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Cloud Technology is a most challenging modern area in the field of modern technologies in which assets (e.g., CPU and capacity) can be rented and discharged by the clients through internet on-demand basis. The cloud computing has been giving virtual computing services to a little, medium and extensive industries, and services, for example, infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Cloud computing has a great combination with the agile software development as a research area. Many researchers worked in Agile Cloud field. The software industries are using the agile methodology for efficient software development need some platform to get quick feedback from the client. Therefore, the agile-cloud is a great combination for it but due to security reasons that directly influence the less adoption of cloud in software industries. This paper reports the survey results of software industries. The total of seven IT industries and many professionals was involved in this paper. However, this paper also contributes and reveals how existing issues can affect agile-cloud adoption for efficient software development. Furthermore, we do not find any type of survey conducted in Pakistan’s software industries-related to cloud-agile adoption.

The advancements in quantum computing and quantum cryptology have recently started to gain momentum and transformation of usable quantum technologies from dream to reality has begun to look viable. This has created an immediate requirement to comprehend quantum attacks and their cryptographic implications, which is a prerequisite obligation to design cryptographic systems resistant to current and futuristic projected quantum and conventional attacks. In this context, this paper reviews the prevalent quantum concepts and analyses their envisaged impact on various aspects of modern-day communication and information security technologies. Moreover, the paper also presents six open-problems and two conjectures, which are formulated to define prerequisite technological obligations for fully comprehending the futuristic quantum threats to contemporary communication security technologies and information assets processed through these systems. Furthermore, the paper also presents some important concepts in the form of questions and discusses some recent trends adapted in cryptographic designs to thwart quantum attacks.

Pneumonia is one such deadly infectious disease that causes inflammation the air sacs and could be life threatening if not diagnosed and treated early. Diagnosis based on symptoms and examination of Chest X-ray by a doctor to find the infection in lungs and severity of the Pneumonia, which is tedious and prone to errors. The main objective of this paper is to propose a Deep Convolution Neural Network (CNN) model based on Inception-V3 architecture and perform various hyper parameter tuning to get better accuracy than original Inception-V3 to classify the X-Ray images and find whether the person has pneumonia or not. This paper presents a model which reduces the abruptness in the Inception-V3 model while converting to a binary classification model. The proposed network consists of 130,514 trainable parameters which is significantly less compared to models that are not pre-trained and do not use transfer learning. Data augmentation is performed to improve the veracity of the data and have evaluated the results in varying environments by applying batch normalization and dropout separately and evaluated the accuracy.

This paper presents an EMG based active system for a prosthetic hand. The dominant muscles for each finger movements are identified by the anatomical study of the arm. EMG signals are recorded on these muscles and analyzed. Threshold-based detection is used for the classification of different finger movements. The recovered signals are used as a control for the prosthetic hand. The focus of the study is the control of a prosthetic hand with five fingers each with a single degree of freedom by using separate EMG signal for each finger obtained from a unique location of the forearm so as to bypass extensive classification techniques and achieve accuracy.

The Internet of things (IoT) is an active area in the current research community due to the improvement in mobile computing and wireless networks. Currently, the IoT is involved in many fields like smart cities, smart health monitoring, smart tracking, and smart factory; therefore, it is introducing new research opportunities and industrial revolutions. Smart health, in particular, is very important and trendy domain for researchers and practitioners due to its continuous monitoring of health of patients. The objective of smart health is to provide medical facilities to patients at anytime and anywhere. The smart health monitoring systems are mostly connected with the wireless network medium that is extremely vulnerable for threats. However various attacks are observed that can endanger these health monitoring applications and systems. These attacks include Denial of Service (DoS) Attack, Fingerprint and Timing-based Snooping, Router Attack, Select and Forwarding attack, Sensor attack and Replay Attack. In this paper, we discuss these attacks with their impact on health monitoring systems with some suggestive measures from our research findings.

The overall performance of the development of computing systems has been engrossed on enhancing demand from the client and enterprise domains. but, the intake of ever-increasing energy for computing systems has commenced to bound in increasing overall performance due to heavy electric payments and carbon dioxide emission. The growth in power consumption of server is increased continuously, and many researchers proposed, if this pattern repeats continuously, then the power consumption cost of a server over its lifespan would be higher than its hardware prices. The power intake troubles more for clusters, grids, and clouds, which encompass numerous thousand heterogeneous servers. Continuous efforts have been done to reduce the electricity intake of these massive-scale infrastructures. To identify the challenges and required future enhancements in the field of efficient energy consumption in Cloud Computing, it is necessary to synthesize and categorize the research and development done so far. In this paper, the authors discuss the reasons and problems associated with huge energy consumption by Cloud data centres and prepare a taxonomy of huge energy consumption problems and its related solutions. The authors cover all aspects of energy consumption by Cloud data centers and analyze many research papers to find the better solution for efficient energy consumption. This work gives an overall information regarding energy-consumption problems of Cloud data centres and energy-efficient solutions for this problem. The paper is concluded with a conversation of future enhancement and development in energy-efficient methods in Cloud Computing

Pakistan being an underdeveloped country is facing several problems. One of the most important problems of the country has been Energy crises. Pakistan has very rich resources of renewable energy which are yet to be utilized. Among these renewable energy resources, energy from sun is one of the most abundant one. Concentrated Solar power CSP is considered to be the best technology to generate electricity for solar energy. Design of a CSP plant with a capacity of 100 MW using Linear Fresnel Reflectors and integrated with thermal energy storage of six hour is proposed in this paper. A location near Nawabshah receiving direct normal irradiance (DNI) of 1955 KWh/m2/year is selected on the basis of CSP plant requirements. System advisor model (SAM) selected for carrying out the simulation of the proposed plant for one year. Heat transfer fluid is molten salt known as Hitec solar Salt instead of using synthetic oil. From simulation results it was found that the plant can generate 220,734,832 kWh (220.735 GWh) of electricity annually. The capacity factor and gross to net conversion were found to be 25.2% and 90% respectively. These results obtained are encouraging and justify that a plant based on the proposed model is thermodynamically feasible in Pakistani environment.

The grass yield prediction is essential for the decision-making on grazing optimization and remotely sensed NDVI always used for the monitoring of the actual yield, but cannot for its prediction. The artificial neural networks (ANN) was applied to explore the early NDVI and climate data in winter-spring aiming to predict AGB as annual grass yield in the Three-Rivers Headwater Region. The predictor was assessed against the on-ground observations, which showed that the ANN had a better training and validating results with an R2 of 0.75 and 0.84 respectively, but a lower test result of the R2 of 0.31. However, the modelled AGB could well explain the observed values ( $\mathrm{R}^{2}=0.67, \mathrm{p} , and $\mathrm{n}=963$ ), which indicated the ANN model had the good overall performance. This study provided a fundamental data for livestock management in early stage and the parameters optimization of ecosystem process model on the pixel scale.

Data is the heart of any system, which is perhaps the reason why big data is such a ‘big’ deal. With the growing application domain of the technology, research towards big data analytical applications’ development has gained immense attention lately. The cost-effective and scalable nature of Cloud Computing has made it an obvious infrastructural choice. However, until APIs are developed for provisioning analytical services, big data research will not meet its desired outcome. Moreover, existing frameworks are application-specific or cater to only a specific set of requirements. This paper proposes a framework for creating a technological stack for Big Data-as-a-Service. Besides this, it also presents criteria for technology selection, which is based on the identification of computing and data models for the application. Therefore, the proposed framework is application-independent and accommodates all the segments of the big data lifecycle.

A global challenging task is to optimize energy consumption in the field of computing, research community is not only focusing on finding ways for reduction of energy cost and maintaining profitability, but also working on reduction of environment effects of global warming. In this work, we aim to provide a mechanism for data centers with the motive of savings in power consumption by allowing servers to go into sleep mode without compromising on Quality of Services (QOS) in terms of availability and performance in terms of delay that occurs in activation from sleep mode when a job (or a request for a service) arrives at server. Our main focus is on backup servers (whose availability is high) and data recovery servers. Our objective is to propose a simple Dynamic Power Management (DPM) scheme for putting the server in sleep mode based on traffic load which resulted in significant power savings in server infrastructure of data centers. We applied a job scheduling method for backup servers, i.e., force sleep mode. We obtained results in a real time environment to evaluate the impact of our scheme.

This paper presents a mathematical model that describes the behaviour of frequency distribution of various patterns in binary I-sequences. This mathematical model is referred to as, I-distribution for binary I-sequences. The paper also reviews some conspicuous techniques of using I-sequences, generated by feedback with carry shift registers (FCSRs), as building blocks in stream ciphers. In order to thwart cryptanalytic attacks, these techniques rely on hiding the mathematical structures demarcated by I-distribution of binary I-sequences. Moreover, to gauge the efficacy of these techniques, an in-depth security analysis of FCSR-based ciphers is also presented. This analysis is based on Hell-Johansson and linearisation attacks on an eSTREAM candidate known as F-FCSR-H v2. Conceptual foundations of linearisation attacks in the perspective of FCSR-based stream ciphers are also presented in this paper. Moreover, the paper also presents approach-based comparative analysis between Hell-Johansson and linearisation attacks.

This paper presents a new symmetric-key block cipher for those applications which require high data processing speed. The proposed cipher is referred to as Oppel-1. Oppel-1 is an iterative, non-Fiestel, substitution-permutation network. It operates on blocks of lengths 128 bits using secret input keys of the same length. The secret input key is used to generate subkeys using specially designed subkey generation mechanism. Each of these subkeys is used in unique rounds of Oppel-1 encryption and decryption algorithms. The claimed security of the cipher is 2128 primitive operations of its encryption algorithm. In addition, the paper also presents results of mathematical analysis of Oppel-1, and discusses the immunity of Oppel-1 to cryptanalytic attacks, such as, distinguishing attacks, trade-off attacks, linear cryptanalysis, differential cryptanalysis, algebraic attacks.

A ball-bot is an extremely agile mobile robotic platform due to its inherent instability. In order to maneuver at high speeds, a specialized controller is needed. A ball-bot can be modelled as two decoupled, 2-DOF pendulum on a cart systems. These systems comprise a classical and frequently encountered problem in the area of control theory. This paper proposed a novel technique for adaptive control of a ball-bot based on inverted pendulum on a cart system using particle swarm optimization (PSO) trained neural network. The generic PID controller is used to control the above mentioned system. The controller is able to learn the demonstrative behavior and keep the pendulum up right when subjected to perturbations. Mean Square Error for training data is found to be 7.68×10−3 and 5.5×10−4 for the testing data. The results show a promising future of the proposed technique.

During the last years, energy efficiency of wireless networks arises as a dominating concept to answer the huge energy demand due to the increase of mobile traffic. Currently, a new direction appears with powering mobile base stations by renewable energy sources. While most of the studies in this direction are based on simulation or theory, this paper analyzes the usage of renewable energy in a realistic environment. Our study is based on a live scenario, where real traffic measurements of a major European mobile network operator are used. The effects of renewable energy allocation and base stations switching- off are evaluated in terms of on-grid energy saving and electric bill reduction. Moreover, a new algorithm that combines an in- telligent renewable energy allocation and a switch-off mechanism based on renewable energy generation, storage state and price of on-grid energy is proposed. Our results show that switching-off bases stations can achieve about 19 % energy demand reduction. Moreover, equipping base stations with renewable energy sources that generate 20 % of the total network energy demand can lead to 51 % reduction in the electric bill.

This paper presents a new family of secure, small and fast stream ciphers referred to as Oppel-2. Oppel-2 uses Feedback with Carry Shift Registers in combination with an output function to produce pseudorandom keystream. Members of Oppel-2 family of ciphers can also be tailored for different applications to meet their implementation constraints while providing required level of security. The paper defines three instances of Oppel-2, which are referred to as Oppel-2 V 0 , Oppel-2 V 1 and Oppel-2 V 2 . These instances of Oppel-2 provide security of 264, 280 and 2128 primitive operations, respectively. The paper also shows that Oppel-2 is immune to linearisation attacks, which are considered as most effective attacks against those ciphers whose theory depends upon properties of 2-adic numbers. In addition the paper also presents a security strength analysis of Oppel-2 against various other types of attacks. This analysis shows that Oppel-2 family of ciphers is suitable for implementation in practical applications.