Smart Cities is a new torrent and its concept is evolved to benefit people in different ways by providing solutions to many problems. We are racing to achieve the goal of Smart Cities and completely ignoring the perilous consequences. Out of many, one of the gigantic problems is E-waste. E-waste is growing day by day jeopardizing the environment and in turn Sustainable Development. E-waste contains many elements like mercury, lead which are toxic to humans, water, air and soil which lead to another set of serious problems. There is an urgent need to look into this matter and resolve it as expeditiously as possible. To achieve Sustainable Development, we need to get rid of E-waste in the most efficient way possible. In this chapter, we will discuss various problems caused by e-waste and approaches to manage E-waste by applying Smart Technologies. We have also proposed a model for handling e-waste.

Prediction of well-grounded market information, particularly short-term forecast of prices of agricultural commodities, is the essential requirement for the sustainable development of the farming community. Such predictions are mostly performed with the help of time series models. In this study, the soft computing method is used for short-term forecasting of agriculture commodity price based on time series data using the artificial neural network (ANN). The time series data for sunflower seed and soybean seed are considered as the agriculture commodities. The soybean seed time series data were collected for the period of five years (Jan 2014–Dec 2018), for Akola district market, Maharashtra, India. The sunflower time series data were collected for the period of six years (Jan 2011–Dec 2016), for Kadari district market, Andhra Pradesh, India. The dataset is available at the Indian government website taken from the website www.data.gov.in. For forecasting, the ANN model is used on the abovementioned datasets. The performance of the model is compared with the result of the traditional ARIMA model. The mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) are considered as the performance parameters for the forecasting model. It is observed that the ANN is a better forecasting model than the ARIMA model by considering the two forecasting performance parameters MAPE and RMSPE.

In this paper the authors introduce the “Theory of α-Sustainable Development” which is developed using fuzzy set theory. The existing concept and definition of “Sustainable Development” is correct and well understood by the world, but is not very precisely defined. In our “Theory of α-Sustainable Development” we say that every development (D) is a sustainable development up to certain extent depending upon the fuzzy measure ‘α’ of the amount of sustainability of D. We propose double categorization of every hybrid-pillar (HP) Development for better evaluation: Categorization in Type-1 and Categorization in Type-2. For a single-pillar development D the value of the fuzzy measure α is a non-negative number in [0,1], and the same is also true for a hybrid pillar (2P or 3P) development D while categorizing under Type-2. However, while categorizing the HP-Developments under Type-1, the value of α is an ordered pair (α1, α2) where α1 and α2 are in [0,1]. Every development D is graded to be qualified as one of the five categories of sustainable development: SSD, GSD, PSD, WSD and NSD. In ‘Theory of α-Sustainable Development’ the existing core notion of ‘Sustainable Development’ is neither compromised nor diluted. The theory initially discusses about ‘single-pillar Sustainable Development’ and then about ‘multi-pillar Sustainable Development’ (also called by Hybrid Pillar Sustainable Development). The notion of ‘α-Sustainable Development’ will be very much useful to the corresponding regulatory bodies to improve the practices of dealing a development D with the existing notion of Yes-No type of sustainable development. Mathematically, the core aim of this paper is to grade a development D in the continuous range [0,1] instead of the existing notion of grading in the discrete range {0, 1}. This work deals only with those type of developments which are completely constructed, not about reviewing the interim progress of them. The “Theory of α-Sustainable Development” is an open proposal to the 2030 Agenda meeting, and will surely enrich the policy of ESD for educating the present and future generation people and thus to retain the sustainability of our planet as a whole.

There are lot of problems in implementing smart cities because of large number of people with diverse and varied prerequisite. This paper offers and studies the angles and impact of big data and machine learning as approach for smart cities organization and growth. The problems of smart cities canno

The efficient working of cloud computing is based on the most important aspect, which is load balancing. This paper gives a comparison and analysis of the load balancing algorithms in a cloud computing environment. The task and resource allocation is most important for efficient working of cloud computing. The load balancing is referring to task and resource allocation in an efficient manner as this [5] is related to NP-hard optimization problem. Load distribution in the cloud computing environment in an efficient manner and taking care of all the problems at the time of load balancing is most important for load balancing algorithms. The researchers have researched on various algorithms for overcoming the load balancing generating problem during the task and resource allocation phase. In this paper, the focus is on discussing the load balancing classification and their algorithms. This paper lays emphasis on heuristic algorithms and measures the performance using the CloudSim simulator.

Present work considers the minimization of the bi-criteria function including weighted sum of makespan and total completion time for a Multiprocessor task scheduling problem.Genetic algorithm is the most appealing choice for the different NP hard problems including multiprocessor task scheduling.Performance of genetic algorithm depends on the quality of initial solution as good initial solution provides the better results.Different list scheduling heuristics based hybrid genetic algorithms (HGAs) have been proposed and developedfor the problem.Computational analysis with the help of defined performance index has been conducted on the standard task scheduling problems for evaluating the performance of the proposed HGAs.The analysis shows that the ETF-GA is quite efficient and best among the other heuristic based hybrid genetic algorithms in terms of solution quality especially for large and complex problems.

Cloud Computing is one of the rapidly growing areas in the field of computer science. A modern paradigm provides services through the Internet. An Internet-based technology employs pay-as-you-go model (PAYG). Load balancing is one of the important and vital issues in cloud computing. It (load balancing) is a technique which improves the distribution of workloads across various nodes. Load balancing distributes dynamic workload among various nodes so that no specific node breaks down by heavy load. It is crucial to utilize the full resources of a parallel and distributed system. Resource consumption and energy consumption both can be limited by distributing the load evenly and properly using different load balancing techniques. Lowering the use of resources increases the overall working performance of the system thus cutting down the carbon emission rate and providing the greener and safer environment. Different algorithms and techniques are employed to balance the load on nodes. These techniques can be examined on different parameters such as resource utilization, reliability of the system, system performance, related overhead of the system, power saving feature, scalability and many more. This paper presents the insight into the existing load balancing algorithms and their comparison on basis of different parameters.

Due to the escalating growth in energy consumption, global warming and e-wastes, the private and the government agencies are taking the concept of green computing, i.e., the environmentally liable computing into serious consideration. In broader terms by green computing, we mean the designing, manufacturing, employing and dumping of electronic devices done in such a manner that has negligible or no impact on the environment. The five main technologies that have been endorsed by General Communication Inc. for green computing are cloud computing, virtualization, power optimization, green data center and grid computing. Data center are the chief source of energy consumption. It makes use of thousands and lakhs of processing devices, thus consuming incredibly hefty amount of energy. Data center generate a large amount of heat and require enormous cooling equipment for cooling purpose, which in turn again generate the heat. Therefore, such ideas and techniques are required which will reduce the energy consumption and carbon emission. Modern IT systems depend upon a complex blend of folks, networks and hardware; thus, green computing ought to cover up all of these areas. In order to reduce the e-waste, make the processing extra efficient and keep the environment greener and safer, the concept of green computing has grown to be increasingly complex. Since last few years, ICT happens to be the vital element for success of any organization or industry; therefore, focus needs to be shifted to greening ICT which in turn makes the concept of green software important.

Over the past few years human beings have become completely dependent on computers and IT technologies, which in turn have led to the issue of energy and power consumption in IT industries. Since the energy cost as well as the electrical requirements of the industry has increased drastically throughout the world, therefore it has become necessary to shift our focus to Green computing, which refers to environmentally sustainable computing and aims to limit energy and power consumption and shrink costs thus maximizing the efficiency of the system. Green Computing provides the proficient use of computing power.

In clustering process, a set of patterns is separated into disjoint and identical significant groups. Faster data analyzing is one of the important aspects of clustering method. A number of works have been reported by various authors to optimize its multidimensionality toward distinct big data sets. However, the existing techniques are unlabeled to offer an optimal solution with regards to bunching high dimensional information set as their multifaceted nature tends to make things more hazardous while quantities of measurements are included. Especially, for uncertain and unstructured data it produces very poor results. Apart from that, the searching of data from the cloud or storage systems and data security are also important aspects. The current techniques also fail to offer proper care or solution in these matters. In this paper, the authors propose a secure clustering technique for unstructured and uncertain big data, and design an algorithm SCTA. This proposed technique does also offer high dimensionally for distinct types of big data. It includes SDES encryption technique for securing data as well as to maintain low complexity. Consequently, it includes a data searching algorithm from the cloud or storage systems to search data efficiently with low complexity.

Recent technological advancements in the field of computing have been the cause of voluminous generation of data which cannot be handled effectively by traditionally available tools, processes, and systems. To effectively handle this big data, new techniques and frameworks have emerged in recent times. Hadoop is a prominent framework for managing huge amount of data. It provides efficient means for the storage, retrieval, processing, and analytics of big data. Although Hadoop works very well with large files, its performance tends to degrade when it is required to process hundreds or thousands of small size files. This paper puts forward the challenges and opportunities that may arise while handling large number of small size files. It also presents a comprehensive review of the various techniques available for efficiently handling small size files in Hadoop on the basis of certain performance parameters like access time, read/write complexity, scalability, and processing speed.

The technological advancements in the field of computing are giving rise to the generation of gigantic volumes of data which are beyond the handling capabilities of the conventionally available tools, techniques and systems. These types of data are known as big data. Moreover with the emergence of Internet of Things (IoT), these types of data have increased in multiple folds in 7Vs (volume, variety, veracity, value, variability, velocity and visualisation). There are several techniques prevalent in today’s time for handling these types of huge data. Hadoop is one such open source framework which has emerged as a de facto technology for handling such huge datasets. In an IoT ecosystem, real-time handling of requests is an imperative requirement; however, Hadoop has certain limitations while handling these types of requests. In this article, we present an energy-efficient architecture for effective, secured and real-time handling of IoT big data. The proposed approach adopts atrain distributed system (ADS) to construct the core architecture. This study uses software-defined networking (SDN) framework for energy-efficient and optimal routing of data and requests from source to destination, and vice versa. Furthermore, to ensure secured handling of IoT big data, the proposed approach uses ‘Twofish’ cryptographic technique for encrypting the information captured by the sensors. Finally, the concept of ‘request-type’ identifying unit has been proposed. Instead of handling all the requests in an identical way, the proposed approach works by characterising the requests on the basis of certain criteria and parameters, which are identified here.

In this paper the author introduces a new type of calculus called by “Region Calculus” which could be considered as a generalization of the classical Newton Calculus. The generalization is done by improving the „distance‟ concept used in Newton Calculus for measuring the amount of increment/decrement of x-values (and y-values). Besides that, Region Calculus is developed based upon the region concerned, not just based upon a particular region R (set of Real numbers). Another new branch of mathematics called by “Object Geometry” is also introduced as a prerequisite for developing the branch of Region Calculus.

The Forecasting of agriculture commodity price plays an important role in the developing country like India, whose major population directly or indirectly depends upon farming. There are several forecasting techniques like Time series analysis, regression techniques, learning techniques. We used Auto Regressive Integrated Moving Average (ARIMA) model under Time series analysis for forecasting, which consider only the historical data. We selected price of sunflower seed for the period 1st January 2011 to 31st December 2016, gathered from “data.gov.in” for the market Kadiri, Anantpur district, Andhra Pradesh, India. We used the data from 1st Jan, 2011 to 31st Dec 2015 for training purpose and the data from 1st Jan, 2016 to 31st Dec 2016 for testing purpose. Based on the training data, ARIMA(1, 1, 2) selected as best model. Mean Average Percentage Error (MAPE) for the selected model is calculated as 2.30%. The Root Mean Square Percentage Error (RMSPE) observed by the model as 3.44%.

Ontology is a set of concepts in a domain that shows their properties and the relations between them. Medical domain Ontology is widely used and very popular in e-healthcare, medical information systems, etc. The most significant benefit that Ontology may bring to healthcare systems is its ability to support the indispensable integration of knowledge and data (Pisanelli et al, Proceedings biological and medical data analysis, 6th international symposium, 2005, [1]). Graph structure is very important tool for Foundation, Analysis, and Domain Knowledge. Ontology as a graphical model envisages the process of any system and present appropriate analysis (Pedrinaci, Ontology-based metrics computation for business process analysis, [2]). In this study, the knowledge provided by the Ontology is further explored to obtain the related concepts. An algorithm to compute the related concepts of Ontology is also proposed in a simplified manner using Boolean Matrix. The inferences from this study may serve to improve the diagnosis process in the field of Biomedical Intelligence and Clinical Data Analysis.

The entire work reported here is a philosophy based theoretical presentation. In this work it has been justified that intuitionistic fuzzy theory is more appropriate tool than fuzzy theory for soft-computing. Any association of computing methodologies centered on fuzzy set theory is well regarded as one kind of Softcomputing. Soft computing with fuzzy theory involves fluent use of both μ(x) and μ(x) i.e. ν(x) of the elements of all the universes of the concerned decision problem. The role of both μ(x) and ν(x) are very fluent in solving decision problems in almost all application areas of fuzzy theory. Recent literatures reveal that fuzzy set theory may not be an appropriate model to deal with ill-defined large size decision problems. In this work the author very precisely unearths the weakness of fuzzy theory in a further dimension, which is not caused due to μ(x) but due to the other part ν(x) of the coin. Doing a rigorous exercise with few real life examples it is observed that the fuzzy set theory is inappropriate not only for large size decision problems but also for many decision problems, irrespective of its size, large or small, in real life environment. Consequently, a set of necessary eligibility conditions is proposed at least one of which is to be mandatorily satisfied by any fuzzy decision maker before using ‘Fuzzy Set Theory’ in SoftComputing. The initial content of this paper deals with a basic question: Is ‘Fuzzy Theory’ really always good for soft-computing? In the Theory of CIFS it is justified that while solving any decision making problem, the selection of a suitable soft-computing set theory or crisp theory is made by the concerned decision maker by his own choice and own knowledge, which functions at the outer sphere of the cognition system of the decision maker. It is analogous to the case of a computer programmer who solves a problem by writing codes in a