Fahad Ahmad | National College of Business Administration and Economics (original) (raw)

Papers by Fahad Ahmad

Sensors, Jun 19, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Egyptian Informatics Journal, Jul 1, 2022

IEEE Access, 2021

Not only weighty energy usage pose issues for the environment, but it also raises server maintena... more Not only weighty energy usage pose issues for the environment, but it also raises server maintenance costs in data centers. The massive task with the various power control functions in computer components was made to minimize energy consumption. Increasing consumption of energy in data server environments means that data centers will have high maintenance costs. Various geo-distributed data centers are starting to grow in an age of data proliferation and information growth. Energy management for servers is now demanded for technological, environmental, and economic reasons. In this environment, the main memory is a major energy consumer, not less than the processor. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping Virtual Machine (VM) resources to the Main Memory (MM) demands to achieve good performance by minimizing the energy consumption within a certain limit is a huge challenge. This paper simulates energy-efficient task scheduling algorithms in a heterogeneous virtualized environment using real-time virtual machine scheduling to resolve the issue of energy consumption. Using a simulator Real-Time system SIMulator (RTSIM), several hardware-based scheduling algorithms are implemented to observe VM memory scheduling efficiency to save memory energy. The simulation results show that, compared to current energy-efficient scheduling methods Rate Monotonic (RM), Earliest-Deadline-First (EDF), and Least-Laxity-First (LLF), helps to reduce energy consumption and improve performance. It is also observed that memory-aware energy management architecture reduces energy and memory consumption efficiently by using EDF scheduling algorithms. In particular, EDF saves approximately 58.3 percent of memory energy than conventional systems that cannot benefit from memory-aware energy management algorithms. The energy efficiency of the algorithms continues to improve as the level of server consolidation rises. We also implemented the EDF scheduling algorithm in Xen's Credit scheduler to see if the simulation outcomes can be simulated on physical systems. Results of simulation and deployment are equated, and comparable outcomes are achieved. We also identified that shared memory between virtual machines deliberately affects memory's energy consumption based on the implementation.

Computational Intelligence and Neuroscience, Jan 7, 2022

Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generati... more Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of ings (IoT) based scenarios. erefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. e framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. ese videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm's training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking "n" qubits that can be stored and execute 2 n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. e experiments for content prioritization are conducted using Google Colab, and IBM's Quantum Experience is considered to simulate the quantum phenomena.

Computers, materials & continua, 2021

COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainab... more COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea, Japan, Saudi Arabia, and Pakistan. Significant environmental and non-environmental features were taken as the input dataset, and confirmed COVID-19 cases were taken as the output dataset. A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables. The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases. However, age and the human development index had a negative influence on the cases. The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases. During training, the binary classification model was highly accurate, with a Root Mean Square Error (RMSE) of 0.91. Likewise, the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate (RMSE = 0.95239) when predicting the number of confirmed COVID-19 cases in an area. However, dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches, like Artificial Intelligence (AI). In this study, an SSLPNN model has been trained to fit public health associated data into an appropriate class, allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ICST Transactions on Scalable Information Systems, Jul 13, 2018

The Android-based devices are gaining popularity now a day. With the widespread use of smartphone... more The Android-based devices are gaining popularity now a day. With the widespread use of smartphones both in private and work-related areas, securing these devices has become of paramount importance. These devices are prone to various security issues of malicious attacks and performance problems. Owners use their smartphones to perform tasks ranging from everyday communication with friends and family to the management of banking accounts and accessing sensitive work-related data. These factors, combined with limitations in administrative device control through owners and securitycritical applications, make Android-based smartphones a very attractive target for attackers and malware authors of any kind and motivation. Applications keep and manage diverse intrinsic data as well as sensitive private information such as address books. Smartphones enable swift and easy data exchange via 3G, 4G, and Wi-Fi. Thus, personal information stored on smartphones is prone to leakage. Up until recently, the Android Operating System's security model has succeeded in preventing any significant attacks by malware. This can be attributed to a lack of attack vectors which could be used for self-spreading infections and low sophistication of malicious applications. The research provides a distinctive solution to the security threats being found in the Android operating system. This paper presents a data security and quality enhancement method based on amalgamating quantum attributes into the Android operating system that could effectively solve the issue raised. The paper provides a proposed architecture of Quantum Key distribution being embedded within the Android OS to improve efficiency. However, QKD is a new technology. The research unleashes the possible ways in which quantum could be effectively embedded in smartphones to resolve certain data security problems. Quantum key distribution implements the Android to guard and use in the case of a run-time kernel compromise. That is, even with a fully compromised kernel, an attacker cannot read key material stored in Quantum key.

Research Square (Research Square), Jun 27, 2023

Background: The new improvements in hardware and machines showing shrewd qualities includes vario... more Background: The new improvements in hardware and machines showing shrewd qualities includes various procedures comprising software and hardware architectural improvements. A wide range of wearable-sensors, hardware equipment, machine and deep-learning models are being applied in Human Activity Recognition (HAR) oriented systems and applications lately. Whereas, to foster best models for accurate classification of human actions is of critical significance. Results: For the accomplishment of this objective this study utilizes sensor's data from two less-expensive sensors, accelerometer, and gyroscope alongside the execution of reconstruction based feature encoding approach i.e. Locality-constrained Linear Coding (LLC) for human activity recognition. This research i s intended to perform human action classification where LLC is used in this research for encoding the discriminative data of human body movements (acquired through sensors) while performing a specific action. For encoding the hand crafted features the utilization of LLC is legitimized by exhibiting its prevalence over other different approaches e.g. Sparse-Coding etc. Conclusions: Using LLC encoding approach, final classification of the feature vector is performed using different machine learning approaches i.e. Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Support Vector Machine (SVM). The results for activity classification are evaluated in terms of precision, recall, F1-Score against each activity.

Computers, materials & continua, 2021

With the rising demand for data access, network service providers face the challenge of growing t... more With the rising demand for data access, network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access. To increase efficacy of Software Defined Network (SDN) and Network Function Virtualization (NFV) framework, we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency, reduce network performance, and increase maintenance cost. The existing frameworks lack in security, and computer systems face few abnormalities, which prompts the need for different recognition and mitigation methods to keep the system in the operational state proactively. The fundamental concept behind SDN-NFV is the encroachment from specific resource execution to the programming-based structure. This research is around the combination of SDN and NFV for rational decision making to control and monitor traffic in the virtualized environment. The combination is often seen as an extra burden in terms of resources usage in a heterogeneous network environment, but as well as it provides the solution for critical problems specially regarding massive network traffic issues. The attacks have been expanding step by step; therefore, it is hard to recognize and protect by conventional methods. To overcome these issues, there must be an autonomous system to recognize and characterize the network traffic's abnormal conduct if there is any. Only four types of assaults, including HTTP Flood, UDP Flood, Smurf Flood, and SiDDoS Flood, are considered in the identified dataset, to optimize the stability of the SDN-NFV environment and security management, through several machine learning based characterization techniques like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR) and Isolation Forest (IF). Python is used for simulation purposes, including several valuable utilities like the mine package, the open-source Python ML libraries Scikit-learn, NumPy, SciPy, Matplotlib. Few Flood assaults and Structured Query Language (SQL) injections anomalies are validated and effectively-identified through the anticipated procedure. The classification results are promising and show that overall accuracy lies between 87% to 95% for SVM, LR, KNN, and IF classifiers in the scrutiny of traffic, whether the network traffic is normal or anomalous in the SDN-NFV environment.

arXiv (Cornell University), Nov 11, 2019

Nuclear monitoring must considered as high precedence against national security. Now, with the in... more Nuclear monitoring must considered as high precedence against national security. Now, with the increasing nuclear threats it is crucial to ensure that malicious entity never procure nuclear warheads. Which comprises the prevention of illegal or terrorist access to nuclear weapons. The disastrous damage that could be the consequence of unauthorized, unapproved utilization of nuclear weapon and from the expansion of nuclear technologies to unacceptable states, has driven the nuclear forces to spend epic measures of securing nuclear warheads as well as the supporting materials, infrastructure, and industries. The procedure of ratifying user's credentials is known as authentication. Cognitive based authentication is a type of authentication that is actually the amalgamation of neurobiological and psychological techniques. This research is intended to provide human inspired Cognitive Multi-level Authentication (CMLA) utilizing the extensive quantum processing capabilities. Simulation is being done on online QUVIS quantum simulator using quantum cryptography BB84 algorithm where the intended person is successfully authenticated while considering different scenarios. So, the proposed scheme will come up with self learning intellectance based secure, speedy and reliable authentication systems against nuclear command and control.

International Journal of Environmental Research and Public Health

Public feelings and reactions associated with finance are gaining significant importance as they ... more Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public’s mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public’s mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policie...

Purpose – The aim of every organization is to achieve its set goals and objectives as well as sec... more Purpose – The aim of every organization is to achieve its set goals and objectives as well as secure competitive advantage over its competitors. However, these cannot be achieved or actualized if staff or workers act independently and do not share ideas. Today prominent businesses are becoming more aware that the knowledge of their employees is one of their primary assets. Sometimes organizational decisions cannot be effectively made with information alone; there is need for knowledge application. An effective Knowledge Management System can give a company the competitive edge it needs to be successful, and, for that reason, knowledge Management projects should be high priority. This means that for any organization to be competitive in today‟s global world there is need for combination or pooling together of ideas by employees in order to achieve teamwork; this is in support of the saying that „two good heads are better than one‟. Due to the advent of the knowledge-based economy and...

Computational Intelligence and Neuroscience, 2022

International Journal of Advanced Computer Science and Applications, 2019

Data security is becoming more important in cloud computing. Biometrics is a computerized method ... more Data security is becoming more important in cloud computing. Biometrics is a computerized method of identifying a person based on a physiological characteristic. Among the features measured are our face, fingerprints, hand geometry, DNA, etc. Biometric can fortify to store the cloud server using bio-cryptography. The Bio-cryptography key is used to secure the scrambled data in the cloud environment. The Biocryptography technique uses fingerprint, voice or iris as a key factor to secure the data encryption and decryption in the cloud server. In this paper, the security of the biometric system through cloud computing is discussed along with improvement regarding its performance to avoid the criminal to access the data. Biometric is a genuine feature for the cloud provider. Cryptography algorithm will be explained using blockchain technology to overcome security issues. The blockchain technology will provide more protection through cryptographic keys to secure biometric data.

IEEE Access, 2021

Interest in serious games for education has grown during the last years. The credit goes to the p... more Interest in serious games for education has grown during the last years. The credit goes to the potential for engaging students with new ways to capture and maintain attention. The goal of this paper is to introduce a new multidisciplinary approach that incorporates psychological analysis theory with fuzzy inference and neural networks in the Foreign Language Learning prospects identification through serious games. Our research first used a Delphi method to accumulate Information Systems students' opinions resulting from a SWOT analysis of the use of serious games to learn English language. Then, we designed a Fuzzy Logic based Foreign Language Learning Prospects Identification System through serious games that takes four standard input parameters (Strengths, Weaknesses, Opportunities, Threats) already identified during our SWOT analysis and predicts the output (Learning Prospects of Foreign Language) by considering impact of certain variations in input parameters. Implementation results have been obtained through (MATLAB R2020a) and have shown reliable incites and findings.

Consuming energy at the maximal level is a major concern in wireless sensor networks (WSNs). Many... more Consuming energy at the maximal level is a major concern in wireless sensor networks (WSNs). Many researchers focus on reducing and preserving the energy. The duration of active network of WSNs is affected by energy consumption of sensor nodes. For typical applications such as structure monitoring, border surveillance, integrated in the external surface of a pipeline, and clambered along the sustaining structure of a bridge, sensor node energy efficiency is an important issue. We present modified chemical reaction optimization (MCRO) algorithm to form clusters and select cluster head (CH) among the cluster members. The simulation result shows that the proposed routing protocol provides significant energy efficiency with network lifetime over the existing routing protocols.

Social relationship quality rates our social interaction. Evaluation of emotional situation and i... more Social relationship quality rates our social interaction. Evaluation of emotional situation and identification of effective responsive strategy for currently observed situation management is dependent to social interaction and interpersonal relationship quality. According to functional perspective on emotion, to adapt and navigate the social environment ‘affective responses ‘assist individuals. Emotional Intelligence (EI) is a cognitive intelligence. For humanizing social interaction, we propose a neuro-biological emotional intelligence model covering six basic primary emotions for natural human-machine interaction, which captures extrinsic inputs through sensory receptors, and after processing, recalling prior memories, map those inputs to current exposition in order to exhibit an adaptive emotional behavior using Artificial Neuro Fuzzy Inference System Technique.

In the scientific environment, the leading variables such as voltage, current, power, heat from c... more In the scientific environment, the leading variables such as voltage, current, power, heat from cooling system, membrane temperature and hydrogen pressure are uses as steady state and transient behaviors of Fuel Cells (FC). In the reproducing process of Fuel Cells (FC) variations, DC-DC converters are connected transversely its terminals, the efficiency, stability and durability are considered as operational problems for steady state. Since the Proton Exchange Fuel Cell is a non-linear process and its parameters change when it is delivering energy to the grid. The conventional controllers can’t content the control objectives. In this paper, an intelligent DC-AC power optimization is proposed for Fuel Cell (FC) control system to produce energy in the grid stations and to improve the power quality when FC is supplying load to grid. Furthermore, a Genetic Algorithm (GA) based reactive power optimization for voltage profile improvement and real power minimization in DC-AC system. A fuzz...

Computers, Materials & Continua, 2021

Clinical image processing plays a signi cant role in healthcare systems and is currently a widely... more Clinical image processing plays a signi cant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image's accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations. In this paper, we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results. In this proposed study, mammograms are primarily used to diagnose, more precisely, the breast's tumor component. The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization. The resulting images' tumor portions are then isolated by a segmentation process, such as threshold detection. Furthermore, morphological operations, such as erosion and dilation, are applied to the images, then a gray-level co-occurrence matrix texture features, Harlick texture features, and shape features are extracted from the regions of interest. For classi cation purposes, a support vector machine (SVM) classi er is used to categorize normal and abnormal patterns. Finally, the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images, and the exact categorization of prior patterns is gained through the SVM. Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases. Substantial results are obtained through cubic support vector machine (CSVM), respectively, showing 98.95% and 98.01% accuracies for normal and abnormal mammograms. Through

IEEE Access, 2021

ICST Transactions on Scalable Information Systems, 2018

The fast-growing use of online social networks (OSNs) has prompted stakeholders to change their m... more The fast-growing use of online social networks (OSNs) has prompted stakeholders to change their market strategies and hence have raised several questions on the users' decision making (DM). OSNs, as being regularly used, have resulted in playing a significant role in supporting consumer's rational DM. We use a hybrid approach i.e. an online survey and fuzzy model development to indicate how OSNs have resulted in giving an impact on the decision making. The research depicts that OSNs support and empower users in the DM process specifically in Rationality, Design, & Choice and the model predicts the DM strategy of each user according to their present decisions using fuzzy logic. Our results also reveal that different types of users (observers, seekers, and advisers) have significantly different participation styles, which in turn have an impact on the efficacy of the DM process. We discussed the implications for OSN designers and developers based on the findings from the research.

Sensors, Jun 19, 2022

Egyptian Informatics Journal, Jul 1, 2022

IEEE Access, 2021

Computational Intelligence and Neuroscience, Jan 7, 2022

Computers, materials & continua, 2021

ICST Transactions on Scalable Information Systems, Jul 13, 2018

Research Square (Research Square), Jun 27, 2023

Computers, materials & continua, 2021

arXiv (Cornell University), Nov 11, 2019

International Journal of Environmental Research and Public Health

Computational Intelligence and Neuroscience, 2022

International Journal of Advanced Computer Science and Applications, 2019

IEEE Access, 2021

Computers, Materials & Continua, 2021

IEEE Access, 2021

ICST Transactions on Scalable Information Systems, 2018

The aim of every organization is to achieve its set goals and intentions as well as secure compet... more The aim of every organization is to achieve its set goals and
intentions as well as secure competitive advantage over its competitors. However, these cannot be accomplished or objectified if workforce or workers act individually and do not share thoughts. The environment in which businesses operate is ever changing. The market has become global and the technological advancement has transformed the way business is done. The subsequent impression of globalization is ferocious competition that has renovated the corporate landscape. Firms are increasingly employing various techniques in order to remain germane and competitive. The modern-day procedures in the areas of information storage and retrieval, web search, image processing, control, pattern recognition, bio-information and computational biology, e-markets, autonomous navigation, and guidance are benefited using cryptographic techniques. The existing inclination towards information technology have verified that this growing level of intelligence, independence and required flexibility comes true with the augmented human centricity.
Data and Network security is a major concern in structural networks i.e., enterprise level network. Cryptography helps in enhancement of data security and encryption is a key element of a cryptosystem. There are many strong and complex encryption and communication safety techniques which provides robust protection to the data against theft manipulation and destruction threats. Safe routing and node security is another core area. A lot of work has been done for safe routing techniques and node management but the implementation of these techniques offer better security for data but up to some extent. Existing network management requires all the time human interaction and availability to manage innovative or unknown tasks. There is no performance based criteria for addition and removal of routes and nodes. Although these cryptosystems are still in use but have the ability to absorb more modification for the betterment. These precautionary measures are not enough and reveal many gaps in security of the data. The aforementioned problem highlights four critical areas to be focused upon autonomous selection of suitable encryption methods according to the data nature and sensitivity, selection of appropriate protocol according to the communication medium, security of the data transmission routes and reliability analysis for
nodes management.
A data and network security mechanism called Knowledge
Based Intelligent Quantum Cryptosystem is proposed that is based on knowledge management procedures, agency and quantum cryptography techniques. This cryptosystem will grow on the basis of its knowledge expansion through learning and manage encryption complexity after analysing the identified threats, Security of routes after performing reliability analysis and Nodes structuring after analysing performance.
An innovative idea of selection, adaptability and autonomy is
offered in this book. Emerging environments demands suggested framework where resource constraint and alteration in environment are more dominant. In this research work a new concept has offered to secure data or information and has mentioned some spaces requires to be debated and some ways are suggested for the management of those problems. Proposed framework recommends methods to sense and reduce security threats in evolving setting. An Artificial Immune Base Algorithm is also proposed in order to select the best combination of encryption technique, protocol, route and node according to the data and communication requirement. Evolving nature of proposed framework make it suitable not only for prevailing technologies but for emerging technologies also. In proposed cryptosystem agency will decide encryption process complexity, relevant protocols, safe routes for transmission and secure nodes from source to destination according to the nature of data, sensitivity of data and communication security requirement.