Dr. A.P.J. Abdul Kalam Technical University

In this paper, a detailed experimental study of face detection algorithms based on ”Skin Color” has been made. Three color spaces, RGB, YCbCr and HSI are of main concern. We have compared the algorithms based on these color spaces and have combined them to get a new skin color based face detection algorithm which gives higher accuracy. Experimental results show that the proposed algorithm is good enough to localize a human face in an image with an accuracy of 95.18%.

Microbes are unique creatures that adapt to varying lifestyles and environment resistance in extreme or adverse conditions. The genetic architecture of microbe may bear a significant signature not only in the sequences position, but also in the lifestyle to which it is adapted. It becomes a challenge for the society to find new chemical entities which can treat microbial infections. The present review aims to focus on account of important chemical moiety, that is, pyrimidine and its various derivatives as antimicrobial agents. In the current studies we represent more than 200 pyrimidines as antimicrobial agents with different mono-, di-, tri-, and tetrasubstituted classes along with in vitro antimicrobial activities of pyrimidines derivatives which can facilitate the development of more potent and effective antimicrobial agents.

Gymnema sylvestre (Asclepiadaceae), popularly known as "gurmar" for its distinct property as sugar destroyer, is a reputed herb in the Ayurvedic system of medicine. The phytoconstituents responsible for sweet suppression activity includes triterpene saponins known as gymnemic acids, gymnemasaponins, and a polypeptide, gurmarin. The herb exhibits a broad range of therapeutic effects as an effective natural remedy for diabetes, besides being used for arthritis, diuretic, anemia, osteoporosis, hypercholesterolemia, cardiopathy, asthma, constipation, microbial infections, indigestion, and anti-inflammatory. G. sylvestre has good prospects in the treatment of diabetes as it shows positive effects on blood sugar homeostasis, controls sugar cravings, and promotes regeneration of pancreas. The herbal extract is used in dietary supplements since it reduces body weight, blood cholesterol, and triglyceride levels and holds great prospects in dietary as well as pharmacological applications. This review explores the transition of a traditional therapeutic to a modern contemporary medication with an overview of phytochemistry and pharmacological activities of the herb and its phytoconstituents.

This paper develops an effective encryption and steganography-based text extraction in IoT using deep learning method. Initially, the input text and cover images are separately pre-processed. DCT (discrete cosine transform) is utilized to transfer the image from spatial domain to frequency domain. Then, the original text is encrypted using new optimized equilibrium-based homomorphic encryption (OEHE) approach. Next, the extended wavelet convolutional transient search (EWCTS) optimizer with quotient multi-pixel value differencing (QMPVD) is developed to embed the secret text in cover images. Then, at receiver side, the reverse process for encryption and steganography is executed with secret key provided by the sender. Finally, the accurate text is extracted at receiver side using steganalysis process. The developed approach is executed in MATLAB software. The various evaluation metrics are used to authorize the effectiveness of suggested approach. Simulation outcomes proved that the suggested technique provides better outcomes than other existing approaches.

Today is the era of Cloud Computing Technology in IT Industries. Cloud computing which is based on Internet has the most powerful architecture of computation. It reckons in of a compilation of integrated and networked hardware, software and internet infrastructure. It has various avails atop grid computing and other computing. In this paper, I have given a brief of evaluation of cloud computing by reviewing more than 30 articles on cloud computing. The outcome of this review signalizes the face of the IT industries before and after the cloud computing.

Parkinson's disease (PD) has affected millions of people worldwide and is more prevalent in people, over the age of 50. Even today, with many technologies and advancements, early detection of this disease remains a challenge. This necessitates a need for the machine learning-based automatic approaches that help clinicians to detect this disease accurately in its early stage. Thus, the focus of this research paper is to provide an insightful survey and compare the existing computational intelligence techniques used for PD detection. To save time and increase treatment efficiency, classification has found its place in PD detection. The existing knowledge review indicates that many classification algorithms have been used to achieve better results, but the problem is to identify the most efficient classifier for PD detection. The challenge in identifying the most appropriate classification algorithm lies in their application on local dataset. Thus, in this paper three types of classifiers, namely, Multilayer Perceptron, Support Vector Machine and K-nearest neighbor have been discussed on the benchmark (voice) dataset to compare and to know which of these classifiers is the most efficient and accurate for PD classification. The Voice input dataset for these classifiers has been obtained from UCI machine learning repository. ANN with Levenberg–Marquardt algorithm was found to be the best classifier, having highest classification accuracy (95.89%). Moreover, we compared our results with those obtained by Resul Das [“A comparison of multiple classification methods for diagnosis of Parkinson Disease,” Expert Systems and applications, vol. 37, pp 1568–1572, 2010].

The battery–supercapacitor hybrid (BSH) device has potential applications in energy storage and can be a remedy for low-power batteries and low-energy supercapacitors. Although several studies have investigated electrode materials (particularly for a battery-type anode material) and design for BSHs, the energy density and power density are insufficient (far from the levels required for practical applications). Herein, a hierarchical vanadium(IV) oxide on reduced graphene oxide (rGO@VO2) heterostructure as an anode and activated carbon on carbon cloth (AC@CC) as a cathode are proposed for fabricating an advanced BSH. The mixed valency of V ions inside the as-prepared VO2 matrix (V3+ and V4+) facilitates redox reactions at a low potential, giving rise to rGO@VO2 as a typical anode with a working potential of 0.01–3 V (vs Li/Li+). The sheet-on-sheet heterostructured rGO@VO2 yields a high specific capacity of 1214 mAh g–1 at 0.1 A g–1 after 120 cycles, with a high rate capability and stability. The rGO@VO2//AC@CC BSH device exhibits a maximum gravimetric energy density of 126.7 Wh kg–1 and a maximum gravimetric power density of ∼10 000 W kg–1 within a working voltage range of 1–4 V. Moreover, it exhibits fast charging times of 5 and 834 s with energy densities of 15.6 and 82 Wh kg –1, respectively.

BACKGROUND: Magnetic resonance image (MRI) brain tumor segmentation is crucial and important in the medical field, which can help in diagnosis and prognosis, overall growth predictions, Tumor density measures, and care plans needed for patients. The difficulty in segmenting brain Tumors is primarily because of the wide range of structures, shapes, frequency, position, and visual appeal of Tumors, like intensity, contrast, and visual variation. With recent advancements in Deep Neural Networks (DNN) for image classification tasks, intelligent medical image segmentation is an exciting direction for Brain Tumor research. DNN requires a lot of time & processing capabilities to train because of only some gradient diffusion difficulty and its complication. METHODS: To overcome the gradient issue of DNN, this research work provides an efficient method for brain Tumor segmentation based on the Improved Residual Network (ResNet). Existing ResNet can be improved by maintaining the details of all the available connection links or by improving projection shortcuts. These details are fed to later phases, due to which improved ResNet achieves higher precision and can speed up the learning process. RESULTS: The proposed improved Resnet address all three main components of existing ResNet: the flow of information through the network layers, the residual building block, and the projection shortcut. This approach minimizes computational costs and speeds up the process. CONCLUSION: An experimental analysis of the BRATS 2020 MRI sample data reveals that the proposed methodology achieves competitive performance over the traditional methods like CNN and Fully Convolution Neural Network (FCN) in more than 10% improved accuracy, recall, and f-measure.

Solar energy harvesting that provides an alternative power source for an energy-constrained wireless sensor network (WSN) node is completely a new idea. Several developed countries like Finland, Mexico, China, and the USA are making research efforts to provide design solutions for challenges in renewable energy harvesting applications. The small size solar panels suitably connected to low-power energy harvester circuits and rechargeable batteries provide a loom to make the WSN nodes completely self-powered with an infinite network lifetime. Recent advancements in renewable energy harvesting technologies have led the researchers and companies to design and innovate novel energy harvesting circuits for traditional battery powered WSNs, such as Texas Instruments Ultra Low Energy Harvester and Power Management IC bq25505 [see https://store.ti.com/BQ25505 for Texas Instruments (TI) Ultra Low Power Boost Charger IC bq25505 with Battery Management and Autonomous Power Multiplexor for Primary Battery in Energy Harvester Applications datasheets (2015).]. In modern days, the increasing demand of smart autonomous sensor nodes in the Internet of Things applications (like temperature monitoring of an industrial plant over the internet, smart home automation, and smart cities) requires a detailed literature survey of state of the art in solar energy harvesting WSN (SEH-WSN) for researchers and design engineers. Therefore, we present an in-depth literature review of Solar cell efficiency, DC-DC power converters, Maximum Power Point Tracking algorithms, solar energy prediction algorithms, microcontrollers, energy storage (battery/supercapacitor), and various design costs for SEH-WSNs. As per our knowledge, this is the first comprehensive literature survey of SEH-WSNs.

A deep neural network is used to develop a covert communication and textual data extraction strategy based on steganography and picture compression in such work. The original input textual image and cover image are both pre-processed before the covert text-based pictures are separated and implanted into the least significant bit of the cover object picture element using spatial steganography. Following that, stego-images are compressed and transformed(by using Leh Transformation) to provide a higher-quality image while also saving storage space at the sender's end. After then, the stego-image will be transmitted to the receiver over a communication link. At the receiver's end, steganography and compression are then reversed. This work contains a plethora of issues, making it an intriguing subject to pursue. The most crucial component of this task is choosing the right steganography and picture compression technology. The proposed technology, which combines picture steganography with compression and transformation, delivers higher peak signal-to-noise efficiency.

Abstract Air pollutants originating from various sources like vehicular emission, power stations, factories, refineries, industrial emissions, and burning of garbage in open and laboratories, include many toxic gases and pollutants like hydrogen chloride (HCl), hydrogen sulfide (H 2 S), and volatile organic compounds (VOCs) like benzene, toluene, xylene, and so on, ammonia (NH 3 ), carbon‐monoxide (CO), carbon‐dioxide (CO 2 ) and nitrogen oxides (NOx), and so on, that are constantly released into the atmosphere and continuously deteriorating our natural environment and surroundings. These pollutants create harmful effects on the ecosystem and affect human health. Though there are many devices available for monitoring these pollutants and toxic gases, they are very expensive and time‐consuming. Since the safety of the life of human health, plants, animals, and their surrounding area on topmost priority, thus there is a significant need to develop user‐friendly and environmentally friendly sensing devices for real‐time monitoring of air pollutant emissions, which are extremely hazardous to the environment, breathable air, and human health. Extensive research has been made by researchers to develop an ideal sensor suitable for the environment and which can be within the reach of the masses. This review article presents different types of gas sensors and the technologies on which they work upon. We have also discussed the different gas sensors with their principle of operation. The working, advantages, and disadvantages of each type of sensor have also been discussed and compared with each other. Despite extensive research over several years to develop a highly sensitive, selective, and quick response sensor for the detection of flammable and toxic gases, the future scope and perspective for scientists are also proposed at the end of this article.

The review introduced in this paper depends upon point by point survey of the Zeroed on the utilization of phase change material (PCM) for PV-Module. Thermal regulation & electrical efficiency improve the impact of high temperatures. On PV power age has been analysed and the discoveries have featured the significance of visible high temp. Guideline for PV-model. Different cooling technique utilized to keep up better PV execution are examined and the as of late arising PV-PCM framework idea for high-temperature guidelines is presented. A comprehensive paper review of best in class part of this innovation like framework improvement, execution, assessment material choice, heat remove improvement mathematical model, reproduction, and application in practice is given. The PVST-PCM system for example coordinated with a sunlight base warm (ST) system. Has subsequently been explored as the put-away intensity can be extricated for the warm application. The double PCM jobs exhibit huge application possibilities for consolidated innovation in any case. Both PV-PCM and PVST-PCM framework (system) are still mostly in the exploration and research faculty test stage, with clear extension for viable application yet with orderly difficulties. Ideas for the future work are introduced.

The 3D image segmentation is the process of partitioning a digital 3D volumes into multiple segments. This paper presents a fully automatic method for high resolution 3D volumetric segmentation of medical image data using modern supervised deep learning approach. We introduce 3D Dense-U-Net neural network architecture implementing densely connected layers. It has been optimized for graphic process unit accelerated high resolution image processing on currently available hardware (Nvidia GTX 1080ti). The method has been evaluated on MRI brain 3D volumetric dataset and CT thoracic scan dataset for spine segmentation. In contrast with many previous methods, our approach is capable of precise segmentation of the input image data in the original resolution, without any pre-processing of the input image. It can process image data in 3D and has achieved accuracy of 99.72% on MRI brain dataset, which outperformed results achieved by human expert. On lumbar and thoracic vertebrae CT dataset it has achieved the accuracy of 99.80%. The architecture proposed in this paper can also be easily applied to any task already using U-Net network as a segmentation algorithm to enhance its results. Complete source code was released online under open-source license.

Abstract A majority of biochemical reactions are often catalysed by different types of enzymes. Adsorption of the enzyme is an imperative phenomenon, which protects it from physical or chemical degradation resulting in enzyme reserve in soil. This article summarizes some of the key results from previous studies and provides information about how enzymes are adsorbed on the surface of the soil solid phase and how different factors affect enzymatic activity in soil. Many studies have been done separately on the soil enzymatic activity and adsorption of enzymes on solid surfaces. However, only a few studies discuss enzyme adsorption on soil perspective; hence, we attempted to facilitate the process of enzyme adsorption specifically on soil surfaces. This review is remarkably unmatched, as we have thoroughly reviewed the relevant publications related to protein adsorption and enzymatic activity. Also, the article focuses on two important aspects, adsorption of enzymes and factors limiting the activity of adsorbed enzyme, together in one paper. The first part of this review comprehensively lays emphasis on different interactions between enzymes and the soil solid phase and the kinetics of enzyme adsorption. In the second part, we encircle various factors affecting the enzymatic activity of the adsorbed enzyme in soil.

Microalgae are sunlight-driven miniature factories that convert atmospheric CO2 to polar and neutral lipids which after esterification can be utilized as an alternative source of petroleum. Further, other metabolic products such as bioethanol and biohydrogen produced by algal cells are also being considered for the same purpose. Microaglae are more efficient than the conventional oleaginous plants in capturing solar energy as they have simpler cellular organization and high capacity to produce lipids even under nutritionally challenged and high salt concentrations. Commercially, microalgae are cultivated either in open pond systems or in closed photobioreactors. The photobioreactor systems including tubular bioreactors, plate reactors and bubble column reactors have their own advantages as they provide sterile conditions for growing algal biomass. Besides, other culture conditions such as light intensity, CO2 concentration, nutritional balance, etc, in closed reactors remain controlled. On the other hand, though the open ponds provide a cost-effective option to utilize natural light facility for algal cells, the tough maintenance of optimal and stable growth conditions makes it difficult to manage the economy of the process. Further, these systems are much more susceptible to contamination with unwanted microalgae and fungi, bacteria and protozoa that feed on algae. Recently, some work has been done to improve lipid production from algal biomass by implementing in silico and in vitro biochemical, genetic and metabolic engineering approaches. This article represents a comprehensive discussion about the potential of microalgae for the production of valuable lipid compounds that can be further used for biodiesel production.   Key words: Biodiesel, fatty acid, lipids, microalgae, triacylglycerol.

Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time.

β-glucosidases catalyze the selective cleavage of glucosidic linkages and are an important class of enzymes having significant prospects in industrial biotechnology. These are classified in family 1 and family 3 of glycosyl hydrolase family. β-glucosidases, particularly from the fungus Trichoderma, are widely recognized and used for the saccharification of cellulosic biomass for biofuel production. With the rising trends in energy crisis and depletion of fossil fuels, alternative strategies for renewable energy sources need to be developed. However, the major limitation accounts for low production of β-glucosidases by the hyper secretory strains of Trichoderma. In accordance with the increasing significance of β-glucosidases in commercial applications, the present review provides a detailed insight of the enzyme family, their classification, structural parameters, properties, and studies at the genomics and proteomics levels. Furthermore, the paper discusses the enhancement strategies employed for their utilization in biofuel generation. Therefore, β-glucosidases are prospective toolbox in bioethanol production, and in the near future, it might be successful in meeting the requirements of alternative renewable sources of energy.

With the advent of small-scale robotics, several exciting new applications like Targeted Drug Delivery, single cell manipulation and so forth, are being discussed. However, some challenges remain to be overcome before any such technology becomes medically usable; among which propulsion and biocompatibility are the main challenges. Propulsion at micro-scale where the Reynolds number is very low is difficult. To overcome this, nature has developed flagella which have evolved over millions of years to work as a micromotor. Among the microscopic cells that exhibit this mode of propulsion, sperm cells are considered to be fast paced. Here, we give a brief review of the state-of-the-art of Spermbots - a new class of microrobots created by coupling sperm cells to mechanical loads. Spermbots utilize the flagellar movement of the sperm cells for propulsion and as such do not require any toxic fuel in their environment. They are also naturally biocompatible and show considerable speed of motion thereby giving us an option to overcome the two challenges of propulsion and biocompatibility. The coupling mechanisms of physical load to the sperm cells are discussed along with the advantages and challenges associated with the spermbot. A few most promising applications of spermbots are also discussed in detail. A brief discussion of the future outlook of this extremely promising category of microrobots is given at the end.

This paper presents an individual identification system using single lead electrocardiogram (ECG). The proposed techniques for P and T wave delineation are based on time derivative and adaptive thresholding. The performance of proposed delineators is evaluated on manually annotated Physionet QT database. The accuracy of delineators are quantified on mean error and standard deviation of differences between manually annotations and automated results. Especially, lower values of error in standard deviation for onset and offset of P wave fiducials are obtained as 8.1 and 6.29 while for T wave fiducials are 9.4 and 11.2 (where units are in ms). It shows the performance of P and T wave delineators is optimum and also stable in comparison to other published results. Found fiducials are processed for the extraction of heartbeat features. From each heartbeat, 19 stable features related to interval, amplitude and angle are computed. The feasibility of ECG as a new biometric is tested on proposed identification system designed on template matching and adaptive thresholding. The accuracy of identification system is achieved to 99% on the datasize of 125 recordings prepared from 25 individual ECG of Physionet.

This review article is aimed at providing precise information on the global scenario of the intensity and severity of excess fluoride in drinking water and the efforts made by various investigators in the field of fluoride removal from drinking water. The fluoride levels in foodstuffs and edible items are also presented with a view to help effective fluorosis mitigation in fluoride-affected areas. The critical assessment of various available technologies for the removal of fluoride reveals that, among various available technologies, electrolytic defluoridation appears to be a promising alternative for the treatment and will go a long way toward providing safe drinking water in the fluoride-affected areas of developing countries like India. It provides a technically simple, cost-effective, and reliable system for supplying fluoride free drinking water. Thus, electrolytic defluoridation is a step in upgrading access to safe drinking water and reconsidering the way forward in light of the millennium development goals.