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The difficulty of allocating a balanced educational syllabus to academic periods of a curriculum, also known as curriculum balancing, has long been a source of consternation for any institution of higher education attempting to connect learners and teachers. The balanced academic curriculum challenge entails assigning courses to academic times while adhering to all load restrictions and prerequisite requirements. The balanced academic curriculum problem (BACP) includes assigning subjects to class hours that fulfill standards even while managing students’ burden in terms of credits, course load, and perquisites that includes subjects covered in the previous semesters/periods. The number of credits every semester corresponds to the academic load. As a result, educational frameworks must be “balanced,” which means the credits for each period should be equivalent in order for students to bear minimum work. As a result, it is desirable to reduce this cost by developing a study plan that employs an algorithm that conducts this work automatically. Using an optimization method, this article provides a solution to the challenge of curricula balancing based on the discrete firefly algorithm (DFA). In research, FA has already been used to solve the BACP problem. However, the basic FA is modified to DFA with a local search mechanism inbuilt that helps to reach optimum solution in less number of iterations. A series of tests on standard and real data instances are done to check the efficiency of the suggested approach, with the objective of producing a platform that would simplify the procedure of building a curriculum system at institutions of higher learning. The results show that the proposed solution obtained a rather rapid solution and hit the recognized optimum in most of the iterations.

Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage.

Peer observation demands the teaching community to observe each other’s teaching practice followed in classrooms and to learn from it. It focuses on individual requirements and learning by receiving and providing constructive feedback. This study gives detailed information about the purpose and principle of peer observation along with the steps followed in the peer observation cycle. The data gathered are from postobservation forms and meetings. Feedback from peers is used to demonstrate the importance of peer observation in improving the overall teaching experience. The results indicated that there is a significant improvement in the teaching style of the instructors. The category of attaining student attention and engagement grew by 28.8 percent, while the category of students demonstrating passion and motivation climbed by 15.27 percent. Peer observation is seen as a beneficial tool for teachers’ professional development. Teachers stated that peer observation reduced their worry, hesitancy, and pressure throughout the teaching process, making it more dependable and real. However, participants identified time constraints and teachers’ lack of observation expertise as barriers to peer observation.

Public transport plays quite an essential role in the life of a common man. In metropolitan cities, people try to avoid road-based public transport due to reasons like increasing road congestion, air pollution, energy, and oil consumption, and most importantly to save time. Metro is the most common means of transportation which connects the whole city and the nearby areas via a train that runs on various routes connecting every station in the city. The aim of this paper is to find an optimal metro route for a passenger. The proposed system finds all the stations in order the passenger needs to visit or pass by to reach his destination station by the shortest route. The system considers the whole Metro Map as a graph where the stations act as nodes and are connected using edges. The system uses the Q-learning algorithm to find the optimal path. Q-learning is a reinforcement learning algorithm that teaches an entity which actions to perform under which conditions. It is a reward-based algorithm, stateless and off-policy and hence learns how much long-term reward it will get for every possible action taken on or from every state. Q-learning is also a feedback-based algorithm, it carries forward the output of the previous step as input to the next step. The proposed technique depicts the optimal metro route found by the algorithm. The proposed system aims to reduce the idle/stationary time by reducing the number of stations traversed while reaching the destination station which in turn reduces the total travel time.

Memory leak is a situation when the memory taken up by dynamically allocated objects is not deallocated after its use. Memory leak is a serious problem in embedded systems as they are memory constrained devices. This can also be a serious problem in servers as continuous leaking of memory space results in denial of client requests in due time. The programmer has to take care while writing the code of the application for such main memory related issues. Apart from manual inspection of the code for memory leak, finding illegal memory issues can be tedious sometimes. Various tools are available in order to detect main memory related issues in the software which can ease the testing time of application. These tools are categorized as static and dynamic analysis tools. These tools help programmers to write memory safe and clean code without going into memory leak and related issues. This paper discusses different memory leakage detection tools. The paper also provides the comparison of different tools on the basis of different parameters and concludes with the evaluation of the memory detection tools on the basis of time taken. The results show that Mtrace tool takes maximum time for analysis and Electric fence tools takes minimum time for analysis.

The performance of cloud services depends on the scheduling algorithms that distribute the incoming network traffic among their servers to achieve effectiveness in execution of tasks. These algorithms assign the tasks to various computing resources which are virtual in nature. In a cloud environment, assigning tasks to corresponding resources is NP-hard in nature. The traditional scheduling algorithms like FCFS, SJF, and Round Robin etc. will not be suitable to solve NP-hard scheduling problems. Cloud scheduling considers various criteria like resource utilization, cost, makespan and throughput. In this paper, cloud scheduling algorithms such as Max-Min Algorithm, Min-Min Algorithm, Enhanced Max-Min Algorithm and Greedy algorithms for server load balancing in cloud environments are implemented. The paper concludes with the comparative analysis of these algorithms.

The stock market is extremely important in the financial component of the country's growth, but it is also extremely dynamic and unpredictable. Significant political concerns, investor calls, journal stories, the company's restructuring and development strategies, and many other factors will quickly affect it. Single business day, thousands of investors invest in the stock market. Some of these investors either lose or gain money. However, the loss or benefit on any given trading day is unpredictable. The demand for stock price predictions is incredibly strong, necessitating the use of stock market research. Effective forecast tools assist traders indirectly by supplying supportive knowledge such as price position in the future. In this paper, the system for forecasting stock is developed that makes use of stacked LSTM, linear regression, random forest and K-nearest neighbours neural network algorithm, to forecast the stock trends on the basis of the price history. The proposed framework is designed to analyse every company's stock using mathematical technical metrics. The remaining metrics are probabilistic, although others are deterministic. The aim of this paper is to reduce the risk of failure in each exchange while increasing benefit.

COVID-19 has impacted the lifestyle worldwide. Health, economy, daily routines, work culture, research, mode of communications etc. all have been adversely affected and a new normal has been defined. Until an effective and reliable vaccine is developed, social distancing is one of the key solutions to prevent the virus from affecting the community as lockdown is never a permanent solution to this problem. In order to ensure proper social distancing at any organization, a system is developed that is able to detect whether the people are following social distancing norms by using the proposed ‘Distancia-the new normal Algorithm’. The system is designed such that it gives an alert whenever the social distancing norms are violated. It can be deployed with a CCTV camera, so that the live footage fetched by the CCTV can be directly used by the system for checking that the social distancing is being followed or not. Any organization regardless of the number of employees can use this system. Existing mobile applications designed for this purpose cannot be used at schools, educational institutes or any other organization where the use of mobile phones is prohibited. Also, since the effect of rain is not considered in any of the existing system, therefore the present applications can only be deployed at limited places where there is no hindrance caused by rain/ snow etc. Thus, the proposed system can prove to be of great importance for organizations where keeping a check of the social distancing is difficult.

Virtual machine migration technique is used in cloud computing to increase the reliability and scalability of the cloud computing systems. It helps the service providers to achieve resource efficiency and quality of service. At the time of live migration, the underlying virtual machine continuously works until the entire or part of data is migrated from source to destination. Live migration of Virtual Machine (VM) acts as an important technique that allows the management of resources, maintenance of server and load balancing in cloud data centers, but there is a degradation of performance at the source and destination physical machines. Different live migrations techniques have been proposed, each showing different properties like completion time, amount of data transferred, down time of VM and degradation of performance of VM. In this paper, a live migration algorithm, Live Migration Annealing (LMA) Virtual Machine Migration that makes use of an evaluation function to perform analysis on the time series data collected over the iterations made during the live migration period is proposed. It embodies the concept of exploration and exploitation of knowledge and space. It also takes ideas from the Iterative depth first Search to perform the iterations and Simulated Annealing to find the pages eligible for the live Migration. All the eligible pages undergo a phase called Selection phase before being finally sent to the destination virtual machine which incorporates the idea of Second Chance. Live Migration is only performed if it isn’t happening at the expense of the downtime. An overall decrease in the number of iterations and downtime with least possible live migration time is achieved through the proposed algorithm.

Internet of things (IoT) connects people and things. It helps in transforming the current system to provide quality of services to the society. In order to support the idea of a smart city, IoT plays a key role to add services for citizens and the administration of the city with advanced technology. IoT has the capability to provide updated information to all the authorities who are dealing with the emergencies like fire, crime etc. IoT is also responsible for mitigating the challenges to emergency response that includes the current problems like poor communication network and lag in information. In this paper, an ARC-GIS and IoT based autonomous system is proposed. The system detects fire. The system consists of the fire-detecting device that makes use of different sensors with the ESRII ArcGIS map, responsible for detecting location of the incident. It first sends the push notification to the owner of the location. In case the fire is very high, it sends the notification to the fire-brigade application with all the details like, address of the incident, contact number, type of fire and the latitude and longitude values of the incident. The driver from the fire brigade can use the latitude and longitude values to get the shortest route from his current location to the incident location. The administrator web application is also developed that can be used for surveillance.

Crime is one of the most critical issues that the entire world is facing nowadays. The rate of crime should be minimized by using different techniques of machine learning in order to safeguard the global community from getting trapped into the activities of the criminals or anti-social elements. The paper identifies the crime patterns by utilizing the different mathematical and statistical models to forecast the probability of the crime. Crime datasets of the State of North Carolina have been used for this purpose. The paper aims to check different statistical parameters and Figure out the most common factors that affect crime. The univariate and bivariate exploratory analysis is used for extracting the most dominant features. The Akaike Information Criteria (AIC) method is used to drop out unimportant attributes followed by testing of model through Mean Absolute Error (MAE), Median Squared Error (MSE) and Root Mean Squared Error (RMSE) techniques. The work done in the paper concludes that crime predictability and criminology can be very useful in eliminating menace of crime from our society. These mathematical and statistical models can aid us in the process of making our society a safer place to live in.

Every second a large amount of news is exchanged amongst people with Internet as its driving force. Cheap and easily accessible Internet services across the world have made it even easier for the fake news to spread quickly than the real ones. Moreover, in order to gain TRP (Television Rating Point), many of the news agencies and media houses themselves indulge in malpractices, contributing towards the spread of false news. This sometimes results in riots and political as well as communal instability. Thus, in order to stop the spread of false news, blockchain technology integrated with artificial intelligence and machine learning techniques can be used. In this paper, we have proposed a model based on the above stated technologies named as Reliable News Sharing Platform (RNSP) that aims at ensuring that only real news is communicated and false news is not only detected but is also stopped from being communicated. Anonymous news publication, no central governance, no external interference, credit system are some of the salient features of our proposed model.