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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.

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.

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.

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.

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.

In this digital era, most of the daily works of users are captured in digital forms like image, audio, video, or even text. These may contain sensitive, confidential, or private information as well. To keep these, there are different techniques exercised over the years like chest, coffer, repository, treasury, etc. Sometimes such ways of hiding information attract unintended users. Therefore, steganography has been applied to digital objects in a new form called digital steganography. This chapter enforces identifying methods in digital steganography. The basic model of any type of digital steganography is composed of three types of objects: cover object, text to be hidden, and stego object. Depending on the type of cover object, different types of digital steganography methods have been defined. Only data hiding in text is discussed.

In this chapter, the authors present an innovative, smart controller to sustain mobility in wireless sensor networks (WSNs). Principally, the focal point is dependent on the arrangement of fuzzy input variables (i.e., remaining battery power [RBP], mobility, and centrality solution) to crucial usages, similar to personnel safety in an industrialized atmosphere. A mobility controller dependent upon type-1 fuzzy logic (T1FL) is planned to support sensor mobile nodes (MN). Here, a role model cluster head (RMCH) is picked out among the cluster heads (CHs) that may simply convey the message to the mobile base station (BS) by determining the appropriate type-1 fuzzy (T1F) descriptors such as RBP, mobility of the sink, and the centrality of the clusters. Type-1 fuzzy inference system (Mamdani's rule) is utilized to opt for the possibility to be RMCH. The validity of the introduced model is carried out by means of multiple linear regressions.

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.

Microblogging, where millions of users exchange messages to share their opinions on different trending and non-trending topics, is one of the popular communication media in recent times. Several researchers are concentrating on these data due to a huge source of information exchanges in online social media. In platforms such as Twitter, dataset-generated lacks coherence, and manually extracting meaning or knowledge from them proves to be painstakingly difficult. It opens up the challenges to the researchers for knowledge extraction driven by a summarization approach. Therefore, automated summary generation tools are recommended to get a meaningful summary out of a given topic becomes crucial in the age of big data. In this work, an unsupervised, extractive summarization model has been proposed. For categorization of data, k-means algorithm has been used, and based on scoring of each document in the corpus, summarization model is designed. The proposed methodology achieves an improved outcome over existing methods, such as lexical rank, sum basic, LSA, etc. evaluated by rouge tool.

COVID-19 is likely to pose a significant threat to healthcare, especially for disadvantaged populations due to the inadequate condition of public health services with people's lack of financial ways to obtain healthcare. The primary intention of such research was to investigate trend analysis for total daily confirmed cases with new corona virus (i.e., COVID-19) in the countries of Africa and Asia. The study utilized the daily recorded time series observed for two weeks (52 observations) in which the data is obtained from the world health organization (WHO) and world meter website. Univariate ARIMA models were employed. STATA 14.2 and Minitab 14 statistical software were used for the analysis at 5% significance level for testing hypothesis. Throughout time frame studied, because all four series are non-stationary at level, they became static after the first variation. The result revealed the appropriate time series model (ARIMA) for Ethiopia, Pakistan, India, and Nigeria were Moving Average order 2, ARIMA(1, 1, 1), ARIMA(2, 1, 1), and ARIMA (1, 1, 2), respectively.