Search

Internet is used for exchanging information. Sometimes it is needed to transmit confidential data via internet. Here the authors use image steganography to pass confidential data within a cover image. To construct the algorithm, they take the combinational help of particle swarm optimization (PSO), bi-orthogonal wavelet transform (BWT), and genetic algorithm (GA). They use PSO to take the enhanced version of cover image. They use BWT to choose the selective sub bands of cover image and we utilize GA to select a particular stego image among a set of stego images. Thus, an innovative technique of image steganography has been made to transmit confidential data via cover image generating stego image. This combinational approach of image steganography is quite safe for confidential data transmission and makes it hard for the attackers to retrieve the confidential data.

The world as we know it has changed over a short period of time, with the rise and spread of the deadly novel Corona virus known as COVID-19, the world will never be the same again. This study explores the devastating effects of the novel virus pandemic, the resulting lockdown, thus the need to transform the offline classroom into an online classroom. It explores and describes the numerous online teaching platforms, study materials, techniques, and technologies’ being used to ensure that educating the students does not stop. Furthermore, it identifies the platforms, technologies which can be used to conduct online examination in a safe environment devoid of cheating. Additionally, it explores the challenges facing the deployment of online teaching methods. On the basis of literature review, a framework was proposed to deliver superior online class room experience for the students, so that online classroom is as effective as or even better than offline classrooms. The identified variables were empirically tested with the aid of a structured questionnaire; there were 487(according to Craitier and Morgan)150 number of respondents who were purposefully sampled. The results indicate that students prefer the multimedia means of studies. As a result of binary logistic regression, poor internet connection, awareness on COVID-19, enough sources of materials, recommends massive open online course, favourite online methods, and satisfaction with online study are significant in the model or attitudes towards delivering of online classes during lockdown COVID-19 pandemic at 5% level of significance. The study recommends online teaching methods, but finally, the study concludes that satisfaction with online study is significant in the model or attitudes towards delivering of online classes during lockdown COVID-19 pandemic at 5% level of significance.

The Internet is used for exchanging information. Sometimes it is required to transmit confidential data over the internet. Here the authors use image steganography to embed confidential data within a cover image. To construct the technique, this article combines the help of Particle Swarm Optimization (PSO), Bi-Orthogonal Wavelet Transform (BWT) and Genetic Algorithm (GA). PSO is included to take the enhanced version of the host image. The enhanced version of the host images is more sharp and bright. Bi-Orthogonal Wavelet Transform is included to choose the selective sub-bands of the host image. Genetic Algorithm is used to select the fittest hidden image among a set of hidden images which are created after mutation. Later, the hidden image would produce a confidential password using an innovative technique. Thus an innovative technique of image steganography is introduced to transmit confidential data in a cover image. This combinational approach of image steganography is quite safe for confidential data transmission and really difficult for the attackers to retrieve the confidential data.

Herein we report the use of a hue parameter of HSV (Hue, Saturation and Value) color space-in combination with chromaticity color coordinates-for exploring the complexation-induced luminescence color changes, ranging from blue to green to yellow to white, from a non-luminescent Fe-doped ZnS quantum dot (QD). Importantly, the surface complexation reaction helped a presynthesized non-luminescent Fe-doped ZnS QD to glow with different luminescence colors (such as blue, cyan, green, greenish-yellow, yellow) by virtue of the formation of various luminescent inorganic complexes (using different external organic ligands), while the simultaneous blue- and yellow-emitting complex formation on the surface of non-luminescent Fe-doped ZnS QD led to the generation of white light emission, with a hue mean value of 85 and a chromaticity of (0.28,0.33). Furthermore, the surface complexation-assisted incorporation of luminescence properties to a non-luminescent QD not only overcomes their restricted luminescence-based applications such as light-emitting, biological and sensing applications but also bring newer avenues towards unravelling the surface chemistry between QDs and inorganic complexes and the advantage of having an inorganic complex with QD for their aforementioned useful applications.

With governments of different countries having a vision of smart cities, the technology adoption and implementation are at its peak and the current increase in the usage of advanced technology for a smart city has led to an increase in the carbon imprint across the globe, which needs immediate attention for the environment sustainability. Although the Internet of things (IoT)-enabled devices have changed our world by bringing an ease to our lifestyle, it has to be kept under consideration that they also have adverse effects on the environment. Over the past few years, enabling energy conservation via Internet of Things in the growth of smart cities has received a great deal of attention from researchers and industry experts and has paved the way for an emerging field called the green IoT. There are different dimensions of IoT, in which an effective energy consumption is needed to encourage a sustainable environment. This conceptual paper focuses on the key concept of green IoT and sustainability, knowledge of Smart cities’ readiness to Green IoT (G-IoT)-enabled sustainable practices, and identifying the Green IoT sustainability practices for smart cities.

The immense measure of classified information has been moved on the internet. Information security turns out to be progressively significant for some applications, for instance, private transmission, video observation, military, and clinical applications. Lately, there has been a great deal of enthusiasm for steganography and steganalysis. Steganography is the specialty of covering up and transmitting information through clearly harmless transporters with an end goal to disguise the presence of information. The advanced picture information, for example, BMP, JPEG, and GIF, are generally utilized as a transporter for steganography. Here the mystery message is implanted into a picture (or any media) called spread picture and afterward sent to the beneficiary who extricates the mystery message from the spread message. This picture ought not to be discernible from the spread picture, with the goal that the aggressor can't find any implanted message. The authors have proposed three approaches of steganography that can easily support message privacy.

In the information technology community, communication is a vital issue. And image transfer creates a major role in the communication of data through various insecure channels. Security concerns may forestall the direct sharing of information and how these different gatherings cooperatively direct data mining without penetrating information security presents a challenge. Cryptography includes changing over a message text into an unintelligible figure and steganography inserts message into a spread media and shroud its reality. Both these plans are successfully actualized in images. To facilitate a safer transfer of image, many cryptosystems have been proposed for the image encryption scheme. This chapter proposes an innovative image encryption method that is quicker than the current researches. The secret key is encrypted using an asymmetric cryptographic algorithm and it is embedded in the ciphered image using the LSB technique. Statistical analysis of the proposed approach shows that the researcher's approach is faster and has optimal accuracy.

Digital enterprise transformation is the amalgamation of digital techniques into the scopes of a business enterprise, fundamentally altering how one can employ and furnish ethics to clients. An organization can be authorized to take digital transformation due to several reasons. But the most important reason is that it is the survival issuance for many people. Digital transformation considers dissimilarity in every organization. Generally, it is the amalgamation of digital technology into all quarters of business. That consolidation brings about major alterations in how the business functions and conveys usefulness to its clients. Here, steganography and cryptography are used to facilitate digital transformation in any business.

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

Steganography is the way to conceal data inside a normal (non-mystery) record to keep it from discovery. While encryption expects to conceal the substance of data, steganography means to shroud the presence of data. Information security is significant when touchy information is communicated over the internet. Steganography and steganalysis methods can take care of the issue of copyright, possession, and discovery of malevolent information. Steganography is to conceal mystery information without contortion, and steganalysis is to distinguish the presence of shrouded information. In this chapter, steganography and steganalysis methods are depicted along with AI structures to show that AI systems can be utilized to recognize the mystery information covered up in the picture utilizing steganography calculations.

Steganography deals with the craft of obscuring private data inside a spread media. In confidential data communication security is a vital issue. In this paper, we use a two-layer security. At first, data encryption is achieved by the method of RSA algorithm of asymmetric cryptography, and later the ciphered data is hidden into host image by an innovative embedding technique. To hide our ciphered data into host image, we modify the existing LSB technique and use a mapping function that ensures a secure and confidential image steganography resulting in a stego image. Here cryptography is blended with steganography and provides two level security in the confidential data transmission over the internet.

Data Transmission in network security is one of the most vital issues in today's communication world. The outcome of the suggested method is outlined over here. Enhanced security can be achieved by this method. The vigorous growth in the field of information communication has made information transmission much easier. But this type of advancement has opened up many possibilities of information being snooped. So, day-by-day maintaining of information security is becoming an inseparable part of computing and communication. In this paper, the authors have explored techniques that blend cryptography & steganography together. In steganography, information is kept hidden behind a cover image. In this paper, approaches for information hiding using both cryptography & steganography is proposed keeping in mind two considerations - size of the encrypted object and degree of security. Here, signature image information is kept hidden into cover image using private key of sender & receiver, which extracts the information from stego image using a public key. This approach can be used for message authentication, message integrity & non-repudiation purpose.

Herein we report the use of a white light emitting quantum dot complex (comprising an orange emitting Mn2+-doped ZnS quantum dot and greenish-blue emitting zinc-quinolate complex) as a two-target responsive ratiometric reversible pH nanosensor in the physiological range of 6.5–10.3, following changes in their luminescence intensity ratio, color and chromaticity.

One of the potential top-level goals for 5G heterogeneous networks may be intellectual and perfect network which modifies consumer preferences in a proactive manner in addition to needs of channel. Research provides an interdisciplinary approach to e-health, primarily concern BDA, and radio space management inside a various level fifth generation network in the company of massive data. The growing need for and usage of big data fuelled digital transformation. The research focuses on the effect of Big Data on digital technologies during the 5G era. To carry out digital transformation, three machine learning (ML) algorithms are identified. In addition to decision tree DT the other algorithms used for the classification are NB, LR. These algorithms run on the large data processing engine they work. These algorithms serve as an ensemble tool for examining old records of stroke outpatients (OPs) and body built IOT based sensors [19]. These readings are available as Big Data. In the model which has been proposed here, OP-Centric Network Optimization Framework was presented before evaluating the machine learning algorithm function and all appropriate steps to plan massive data. An ensemble method in the company of NB classification device, decision tree classification device, and logistic classification device was used in this analysis. These entire classification devices are highly controlled and managed classification device. This method is based on the OP data set and feeds the predicted stroke probabilities to an SV classifier.

Herein, we report the fabrication of a single component white light emitting (WLE) nanocomposite – following the formation of a luminescent zinc quinolate complex (using 8-hydroxyquinoline (HQ) as a complexation agent) on the surface of the ZnS shell of CuInS2/ZnS core/shell quantum dots. The synchronous contribution of greenish blue emission by the zinc quinolate surface complex and red emission by the CuInS2/ZnS core/shell quantum dots led to white light generation from the nanocomposite upon UV excitation. The reported nanocomposite exhibited bright natural cool white light emission with chromaticity color coordinates of (0.32, 0.35) and (0.32, 0.33), color rendering indices (CRI) of 91 and 83 and correlated color temperatures (CCT) of 6087 K and 5930 K in the solution and solid phase, respectively. Importantly, the higher temperature (up to 200 °C) sustainable white light emitting nature and long term stability of the solid nanocomposite may have importance for solid state light emitting applications.

The rising requirement of wireless applications has set a number of boundaries on the practice of accessible radio spectrum, which is inadequate and valuable means. If examining of a radio spectrum...

We report the synthesis of a biofriendly highly luminescent white-light-emitting nanocomposite. The composite consisted of Au nanoclusters and ZnQ2 complex (on the surface of ZnS quantum dots) embedded in protein. The combination of red, green, and blue luminescence from clusters, complex, and protein, respectively, led to white light generation.

Herein we report the generation of synchronous tricolor emission for a single wavelength excitation from a quantum dot complex (QDC). The single-component QDC was formed out of a complexation reaction, at room temperature, between ligand-free Mn(2+)-doped ZnS quantum dots (Qdots) and a mixture of two organic ligands (acetylsalicylic acid and 8-hydroxyquinoline). Furthermore, the tunability in chromaticity color coordinates, which is important for solid-state lighting, was achieved following the synthesis of QDC. Moreover, the photostable QDC emitted white light (λex 320 nm) with (0.30, 0.33) and (0.32, 0.32) chromaticity color coordinates in the liquid and the solid phases, respectively. Hence, the white light-emitting QDC may be a superior material for light-emitting applications.

Steganography is data hiding technique in internet. Here we send CAPTCHA codes within a cover image using Image steganography. CAPTCHA are the crazy codes. They are used in human response test. The word is actually an acronym for: "Completely Automated Public Turing test to tell Computers and Humans Apart". It is a type of challenge–response test used in computing to determine whether or not the user is a human. Websites implement CAPTCHA codes into their registration processes due to spam. This is the utility of CAPTCHA codes. Here we generate CAPTCHA codes and later send them in an encrypted version. So, actually CAPTCHA codes are embedded into cover image with an encrypted form resulting stego image and thus attackers cannot fetch the actual CAPTCHA resulting in a secured transmission of confidential data using image steganography.

Embedding maximum information in a stego-image with minimum change in its appearance has been a major concern in image-based steganography techniques. In this paper, utilizing Genetic algorithm (GA) we have built up a competent optimal robust steganography technique. The watermarks are implanted into the HL and LH frequency coefficients in bi-orthogonal wavelet transform (BWT). In order to get better the quality of stego image and robustness of the steganography we have to develop an optimization technique using a model to explore for the optimal locations. We examine the presentation of the suggested technique in terms of Peak signal to Noise ratio (PSNR) and Normalized correlation (NC). The proposed technique is the application of steganography for confidential transmission of symmetric key generated using Genetic algorithm (GA) that can accomplish a good imperceptibility and robustness of the image

The interaction of the neurotransmitter dopamine is reported with a single particle white light-emitting (WLE) quantum dot complex (QDC). The QDC is composed of yellow emitting ZnO quantum dots (Qdots) and blue emitting Zn(MSA)2 complex (MSA = N-methylsalicylaldimine) synthesized on their surfaces. Sensing is achieved by the combined changes in the visual luminescence color from white to blue, chromaticity color coordinates from (0.31, 0.33) to (0.24, 0.23) and the ratio of the exponents (αon /αoff ) of on/off probability distribution (from 0.24 to 3.21) in the blinking statistics of WLE QDC. The selectivity of dopamine toward ZnO Qdots, present in WLE QDC, helps detect ≈13 dopamine molecules per Qdot. Additionally, the WLE QDC exhibits high sensitivity, with a limit of detection of 3.3 × 10-9 m (in the linear range of 1-100 × 10-9 m) and high selectivity in presence of interfering biological species. Moreover, the single particle on-off bilking statistics based detection strategy may provide an innovative way for ultrasensitive detection of analytes.

Impact of climate change on virtual water of a tropical multireservoir system was estimated with the help of models developed by neural network and genetic algorithm. Virtual water or embedded water or embodied water, or hidden water refers to the water used in the production of goods or services. For instance, it takes 1,300 m3 of water on an average to produce 1 t of wheat. The precise volume can be more or less depending on climatic conditions and agricultural practice. The virtual water has major impacts on productive use of water and global trade policy especially in water-scarce regions. The impact of climate change on virtual water could open a path for the efficient use of virtual water in the face of climatic uncertainties, which may directly impact availability of raw water. The present study tried to estimate the future virtual water with the help of neurogenetic models, which estimates stream flow as function of various hydrological, meterological variables, and basin characteristics. The models prepared were distributed in nature and also consider temporal variability. In total, two models were prepared with rainfall, time of concentration, and catchment loss as input and stream flow as output. One model was prepared by classifying the dataset, based on the magnitude of the variable, and the other model was prepared with normal dataset. First, the better performing model was identified and then output from RCM-PRECIS model was applied to the chosen model to estimate the impact of climate change on stream flow. The estimation results were used to calculate the amount of virtual water, and the result was compared with the present-day virtual water to analyze the change in virtual water availability due to climate change. According to the results, model prepared with normal dataset was identified as a better model, and from the estimations it could be concluded that virtual water availability would increase in case of both A2 and B2 scenario of climate change where the change would be more pronounced in case of the latter.