84%, and ResNet-50 v2 based transfer learning model achieved an accuracy of 98.21%.

Light-emitting diodes have become very popular in general lighting due to their various advantages over other luminaires. Power consumption and LED size also play an important part in their selection over CFL’s. LEDs are compact and brighter and can be modelled according to the requirement. Due to the compact size of highpower LEDs, the temperature or heat produced by the LEDs will be more, so some efficient cooling methods must be employed. This paper compares heat dissipation through heat sinks of different sizes. This paper claims that the temperature of highpower LEDs cannot be lowered after reaching a certain point, as a result, an increase in the size of the heat sink at that particular level is useless.

The brain is the central controlling system in the human body. If the structure of the brain changes due to the enlargement of the brain cells, it is diagnosed as a brain tumor, which can lead to fatality. The techniques such as medical imaging provide evidence of whether a patient has a brain tumor or not. This paper discusses various machine learning techniques for brain tumor detection using MRI and provides a performance analysis of such methods based on the state-of-the-art. A comparison of ANN and CNN-based models have also been given after implementing the techniques in Tensorflow to understand the potential benefits of using deep learning-based techniques.

The development of Technology in the field of electronic devices is going very rapidly. Factors such as cost, performance, complexity, and portability are contemplated over and over again. Nowadays low cost and better performance captivate the interest of a wide range of public. To meet these demands of the public various components are integrated onto a single chip. As this integration increases, the complexity of the devices increases which leads to increased chances of faults and failures in a device. It is not that today’s urban human is using the electronics keeping hi-tech gadgets in hand, but today’s mechanical transport means are also driven by daily changing and improved electronics devices. So many times, there are recalls of sold components or devices by leading electronics giants due to post prediction of their failure. This happens because the companies are using traditional techniques for condition monitoring and reliability testing. Even big automobile giants have to recall their cars for defects occurring in the later stage. One of the examples of recalling of TATA public transport buses sold to DTC in Delhi, India, as those automobiles are getting caught in the fire in many cases. Traditionally, to analyze electronic components reliability and condition monitoring, three techniques are used, viz., using empirical methods including standard handbooks MILHDBK-217, BELLCORE, and PRISM; analyzing using life testing experiments; collect maintenance and operating data and perform statistical analysis.

SHM of the bridge is the major issue for finding the condition and life span of the bridge. Recently many bridges have collapsed due to floods and environmental conditions. The up and coming age of extension SHM innovation needs to persistently screen conditions also, issue early alerts before an exorbitant fix or disastrous disappointments. With the assistance of the Internet of Things (IoT), the SHM can be transformed into a real-time monitoring system and the monitoring can be processed from any remote location through the internet. At present cloud computing is integrated with IoT for storing, monitoring, and performing analytics on the data received from the sensor node. In this study, we have proposed the Edge gateway and ZigBeecommunication-based SHM of the bridge. In the case of a bridge, the decision needs to be taken immediately for avoiding the demolition of the bridge. For immediate decisions, we have employed edge computing at the gateway node that performs analytics immediately and gives the necessary action to be undertaken. In this study, we have embedded the Zigbee module in the sensor mote for sensing the distinct parameters of the bridge and communicate them to the edge gateway. Edge gateway performs analytics by applying artificial intelligence techniques for predicting the damage and condition of the bridge and sends the alerts to the cloud server via a Wi-Fi module. From the cloud server, the authorities can access through the web application and mobile application.

Diabetic Retinopathy is the major problem in Diabetic affected people, as it causes blindness to the people who are affected by this particular disease. Recently many people are being affected by Diabetes due to changes in food habits and the quality of food people take. Early and continuous testing of the eye in regular intervals leads to the identification of this disease by which the blindness problem can be overcome, but due to lack of availability of resources like optholomists and the machinery for monitoring the eyes the early detection is not that easy. The early machines and methods adopted will not provide good results in any cases because of many constraints, As the technology is advancing, with the help of neural networks and Edge Devices regular monitoring the diabetic people can be done easily and effectively in any part of the globe with much ease. At present image processing techniques are being used. In this study, we propose Deep Learning algorithms that process the date very accurately in very less time because of which we could good results in performance evaluation. In this study we have also proposed Edge Device which are end term devices, where these devices perform analytics on images of the eye and predict the condition of the eye and also it gives the information of stages of Diabetic Retinopathy, we can also store the data which is processed by edge device on cloud servers via wifi, From the cloud server the concerned people can obtain the records of that person who is affected with Diabetic Retinopathy.

The actual framework will improve seed or plantation growth by increasing their production, efficiency, and economic benefit. It also allows one to serve nature by overseeing plant growth by balancing the climate. Many methods have shown an important role in the variety of uses, such as medical, security, etc. Farming, remote sensing, market research, etc. The use of automated simulation tools to mimic human visual capacities has proved a dynamic function of smart or precision agriculture. This principle has allowed for the automated control and observation of seeds, planting, disease control, water conservation, etc. to improve seed production and efficiency. In this paper, we reviewed several publications that follow the principle of machine learning, deep learning, soft computing and digital image processing (DIP) approaches for the detection and classification of plant diseases.

India is one of the largest agricultural producing countries in the world where about 58% of its population's livelihood is dependent on it. The traditional method of agriculture requires a lot of human effort but then also we cannot assure the maximum yield from the crops due to various environmental factors. With the help of the Internet of Things - IoT we can modernize the traditional methods of farming and control the environmental factors. This not only helps the plants to get the maximum yield but also reduces a lot of human effort. This proposed system mainly depends on the information which is retrieved from the sensors as an automatic watering system waters the plant according to the data retrieved from the temperature and moisture sensor of the system and can be helped in reducing the water wastage.

A common disease that is affecting the whole world is diabetes, and is also known as a silent killer. Diabetes affects the nervous system, retina of the eyes, kidney, heart and affects the entire system of the body. Diabetes boosts the other diseases also whenever patients suffer from them otherwise it remains in sleep mode. The foremost reason for boosting diabetes is the lifestyle of today’s generation. In this paper, we have tried to predict diabetes at the primary stage by making use of a neural network i.e., multilayer perceptron. We have made use of the PIMA Indians diabetes dataset in our article and the experiments were performed using the Weka tool. After applying the proposed algorithm, the experimental result shows an increase in accuracy by 3% which is far better for predicting diabetes.

Ad-hoc network is a kind of temporary network used to establish connections for a temporary purpose. The ad hoc network is a kind of wireless network. This is not an infrastructure-based network. Mobile ad-hoc networks and Vehicular ad-hoc networks are two popular areas of ad-hoc networks. VANET is the most popular network nowadays. VANET is the network on wheels i.e. Vehicle is considered as a node; there is no issue of power management in this network, unlike MANET networks. Further to increasing the efficiency at such high speed, various issues and challenges occur in the path of this self-organized network. VANETs present the largest real-time application but lack security, scalability, efficient routing, and clustering protocols. The main aim of the study is to focus on various issues and challenges related to security requirements in VANET, which will allow researchers to work in this field for increasing the reliability of the network. Also, various attacks are focused which show the threats in the vehicular networks because of their dynamic topology.

Abstract: There is rapid development in the field of image processing. Now, there are a lot of models for face recognition in the whole world. In the field of image recognition, the arrival of deep learning theory has evolved drastically. In today’s world, biometric is used everywhere and the expectations from the system are that it provides positive results in any kind of situation as the quality, alignment, facial expression, and reflection of the picture make it difficult for the machine to validate it. Thus, there is an alternative model to the traditional neural network model which is Convolutional Neural Network (CNN) models which are deep learning models. This model includes the process in which at first the machine is trained with the data set and then the validation is done.

Demands of Cloud computing have been increasing these days and becoming more prevalent in the field of Information Technology. It has been able to provide solutions to various markets in the IT sector. In this paper, we will discuss a cloud computing case study on Aadhar cards that were to be provided to all the citizens of India. This major project included cloud computing technology and due to handling of confidential data, the security needed to play a vital role. Though the cloud is a secure platform there are still some challenges that need to be encountered for the data to be saved from external and unethical attacks. So this paper discusses some solutions to the given problem of handling security on the cloud and how to overcome challenges and issues faced on the cloud.

Machine learning-based classification of breast cancer and its detection is possible without toxic therapy by a well-trained model. The machine learning model detects features and patterns form the data sets that used when training model which is useful for detecting tumor and classify whether it is a benign or malignant and this process simplifies the cancer detection and gives results accurately at a faster rate when compared to the other traditional methods like Magnetic resonance imaging (MRI), Coronary artery disease (CAD), Modalities using ultrasound, etc. Here I am proposing a new technique through which breast cancer can be easily detected by a proper training model with the help of few classifying algorithms in this research a good set of data is used for training classifier machine algorithms in Microsoft azure by comparing all those five algorithms accuracy and working these are the five algorithm models are 2-class Support vector machine, 2-class Neural Networks, 2-class Boosting Tree, 2- Class Logistic Regression, 2-Class Bayes Point and acquired better results which can lead and helpful for detecting cancer in future by using machine learning and deep learning techniques.

Detection of Facial expressions and emotions is always an easy task for humans but to achieve the same task using different computer-based algorithms is a challenging task. It is possible to detect emotions from images using various machine learning algorithms as there is a huge advancement in computer vision and machine learning over the years. Programmed face appearance acknowledgment is an effectively arising research in Emotion Recognition. In this paper, the Convolutional Neural Network (CNN) which is a subset of AI is rehearsed as a way to deal with outward appearance acknowledgment tasks. Thus, the proposed method is found to be more effective than other methods and has an accuracy of 92. Face appearances are the vital qualities of non-verbal correspondence. Non-verbal explanations are imparted through outward appearances. Face looks are the delicate indications of the greater correspondence. Nonverbal correspondence implies correspondence among people and animals through the eye to eye association, signals, outward appearances, non-verbal correspondence, and paralanguage. Human facial expressions can be recognized by using deep learning.

Global energy demand is expected to climb about 25% by 2040. This will be a challenge for the national power grid. Distance between generating stations and consumers is also another concern that may lead to more line losses and reducing the efficiency of the power system. This increasing demand leads to complexities in a national grid with increased demand for reliability, security, and environmental concern. The solution to the above challenges and requirements is the new structure of the power system known as the “Nano grid”. A nanogrid can be considered as an electrical generation and distribution system, which is a building block of small loads, two or more distributed generations (DGs), and the ability to connect or disconnect from the utility grid. In this article, an overview of Nano grid technology with its advancement and the prospect has been presented.

The foundation aim of these Projects is to increase the storage security of bank lockers based on fingerprint Sensors and Shocking mechanisms with the help of a One-time password. These systems may help the bank to attract more customers by the use of high security and smart locker system. In this framework, there is a requirement to verify individuals to recuperate the reports or cash from the storage spaces. In this security framework, unique finger impression and OTP are utilized. The first individual’s fingerprint and mobile number are registered with the system. After that whenever a user will scan their finger with the system, it matches the stored print with the print received after scanning. In case the print matches at that point four-digit code will be sent on approved individual versatile to open. So biometric and OTP security are points of interest than other frameworks. This system will have an attached vibration sensor, to detect in case someone tries to hammer or break the locker. On detection of any such activity alert, there will auto-generated messages will send to high authority persons on individual’s number. Also, the system goes to freeze mode and is activated only when a valid finger is scanned.

The concept/phenomenon of operons, which are organized genes that work in a coordinated way in microbes, is well established. Recent developments in genetics, biochemistry, and bioinformatics have unraveled similar gene arrangements in plants. Here we aim to develop an algorithm/tool which would help us detect and identify biosynthetic gene clusters (BGCs) from any input plant genome. Through this tool, we intend to match or supersede the performance of pre-existing sting tools for BGC prediction, like the popular plantiSMASH. The predictions models were developed using the machine learning tool WEKA using the physicochemical properties as data set to classify between terpene synthases and non-terpene synthases. A set of ten physicochemical properties were selected and their values were predicted for each of the 159 proteins (terpene synthases and non-terpene synthases) Employing the random forest and SMO classifiers, we were able to obtain significantly promising accuracy of over 90 percent with 66 percent percentage split testing. Accurate prediction of BGCs in the plants, especially the major food crops like rice, wheat, and corn revolutionize farming and nutrition for the better.

Papaya is broadly known for its taste and medical advantages yet very few individuals know about the massively helpful Papaya seeds that are by-products of fruit and are generally discarded. These small round black seeds are edible and useful for our wellbeing whenever taken in a restricted amount. Their reuse will be useful not only for the economy and environment rather; it will prove to be a ray of hope for the food industries engaged in the search for plant-based food ingredients to enhance the nutritional status of functional foods. In this review, we have discussed the composition, medicinal properties, its utilization as a nutritive agent, and a promising preservative.

A new method encountered for securing cryptocurrency i.e cryptographic algorithms for example Secure Hash Algorithm (SHA-2) and Message Digest (MD5). It uses Blockchain technology to make the transactions secure, transparent, traceable, and immutable. This is the reason cryptocurrencies have gained popularity in almost all sectors, especially in the financial sector. Cryptocurrency price prediction has become a trending research topic globally. Many Machine Learning algorithms have been developed such as Linear Regression, SVM, Random forest, and Facebook Prophet. Facebook Prophet is a time-series forecasting model for predicting the future price of bitcoins. In this paper, Facebook prophet Model is used, and two cryptocurrencies are considered, namely Bitcoin and Litecoin. The result depicts that FB prophet Model accurately predicts the prices of bitcoin cryptocurrencies. We considered the data from yahoofinace.com for BTC-USD and LTC-USD.

Canine Circovirus (CaCV) or Dog Circovirus (DogCV) causes hemorrhagic gastroenteritis, thrombocytopenia, vasculitis, hemorrhages, and neutropenia and is usually found as co-infection with other canine infectious agents like Parvovirus and Distemper virus in dogs including wild canines (wolf, fox, badger). To know the evolutionary feature of the viruses, nucleotide composition and Codon usage bias (CUB) have been used which further ascertain their adaptability towards the suitable host. In the present study CUB and Nucleotide composition of both cap & rep gene of around 88 CaCV strains were compared. Nucleotide composition of CDSs at 3rd codon position (G3% A3% T3% C3%) together with overall AT% and GC%, GC12, and GC3 were analyzed. using codon W 1.4.2 and CAIcal server, Aromo, and Gravy values, effective no of codons and relative synonymous codon usage values were also analyzed. The results show that CaCV has an AT-rich genome and codons ending with A/T have to preference over GC ending codons. Nc-GC3 plot of CanineCV reveals that selection pressure was dominant over mutation pressure. Correlation analysis between Gravy, Aroma, and CAI indicate natural selection over mutational pressure. The RSCU values of Cap & Rep genes were analyzed to find out overrepresented codons.

Patients suffering from COPD have decreased thoracic mobility which affects the functioning of the respiratory muscles. As the literature on thoracic mobility in COPD patients is scarce the present study focuses on assessing thoracic mobility in different grades of COPD with the help of a digital inclinometer. A total of 59 subjects were included in the present study (37 COPD and 22 Controls) and subcategorized into 4 groups according to the severity (GOLD’s Classification). Thoracic mobility on the left and right sides, both were found to be less in the COPD group as compared to the controls. There was a statistically significant difference for the left side thoracic mobility (p=0.00) between both groups with the mean value of 35.38 ± 6.92 for the COPD group and 44.49 ± 3.92 for control. There was a statistically significant difference for right side thoracic mobility between both groups (p =0.01) with the mean values of 40.08 ± 9.28 in the COPD group and 44.43 ± 4.22 for the control. The right and left thoracic mobility was found to be decreasing with increasing severity but the difference was not statistically significant. There is an alteration in the respiratory mechanic and shoulder girdle kinematics which alters the thoracic mobility. In the present study, the sample size for each subgroup of COPD was less. The mild and severe COPD subgroup had a limited sample and hence more studies should be conducted to understand the changes that happen in thoracic mobility due to an increase in severity. The thoracic mobility is affected in COPD patients and with increasing severity of COPD, the thoracic mobility decreases in this population.

The most popular antenna for portable devices in current communication technologies is the wearable antenna due to its compactness and flexibility; demand was rapidly growing and can communicate through signals with the human body and the wearable devices. The advantages of wearable antennas are flexible, hidden, low profile, and no harm to humans. The key benefit of this antenna is that it is placed on the human body or included in clothing, effortlessly transmits, and receives signals through clothes or on-body. These antennas play a vital role in the number of applications, viz. navigation (118MHz to 137MHz), medicine (750MHz to 2.6GHz), military (225MHz to 400MHz), RFID (433MHz to 5.4GHz), physical training, tracking, and health monitoring, etc. This paper discussed the important aspects of wearable antennas, which include materials used, substrate, and fabrication techniques. Next, discussed a clear overview of wearable antennas existing and design aspects, their advantages, and drawbacks.

Wireless Sensor Network Lifetime improvement is very well demonstrated using sink mobility effectively in the literature. The challenge taken up in the past research was to identify the shortest route that avoids obstacles and delivers the mobile sinks at the designated nodes. In this paper, we present a cluster-based approach for collecting the data and a heuristic algorithm for a well-planned work. The routing protocols may differ from application to application, due to a large number of sensor nodes the algorithm should be studied in a novel way. In this method, the nodes known as cluster heads collect information from different cluster points and sends data to the mobile sink following this clustering process. We also propose an energy-efficient data gathering algorithm for the collection of mobile sinks. Simulations were conducted using NS2 software to verify the efficiency of the proposed algorithm.

Coronavirus (COVID-19) disease is spreading rapidly and is becoming increasingly common every day. This study can be helpful in the basic steps for doctors to diagnose the diseases and treat the patient accordingly. We used 1200 images of COVID-19,1300 Normal images and 1345 Viral Pneumonia images for the research work. We compared three popular deep learning methods which were CNN, VGG19, and Resnet-18 Model. We found that the Resnet-18 model with different useful parameters outperforms all the other proposed models. The accuracy we got was 97% when 50 images from each category were being tested.

The Internet of Things (IoT) is a developing system of articles, gadgets, and types of machinery each ready to interconnect with the other utilizing a remote system to get to the Internet. These frameworks permit users to achieve further mechanization, examination, and integration inside a structure. IoT gadgets have an adaptable scope of both wired and remote availability choices. IoT conventions generally use Industrial Scientific & Medical (ISM) band 915 MHz, 2.4 GHz (Zigbee), 5GHz. Right now, a short review of IoT highlights has been given in the paper. Likewise, a triangular MIMO reception apparatus (antenna) with a truncated edge has been intended to work at the frequency of 2.4 GHz (Zigbee). The proposed reception apparatus has been structured utilizing FR4 material having a dielectric constant of 4.4 and loss tangent 0.002. The thickness of the substrate is taken 1 mm. The proposed reception apparatus is reverberating at 2.4 GHz, which is reasonable to be utilized at Zigbee. The simulated Gain of the proposed antenna is 1.18 dB, its radiation efficiency is approximately 30%. All the simulated parameters such as multiplexing efficiency (ME), envelop correlation coefficient (ECC), and mean effective gain (MEG) is fulfilling the requirement to be used as MIMO antenna for IoT applications.

Forest fires are a great threat to ecologically healthy grown forests and the protection of the environment. The root cause is due to the lack of a scalable network to monitor the physical conditions over vast forest areas. To overcome this, we used the NRF24L01+ module configured in mesh network mode interfaced with Arduino along with DHT22, MQ2 sensors, powered by Lithium-ion batteries which can be recharged with solar panel, this allows the node to reconnect to the network automatically in case of any damage to its parent node.

The earth holds several natural resources in itself. Water is the most crucial resource among them. However, anthropogenic activities in the proximity of water bodies are leading to water pollution. The primary concern is the textile industry, where water consumption and environmental risk are significant. The manner the water reaches into the water body is of primary concern as no proper treatment is done in many cases. The review mainly focuses on the use of microorganisms for degrading textile dyes in water through bioremediation. Among the different methods being explored for dye degradation, bioremediation is one of the most promising. It is easy to alter and more economical when applied to a commercial scale. Bioremediation is also a sustainable solution that can be harnessed at a large scale and for several generations. However, microbes with specific new biotechnological applications such as nanobiotechnology can give better results. Still, significantly less literature is available on this subject matter. Microbes are a powerful alternative for dye degradation as several bacteria and fungi can degrade many acidic and basic dyes in short periods. The effluent obtained at the end of the process does not have a tertiary effect, making these microbes an efficient choice for dye degradation.

Non-adherence and non-compliance to the course of psychiatric medications affect the standard treatment plan of a patient. Inability to monitor the correct time and dosage also results in lesser efficiency of the drug and its action mechanism. Factors that may cause instability and non-compliance with the treatment plan are the possible side effects of drugs or the ease of use. With technological advancements in the field of drug delivery, transdermal patches, while Being non-invasive, ensure proper dosage and delivery of drugs through the skin, minimizing the first-pass metabolism. This review examines the existing literature, working mechanism, preclinical studies, and advancements in the application of transdermal patches in psychotropic drugs and deaddiction while evaluating various psychiatric disorders and comparing their efficacy and remission rate concerning standard oral treatment. It also addresses the challenges, drawbacks, and strategies required to increase its efficiency in clinical use.

Undeniably, public precincts are the stage that unfolds urban life every day, they foster social & economic bonds, bringing people together. Brick & Mortar retail market along streets is one of major mode for retail shopping in a city, in addition, these places play a pivotal role for connecting people with the physical environment and this is not a recent phenomenon but B & M retail market along streets have played this role since the origin of towns and cities. But, in recent past, rather than pedestrian there have been more flow of private vehicles in these public precincts. The position is deteriorating day by day as private vehicles are growing drastically, due to which on one hand quality of life is degraded and on other hands the aspects of public spaces had undermined in several cities. Thus, there is an immediate demand to give attention to all users at these places, so that they can stroll, communicate, shop, and comply with other social acts that are important for sustaining socio-culture aspects of cities. Along with tangible, intangible characteristics also play a pivotal role in maintaining the social & culture of any area, and as retail depicts one of the important elements of urban development so these non-physical characteristics should be incorporated in retail planning. This paper will provide insights to make planning parameters that can revive footfall in B & M stores and increase user satisfaction while physical shopping. It will also help in addressing sustainable social and cultural aspects of the city and its impact on users.

With the increase in population, usage of vehicles is also increasing at an alarming rate. People working round the clock and earning handsome amounts usually prefer four-wheelers to ease their journey from one location to another location. But this ease has now created a lot of problems as now more traffic jams can be seen than before and traveling time is again increasing for people bound with deadlines. So, now people have moved from four-wheelers to either two-wheelers or by foot. Also, while traveling to unknown places and roads, GPS is a must nowadays. But on two-wheelers, it is tough to use GPS on mobile phones. Moreover, the shoe is the necessity of life and when it comes to a blind person shoes can help to protect from pebbles and roads but obstacles cannot be avoided. So, we are trying to make it more convenient for blind people as well, so that with the help of sensors in front of shoes he/she will be indicated with vibrations to avoid the obstacles in the path. we are trying to make it more efficient by using new technology i.e., Smart shoe where information of path is retrieved from Google navigation database, and accordingly, the instructions are sent to the sensors installed in the shoes to take turns (i.e., U-Turn, left, right, etc.). All this can be achieved by interfacing the Bluetooth sensor placed in shoes and it would give signals to 4 sensors placed in four directions in shoes and when to take any specific turn it would vibrate. Also, this signal is provided to the Bluetooth sensor from a mobile application which will be connected to the Google navigation database.

Abstract: The lives of people have been better since the discovery of medicines. Experts were able to discover a cure for even a few chronic diseases like cancer. Scientific studies from multiple areas, including biology, chemistry, and biotechnology, have created a diverse array of solutions to the issues faced by humans in health and medicine. The usage of microorganisms to make valuable products that are important to treat various diseases and improve people's lives by using it in many industrial levels like food, pharmaceutical, and agricultural sectors has made microbial species that include bacteria and fungus, etc., ever-demanding. Research studies from the past few decades proved that many bacteria, including myxobacteria, have been used as a potent source for deriving lots of compounds that could be useful for humans like antibacterial, antifungal, anticancer agents, and enzymes that can degrade cellulose, protein, etc. Since it's a proteobacterium commonly found in organic dwells and swamps, it would be feasible to cultivate this bacterial species to produce chemical compounds with more incredible industrial applications. This review will explain why myxobacterium is considered a potential source for the production of industrially important enzymes and many other beneficial secondary metabolites. Also, this review will shed light on various ways to screen and characterize the myxobacterial population to produce cellulolytic enzymes.

Bone tissue engineering is a stimulating way to directly repair and engineer the defected/damaged bone tissue by omitting the less efficient techniques used in conventional practices. Biomaterial plays a cardinal role in providing support and extracellular environment for cell proliferation, differentiation and enhances tissue regeneration. The traditionally used bone graft has been substituted by the tissue engineering technique, due to their minimal pathogen transmission property. As there have been some challenges and restrictions, bone tissue engineering has not been a hot topic in clinical practices. The aims and objective of this paper are to review the current approaches in the developing field of Bone tissue engineering and find a better substitute as an implant device for the welfare of society.

Software effort estimation is an important part of the software development process as it strives to determine the success or failure of the project. The success of the project is entirely based on the prediction accuracy of the software effort estimation. There has been a major challenge in agile methodology adoption which is effort estimation but the conventional means of estimating the effort mainly results in inaccurate estimates so we need to follow an appropriate model approach. This paper is reviewed to focus on effort estimation in agile projects which is related to story points that help to prioritize user stories for faster deployment of the project and to review different hybrid models that are used to predict the effort. We reviewed the accuracy parameters based on three popular agile datasets and found that the Deep Belief Network-Ant Lion Optimizer (DBN-ALO) model works efficiently for all the datasets and outperforms all the other proposed hybrid models. Different techniques can be used for minimizing the effort estimation so that the tasks can be done efficiently.

Every human wants to be safe and secure health while driving. Here the big role comes for technology. Technology needs to improve a lot because we can't take risks in life. The life of human beings is very precious. The main reason for death during driving is attention. During driving, we all should be more careful and should be alert every time. The reason behind making this project is to rescue the people when they are in an emergency and to give them a medical rescue team via sensors and GPS, GSM, MPU6050 consists of gyroscope and accelerometer. Here Machine learning part is used for theft detection using accessing a local camera. And also this project will help to find our lost or stolen vehicles in India.

Diabetes Mellitus is a heterogeneous metabolic disorder with various aetiologies that are characterized by chronic hyperglycemia. DM is a consequence of defective metabolism in the body was either cells fail to secrete insulin or insulin per se is inactive. Insulin is a pancreatic hormone responsible for glucose uptake and utilization by cells for energy production. Thus, insulin deficiency renders the cells unable to uptake and utilize glucose for metabolism. DM is majorly categorized into two conditions: Type 1, In this case, there is no enough insulin production by the pancreatic cells (Insulin-dependent). In type II, despite insulin production, the cells fail to respond to the hormone (Insulin Independent), the reason behind the former is not yet clear, but the latter is studied to be a result of obesity or inactivity. Thus, as a consequence of an error in glucose metabolism, it leads to comorbidities, with high glucose concentration in the blood causing damage to the circulatory and excretory system. This condition becomes life-threatening without proper medical care. Type 1 can be treated with an external source of insulin provided to the body. Type II is treated with hypoglycaemic drugs. Along with medicine, other options include an improved lifestyle with proper nutrition and exercise. The number of diabetes patients has been increased today. In the present review article, we have discussed the research reported to show medicinally important plants and plant-derived compounds which have been shown to affect insulin production and glucose intake and utilization in type 2 diabetes patients.

It is a common saying in the security industry that the “S” in RFID stands for “Secure”. An RFID Tag/Card dumps all the information it contains when brought close to the RFID Reader. The RFID Card is therefore considered unsuitable for storing sensitive data. Our project aims to find a way of adding a layer of security to the RFID Card using biometric authentication, which would widen the scope for the use of RFID technology in areas where they couldn't have been used before, such as contactless payments.

India is famous for its cultural legacy and its artisans and artistic heritage. Himachal Pradesh has always been known worldwide for its culture, styles, and workmanship. Bamboo is the most versatile and sprightly growing perennial grass. It is the fastest-growing resource that is getting recognition in all the states of India but has shown a very low pace in Himachal Pradesh. The state which is a hub of artists and bamboo resources is not able to utilize the resources and meet consumer demand. The prominent concerns in Himachal Pradesh are inadequate technological know-how & facilities; lack of awareness amongst artisans regarding the latest trend of bamboo furniture and design; lack of promotion of bamboo craft and furniture. The artisans and craftsmen are facing many challenges due to these impediments. Bamboo is an exciting design resource and has many positive attributes such as rapid growth, high strength, and ease of manipulation using simple tools with low investment and can act as a supportive measure to enhance the current status of the artisans in Himachal Pradesh. The removal of poverty and social tensions which Himachali craftsmen are facing, bamboo furniture development and productions can play a major stroke to protect the heritage legacy [1]. The focus of this paper is to find the current status of the bamboo handicraft and furniture industry in this highly tourist-oriented state of Himachal Pradesh, the gaps and challenges faced by all the stakeholders in the industry, and the development policies which are needed to produce a better source of handicraft and furniture to meet consumer demand.

Medicinal plants have great importance these days and the better way to provide medicines is to give them as food. Mushrooms are one of the good sources of food that has good nutritional and therapeutic value. They are the constituents of a good diet which boosts up the immune system and helps to fight diseases like cancer. From ancient times people of China believed that the mushrooms have anti-aging properties and they had been consuming mushrooms as their important diet. Along with the anticancer and anti-aging properties, they increase brain activity. One major advantage of mushrooms over other vegetables is their growth period, i.e., they can grow around the year while many vegetable plants are seasonal. The present review article is focused on the nutritional and therapeutic properties of button mushrooms in more detail, which will help the researchers working in this area to design further experiments and explore more on this plant.

The Indian bay leaf (Cinnamomum Tamala) plant is found in high altitudes along the tropical and subtropical regions of India. This plant is very useful to be used as a food ingredient and as a medicine for centuries to treat several ailments. The leaves are traditionally used in the Indian household as a spice in food and even as a mouth freshener and deodorant. This is because the leaves have a pepper-like aroma which is caused due to the essential oils present in the leaves and the bark. It is rich in alkaloids, flavonoids, terpenoids, polyphenols, etc. These phytochemicals are responsible for their aroma, flavor, and medicinal properties. The plant has therapeutic properties against various types of diseases. Due to its antimicrobial properties, it has been used as a preservative in the food industry. The present review discusses in detail the phytochemical and therapeutic properties of C. Tamala, along with its tremendous potential to be used as a medicine.

Perhaps the contact that is key to coordination and interaction across UAVs (Un-named Aerial vehicle) has been some of the main marketing difficulties regarding mega-UAV (Interplanetary aerospace aircraft) platforms. If any of the UAVs were daisy-chained with the node or maybe a land base network, the UAV may interact via the infrastructure. Moreover, the mega-UAV device functions are limited by such a network-dependent interaction design. The issues of a completely infrastructural UAV system could be solved using ad-hoc networking within UAVs. The following study discusses flying ad hoc networks (FANET), an ad hoc network that links UAVs.

Automation hypothesis and strategies has increased global productivity in the last few years of worldwide competition. It influenced a great variety of enterprises on the manufacturing side through the decline of production time, superior system performance, and process control. The main purpose of today’s manufacturing system is to upgrade productivity. The universe is full of technologies that pressure an elevated amount of production, particularly in industries where automation is needed. To proceed towards automation, the recent trends in all the industries are required to cope up with new technologies. The same vision is applied in water bottle filling plants, to meet the customer demands and to speed up the filling of bottles. Now the process of filling bottles is conducted by PLC in a large number of production units. Due to the high cost of the PLC machine, a filling is yet performed manually in small manufacturing units. The manual filling usually leads to imperfection in the operation of filling and spikes up the cost of labor. The prime purpose of our project is to design and develop an automated electro-mechanical bottle filling system, that can reduce the cost for small-scale en6terprises and assist them to build up an automated factory. The project proposes aspects of computer, electronics, and mechanical i.e., designing and modeling, schematic circuit prototyping and programming, sensor and actuator application, project planning, and presentation skills.

Advanced internet technology contributes to several new apps due to wireless communication technology. Mobile Ad-hoc networks are one of the most productive areas of wireless internet technology development. The cellular ad-hoc channels have now become one of the most competitive and successful communications and connections as their usage has increased considerably over the last few years. A remote ad hoc channel is an independent set of portable devices that interact through wireless networks and collaborate in a decentralized way, in the absence of fixed facilities, in an attempt to provide the communication capabilities required. This article addresses a summary of MANET and its use in devices. This article provides a brief analysis of the styles and benefits of MANET implementations in wireless transmission channels. This kind of network, which acts as an isolated channel or one or more places linked to cellular networks or the Internet, paves the way for various exciting new technologies. This article also gives an overview of the possible applications of ad hoc channels, complex assaults, and addresses the technical problems facing procedure architects and channel designers.

One of the latest emerging trends, the flexible way of communication in an entertainment manner is through emoji (smile emoji, sad emoji, etc), many top companies like Facebook, Snapchat, Instagram, and many more are using this type of trend but now also they are in progress of this trend for the creation of avatar for the virtual world. Therefore we will discuss the backend of this type of work creation, different technologies used for the implementation, all the set of algorithms, and past, present, and future of the Face emoji creation (FEC) and Face emoji detection (FED) with the review in avatar formation. We get to know different advantages and the disadvantages related to the above technology. One of the interesting things for everyone is the avatar of their face. So, we get to know the concepts of AC (avatar creation) with expression oriented and how it is going to be related with the virtual world, which is going to be experienced by the future generation.

Connectivity over the internet is drastically increasing which is the main factor for developing different generations in the mobile network. Earlier, the usage of the internet was primarily through smartphones but now with the invention of IoT devices, the shift has been changed into different sectors like healthcare, agriculture, infrastructures, and vehicles using this internet. With this shift the demand for bandwidth, connectivity has been increased. And can be resolved through 5G network slicing. In this paper, we proposed a slice isolation model for the security of 5G network slicing also it will help users utilize the characteristics of network slicing to the fullest. Our proposed model uses a Machine learning algorithm to perform slice isolation.

QoS is an important component of any operation, but it is particularly important in wireless transmission systems. To improve network strength and data transmission, we need to use a network that can provide the most benefits to its end users. This paper detail simulates the CSMA/CA protocol in DCF mode without using virtual channel sensing (i.e., RTS/CTS frames). In this article, a comparison of the performance of an IEEE 802.11e network is made, and the results are compared to improve the network's efficiency.

Many techniques are laid out that try to protect the web application from unauthorized access with the growing concern for web security. To uncover the possible inconsistencies that can damage the application, new approaches have been introduced. Vulnerability scanning is the most widely used technique. By the term vulnerability, we meant the possible device flaws that make it vulnerable to attack. Scanning these vulnerabilities in the system provides a means of identifying and developing new strategies to protect the system from the risk of damage. This paper focuses on the use of a remote vulnerability scanning framework- “SANJAY” which completely and comprehensively scans the given target most effectively and easily with the help of integrated tools like SLACK and VPS.

Customer Relationship Management (CRM) is a cloud-based technology that helps businesses to interact with their customers and potential customers. CRM makes easy access to shared computing resources with less management effort. Salesforce is an advanced and efficient CRM Solution. In this paper, we are discussing cloud computing, features of a good CRM tool, Salesforce as a CRM solution, analysis of Salesforce growth with other CRM providers, and the Salesforce CRM in COVID 19 insight. This paper aims to provide an extensive study of CRM using Salesforce and to substantiate the efficiency of Salesforce with other CRM providers like Adobe, Microsoft, and Oracle, etc.

In the pursuit of lower and more stable energy expenses coupled with lower carbon emissions, the industry is moving towards the integration of low-cost renewables. This can be achieved through a range of technologies and/or mechanisms, each with its own set of advantages and disadvantages. One potential solution to achieve low-cost, predictable, and low-carbon process heat and power is the coupling of renewable electricity with thermal energy storage backed up by renewable fuel. This combination takes advantage of the low-cost but variable renewable generation coupled with the dispatchability of thermal energy storage with robustness added by utilizing a higher cost but low volume renewable fuel. With this approach, a lower overall cost, dispatchable, robust, and carbon-neutral solution to process heat and power is realized. Therefore, in the current study, several combinations of renewable technologies coupled with electrically charged thermal storage (ECTES) and renewable fuel were designed and simulated to meet the process heat needs of two hypothetical scenarios. The solutions were then evaluated based on their economic (levelised cost of heat and power) and environmental (carbon emission avoided) merits to determine feasible solutions. Of the two studied cases, replacing diesel for heating at a mine site with solar PV, wind, ECTES, and renewable diesel delivered significant financial and environmental benefits. Unfortunately, at today’s prices replacing natural gas heating with solar PV, ECTES, and biomethane is not economically feasible without further cost reductions and/or a carbon tax.

With the advent of modern technologies in concentrated solar power(CSP), research focus has moved to cost reduction to make CSP a competitive alternative to the conventional heat source for power generation. One way to achieve this is by increasing the storage temperature so that size of thermal storage is lower which leads to reduced engineering and maintenance costs. To incorporate the CSP into power generation, one way to reduce the cost is to employ the higher temperature to improve the efficiency of the supercritical carbon dioxide Brayton power cycle. In the current study, a variety of phase change materials (PCMs) and graphite and their combination were assessed. For efficient heat transfer between PCM and heat transfer fluid, a shell and tube configuration was assessed as a suitable arrangement. The storage mediums can be contained in four indirect shell-and-tube configurations, including 3-PCM and 5-PCM cascade storage, PCM-graphite-PCM hybrid storage, and a single graphite storage tank. The sizing and design of the TES systems were performed by using a dynamic cycling methodology based on a transient 2D numerical model. The cost of these TES designs configurations was determined by using an economic model. This work also investigates the impact of some geometric parameters and cost assumptions on the techno-economic performance of the TES system. The analysis suggests a scenario exists whereby a low efficient storage system with less tube or lesser storage material could be more cost-effective. Overall, the cost of hybrid TES is the lowest among all studied systems, $26.96/kWht and $21.49/kWht for charging temperatures of 720 °C and 750 °C, respectively, followed by the 5-PCM storage of $28.06/kWht and $21.82/kWht.