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Due to the development in human population and their requirements, the vehicular population on the globe is increasing day by day in the medium of public transportation. As a result, carpooling comes into play, with the fundamental notion being to share personal automobile space among persons travelling similar paths. Smart carpooling, car sharing, and ridesharing are other terms for the same thing. From a socioeconomic and environmental standpoint, the major task is to develop sustainable transportation. The success of carpooling should be measured in terms of cost, stress-free driving, traffic reduction, and air pollution reduction in the transportation solution system. The major challenge here is to assist vehicle users in gaining access to and picking an appropriate cost-effective transportation option based on their environmental footprint, matching his or her requirements, preferences, and legal limits, and determining the optimum route via specified areas.

Banks cannot afford to be complacent in their operations. Due to the dramatic changes brought about by improvements in computer technology (IT) as well as competitive intensity from FinTech businesses, they must re-evaluate their competing advantages. A major point of emphasis in this essay is that banks must not abandon relationship banking that encourages direct interaction with bank clients. Orienting relationship banking on the long term simplifies incentives while also supporting bank clients' long-term requirements and objectives. However, because to the availability of IT-driven economy of scale as well as rivalry by FinTech start-ups and IT corporations, banks may be tempted to enter the transaction banking business. In this context, the paper evaluates the importance of distance, AI, and behavioural inclinations in the decision-making process. The suggestions for banking stability are analyzed in detail. The authors believe that relationship banking has the potential to reduce its disadvantages, but it should conform to the newer facts to flourish.

Kidney stones are renal calculi that are formed due to the collection of calcium and uric acid. The major symptom for the existence of these renal calculi is severe pain, especially when it travels down the urethras To detect these renal calculi, ultrasound images are preferable. But these images have speckle noise which makes the detection of stone challenge. To obtain better results, Semantic Object Region and Morphological Analysis (SORAMA) found to be productive. First scanned image undergoes noise removal process Later the image is enhanced. Detection of Region of interest (ROI) in the image is done. Later it undergoes Dilation and Erosion were a part of Morphological analysis which produces a smoothening effect on the image. From the smoothened image, the stone is detected. If the stone is not detected then it again undergoes noise removal technique and the whole process is repeated until the smoothened image with the stone is detected. This novel research paper will be a boon to medical patients suffering from this disease to be detected and diagnose at a very early stage.

With the rapid development of digital image technology, the number of digital images stored on the Internet environment has been increasing remarkably over the past decade. As a result, it has become a top priority to come up with an effective and convenient search tools for images. Although several image-based search tools have been introduced till now to allow users to search for images with relatively fast response times, it remains certain limitations from these tools in resolving ambiguity between content of the query image and returned image. In this research, we proposed a method exploiting multi deep neural networks and K-Nearest Neighbor methodologies in Content Based Image Retrieval to improve the image search quality. First, we built our own multi CNN models inherited from the pre-trained CNN models to extract image features. Next, basing on the method of K-Nearest Neighbors, we compiled the similarity measure of the distance between the feature vectors and conducted model experiments. Experiments were performed on the Oxford-IIIT Pet Image Dataset and self-collected dataset of a Kaggle competition. We then evaluated the model by accuracy, confusion matrix, and F1 scores; average precision (AP) and mean average precision (mAP) were used to evaluate the search results of our search proposed system. The experiments show that our proposed system achieved outstanding results.

Fraudulent online transactions have caused significant damage and loss to individuals and companies over a period of time. There has been an increase in online frauds with the progression of state-of-the-art technologies and worldwide communication. The design of efficient fraud detection algorithms is critical for reducing these losses. Machine learning and statistical techniques play a vital role in the detection of fraudulent transactions. Fraud detection model implementation is particularly challenging due to the lack of data, sensitive nature of data and the unbalanced class distributions. It is tough to draw inference and build better models due to the confidentiality of the records. In this paper we aim to focus on the particular problems: i) role of sampling in the presence of class imbalance (i.e. non fraudulent transactions are more in percentage of the total transactions), ii) to build and analyze various machine learning models, iii) to assess and validate the performances of different fraud detection techniques. This paper has research directions towards applying machine learning for data analysis. We have designed and assessed a prototype of a fraudulent transactions detection system which will be able to meet real-world demands and increase the security of transactions for the customers.

Megacities are intricate systems that struggle with difficulties including overcrowding, subpar urban design and planning, inadequate mobility and public transportation, subpar governance, problems with climate change, subpar water and sewage infrastructure, problems with waste and health, and unemployment. By maximising the use of available resources and space for the benefit of residents, smart cities have evolved as a solution to these problems. A smart city model sees the city as a complex adaptive system made up of residents, services, and resources that learn from one another and change across time and space. City planners must adopt a new methodical and modelling approach in order to address the fundamental concerns of dynamic growth and complexity. A method that may be utilised to comprehend complicated adaptive systems better is multiscale modelling (MM). To increase system efficiency and reduce computing complexity and cost, the MM strives to address complicated issues at several sizes, including micro, meso, and macro.

Smart cities are going through enormous moves in technology and operations in recent days, and ‘data science' is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0). Data science is typically the scientific study and analysis of actual happenings with historical data using a variety of scientific methodologies, machine learning techniques, processes, and systems. In this paper, the authors focus on and investigate the “smart city healthcare,” where data for the city gathered from different origins like sensors, devices connected with the internet, or more exterior origins, is being extracted for perceptions and secret associations to improve decision-making procedures and convey superior and further smart facilities to residents. To attain this intention, machine learning methodical design can be utilized to deliver extensive proficiency about data collected, which builds the computing process more practicable and smarter in different actual city facilities.

The current lockouts, climatic variations, population expansion, and constraints on convenience and natural resource access are some of the factors that are making the need for smart cities more critical than ever before. On the other hand, these difficulties may be conquered more effectively with the use of emerging technology. In smart cities, the number of cars on the road has skyrocketed over the years, resulting in severe problems such as gridlock, accidents, and a myriad of other issues. Increased travel time reliability, decreased congestion, more equitable distribution of green phase time, faster response to traffic conditions, timely assistance and support, and accurate prediction of traffic volumes, including timing adjustments for traffic signals; these are some of the benefits that can be achieved. It is possible that the current, conventional traffic management system isn't up to deal with the increased traffic congestion and traffic violations. Image processing is the foundation of the sophisticated traffic management system that is now in place.

Large-scale research evaluations, as well as the acquisition and analysis of massive amounts of data, in order to follow trends and models form the organization. As the world around us continues to change, many entrepreneurs are interested in learning how big data might help them enhance their businesses. While businesses have traditionally analyzed data, recent technical advancements have spawned new entrants and unlocked the potential of big data. The extent of the consequences of big data, according to an SNS research, is presently $57 billion and expected to rise further.

Here, the researchers are trying to prepare a model for identifying whether a patient is diabetic or not. The Pima Indian Dataset has been used in this case study. There are two types of diabetes. The research consists of two stages. The first is data pre-processing, and the other is classifier construction. After pre-processing, the data classifier will be constructed which will predict whether the patient is diabetic or not. Here the researchers plan to use decision tree classifier and random tree classifier. After studying the dataset, the researchers handled the missing values in optimum ways. All the types of proposed algorithm have been described in this article.

In the modern business condition, data being produced in most service associations is estimated in gigabytes or terabytes. On occasion, searching for information in a few terabytes of information can appear to be much the same as scanning for a needle in a sheaf. With the wild use of IT in business forms everywhere throughout the world, organizations as of now have massive measures of data but can't relate the vast majority of it to the creation or survey of corporate methodology. This shows the requirement for intelligent utilization of technology to be ventured in front of others in this gigantically aggressive corporate reality where profit edges are slender and clients are keen for decision.

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 wireless sensor network (WSN) is a swiftly growing technological knowledge stage with noteworthy and dynamic implementations in various fields. Due to minute battery power in WSNs, power utilization in WSN is a foremost problem. In this paper, the authors have compared resourceful power consumption protocols using different variants of SEP protocols. The simulation results demonstrate that out of variants of SEP, ZSEP performs better among all these protocols in relation to stability, life span, and throughput of the sensor network fairly.

If there isn't enough parking within a university's campus, it will take a long time to look for parking. If a university has a number of research stations that are geographically apart from one another, communication occurs between them and the main office using office cars, although occasionally leased vehicles are obtained from a travel agency. The cost of transportation will rise as a result. Similarly, all students who are not from the dormitory are day scholars who come from their homes. Every day they must drive a significant distance to go to the institution in rented automobiles. This chapter deals with the performance solutions in carpooling using machine learning techniques.

In the present study, the waste part of the banana tree was used as a precursor, and copper chloride salt was used as an impregnating agent for the preparation of adsorbent to remove both cyanide and phenol from synthetic wastewater. Initially, thermogravimetric analysis was used to determine the rate of carbonization of the material with temperature, and thus, the optimum temperature (370 °C) and time of carbonization (35 min) were assessed. Different samples of adsorbents were prepared next by varying the weight ratio of pseudo-stem of waste banana tree to copper salt from 1:1 to 30:1. All the samples were then tested for removal of both the pollutants, and the ratio (20:1) corresponding to maximum removal of both the pollutants was considered as optimum. Therefore, further studies were conducted with the adsorbent prepared at optimum ratio, temperature and time and such adsorbent was termed as copper impregnated activated banana tree (CIABT). One variable at a time approach was followed to find out the most effective condition based on the maximum removal of pollutants. Maximum removal of 95.99 ± 1.03% and 97.33 ± 0.04% was achieved for cyanide (initial concentration: 100 ppm) and phenol (initial concentration: 450 ppm), respectively, at an optimum contact time of 150 min, the particle size of 90 μ, the adsorbent dosage of 10 g/L, pH 8.0 using CIABT at 25 °C. Hybrid artificial neural network–particle swarm optimization were employed for modelling-optimization of removal of both the pollutants while achieving 91.4–99.99% and 86.43–99.99% removal of cyanide and phenol, respectively, from simulated wastewater.

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.

Wireless sensor networks (WSNs) have enabled an improved awareness in a range of purposes like battlefield surveillance, disaster management, industrial process monitoring and control, machine health monitoring, and medical applications. Accordingly, great numbers of sensor nodes (SNs) are arranged for usages that can function as an individual. Because of minute power of batteries in WSNs, proficient exploitation of this power is a significant aspect. Clustering is a capable method to expand the lifespan of WSNs by diminishing the power utilization. In the research work, the authors have compared resourceful power consumption protocols using different variants of distributed energy-efficient clustering (DEEC), namely developed DEEC (DDEEC), enhanced DEEC (EDEEC), and threshold DEEC (TDEEC). The simulation results demonstrate that out of three variants of DEEC, TDEEC performs better than other two protocols in respect of stability and WSN lifespan.

The interaction of a presynthesized orange emitting Mn2+ -doped ZnS quantum dots (QDs) with L-Cysteine (L-Cys) led to enhance emission intensity (at 596 nm) and quantum yield (QY). Importantly, the Mn2+ -doped ZnS QDs exhibited high sensitivity towards L-Cys, with a limit of detection of 0.4±0.02 μM (in the linear range of 3.3-13.3 μM) and high selectivity in presence of interfering amino acids and metal ions. The association constant of L-Cys was determined to be 0.36×105 M-1 . The amplified passivation of the surface of Mn2+ -doped ZnS QDs following the incorporation and binding of L-Cys is accounted for the enhancement in their luminescence features. Moreover, the luminescence enhancement-based detection will bring newer dimension towards sensing application.

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.

Purpose: Diabetes is a type of metabolic disorder with a high level of blood glucose. Due to the high blood sugar, the risk of heart-related diseases like heart attack and stroke got increased. The number of diabetic patients worldwide has increased significantly, and it is considered to be a major life-threatening disease worldwide. The diabetic disease cannot be cured but it can be controlled and managed by timely detection. Artificial Intelligence (AI) with Machine Learning (ML) empowers automatic early diabetes detection which is found to be much better than a manual method of diagnosis. At present, there are many research papers available on diabetes detection using ML techniques. Method: This article aims to outline most of the literature related to ML techniques applied for diabetes prediction and summarize the related challenges. Results: It also talks about the conclusions of the existing model and the benefits of the AI model. After a thorough screening method, 74 articles from the Scopus and Web of Science databases are selected for this study. Conclusion: This review article presents a clear outlook of diabetes detection which helps the researchers work in the area of automated diabetes prediction.

Because privacy concerns in IoT devices are the most sensitive of all the difficulties, such an extreme growth in IoT usage has an impact on the privacy and life spans of IoT devices, because until now, all devices communicated one to one, resulting in high traffic that may shorten the life of unit nodes. In addition, delivering data repeatedly increases the likelihood of an attacker attacking the system. Such traffic may exacerbate security concerns. The employment of an aggregator in the system as an intermediary between end nodes and the sink may overcome these problems. In any system with numerous sensors or nodes and a common controller or sink, we can use an intermediate device to combine all of the individual sensor data and deliver it to the sink in a single packet. Aggregator is the name given to such a device or component. Data aggregation is carried out to decrease traffic or communication overhead. In general, this strategy helps to extend the life of a node while also reducing network transmission.

Web mining is a central part of data analysis. The fetching and discovering knowledge from the different web data in data mining mechanism is more important nowadays. Web usage mining customs data mining practice for the investigation of custom decoration from different data storages. In this article paper, introducing a new approach for web mining taxonomy for high-performance computing. The primary motivation of this research is on the data collection in different real-time web servers for implementation and analysis. This article is focussed the WebLog Expert lite 9.3 tools for our study.

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.

This invited feature article focuses on the chemical reactions involving the surface ions of colloidal quantum dots (Qdots). Emphasis is placed on ion-exchange, redox, and complexation reactions. The pursuit of reactions involving primarily the cations on the surface results in changes in the optical properties of the Qdots and also may confer new properties owing to the newly formed surface species. For example, the cation-exchange reaction, leading to systematic removal of the cations present on the as-synthesized Qdots, enhances the photoluminescence quantum yield. On the other hand, redox reactions, involving the dopant cations in the Qdots, could not only modulate the photoluminescence quantum yield but also give rise to new emission not present in the as-synthesized Qdots. Importantly, the cations present on the surface could be made to react with external organic ligands to form inorganic complexes, thus providing a new species defined as the quantum dot complex (QDC). In the QDC, the properties of Qdots and the inorganic complex are not only present but also enhanced. Furthermore, by varying reaction conditions such as the concentrations of the species and using a mixture of ligands, the properties could be further tuned and multifunctionalization of the Qdot could be achieved. Thus, chemical, magnetic, and optical properties could be simultaneously conferred on the same Qdot. This has helped in externally controlled bioimaging, white light generation involving individual quantum dots, and highly sensitive molecular sensing. Understanding the species (i.e., the newly formed inorganic complex) on the surface of the Qdot and its chemical reactivity provide unique options for futuristic technological applications involving a combination of an inorganic complex and a Qdot.

Herein, we report the sustainable, cost-effective, and greener surface modification strategy of a quantum dot complex (QDC)—comprised of zinc chalcogenide quantum dot (Qdot; for example, undoped ZnS and Mn2+-doped ZnS) and surface zinc quinolate (ZnQ2) complexes—following interaction with bovine serum albumin (BSA) protein. The interaction of BSA with QDC led to an enhancement in their luminescence properties (such as the quantum yield and emission lifetime), solubility, and stability (in water), compared to their bare form. The enhanced luminescence properties of BSA coupled with QDC—also termed herein as the BSA–QDC nanocomposite—are due to the BSA-induced structural rigidity of the ZnQ2 complex (being present on the surface of zinc chalcogenide Qdot). The highly green luminescent nontoxic BSA–QDC nanocomposite—consisting of undoped ZnS and the ZnQ2 complex—showed better imaging capability of HeLa cells in comparison to only QDC and sensing ability of an enzyme (trypsin) with a detection limit of 0.06 μ...

In the present study, plywood dust has been used as precursor to prepare activated carbon and copper as impregnating agent. Two methods have been employed to impregnate copper in activated carbon s...

Obesity and overweight is a foremost concern around the globe for each group of age. This can be accelerated by the current imposed lockdown. However, excessive weight gain may result in other chronic diseases. This study has been considering the age group of 25 to 55 years as the sample populations and monitoring them from July, 2020 to November, 2020. The lifestyle of this population, food habit, mental health conditions are explored using deep learning based framework. All these parameters need to be monitored as these have close relation with currently imposed constraints due to COVID-19. A predictive model is constructed using deep learning techniques to predict the risk of gaining weight. The predictive model hybridizes the convolutional layer and gated recurrent neural networks as a unified entity for achieving the objective of early weight gain prediction. The result obtained by this model exhibits an encouraging predictive efficiency of 93.7%.

Herein, we report a complexation reaction between Zn2+ ions present on the surface of an orange-red-emitting environmentally sustainable Mn2+-doped ZnS QD and a non-emitting copper quinolate (CuQ2) complex, which leads to the formation of a greenish blue-emitting surface zinc quinolate (ZnQ2) complex. The synchronous contribution of the surface ZnQ2 complex and Mn2+-doped ZnS QD is directed towards the generation of photostable bright white light (at λex – 355 nm) with chromaticity coordinates of (0.34, 0.42), color rendering index (CRI) of 71 and color-correlated temperature (CCT) of 5046 K. The ZnQ2 complexed Mn2+-doped ZnS QD is herein called as quantum dot complex (QDC). The excitation- and time-dependent tunability in emission, chromaticity, CRI and CCT of QDC revealed their futuristic applications in light-emitting devices with an anticipated color output. The current work also shows the catalytic behavior of Mn2+-doped ZnS QDs towards facilitating the formation of surface ZnQ2 from CuQ2, which is not feasible with regard to the reactivity of CuQ2 under normal conditions according to the Irving–William series. The rate of the reaction was observed to be first order with respect to CuQ2 at 20 °C, and the complexation constant for the formation of ZnQ2 was estimated to be 8.3 × 105 M−1. This is important for understanding the surface chemistry of metal chalcogenide QDs towards complexation reactions.

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.