The cancer cell prolonged and continues proliferation is a major cause of tumorigenesis. In general, Insulin like growth factor receptor (IGF-1R) and Insulin receptor (IR-A) protein are responsible for such cell proliferations. However, with respect to cancers, the specific over-expression of these receptors along with the elevated levels of their agonist, i.e. insulin-like growth factor 1 (IGF-1) and insulin-like growth factor 2 (IGF-2) have shown to be the integral part of cancer cell’s proliferation. The understanding of the dual targeting of (IR) and (IGF-1R) through Artificial Intelligence in tumorigenesis is now considered to be a possible aspect to achieve the desired results. In this research we signify that according to data based on artificial intelligence, the tyrosine kinase domain of these two receptors can accommodates number of small molecules inhibitors to block the ongoing signaling cascade for cell proliferation. It is indeed found to be of paramount importance to develop such candidates as clinical solutions to block the activity of tyrosine kinase domain of IR and IGF-1R. Therefore, this study aims to use artificial intelligence for understanding the key molecular interactions responsible for activation and inhibition of the proliferation signal via tyrosine kinase domain. Further, we optimized docking protocol on crystal structures of such system from protein databank. Our study revealed that H-bond donor and hydrophobic pocket play a key role in the initiation of the signal cascade for cell proliferation. The simulations ran produced an acceptable solution based on the statistical measures of Mathew’s correlation factor and delineated two H- bonds distances between 12-22. Our study also concluded that how a docking protocol can be optimized to accommodate the non-congeneric series small molecules. We successfully ran the simulation to conclude that LYS 1030, GLU 1077, MET 1079 and ASP 1083 amino acids positions play an important role in binding of small molecules to inhibit cancer cell proliferation. This research bridges the gap between in-silico and in-vitro experimentations and paves a way to reproduce the results experimentally.

The cashless revolution is changing the landscape of the banking industry in Pakistan. In addition to internet and phone banking, a major role is played by Fintech applications such as UPaisa, Easypaisa, and JazzCash. This paper aims to study the public perception of a cashless Pakistan and the potential of Pakistan to develop into a cashless economy. We conduct an exploratory survey to find out the needs, expectations, perceptions, and fears of the consumers associated with cashless technology, and then discuss various ways in which Pakistan can go completely cashless such as e-wallets, debit cards, and other digital payment systems. The results of the survey show a positive response of the public towards a cashless Pakistan. Several factors are identified that the public faces. These make the public reluctant to move to cashless technology. This paper will point these out, and suggest solutions to these problems; paving the path towards a cashless Pakistan.

This study intends to identify school readiness in implementing information and communication technology (ICT) assisted learning, especially the use of computers. This research applied descriptive quantitative. The research samples used were subject teachers and second grade of high school students. The data collection technique employed a survey method, carried out with a random questionnaire distribution to the research sample. The results of the research sample responses were analyzed quantitatively by interpreting the percentage. Information was obtained that schools were basically "ready" to implement ICT-assisted learning. Student responses’ results showed that 55% of students "agreed" that the school had a computer laboratory, meanwhile 41.7% of students stated that they had sufficient ability at operating computers. The teachers' responses showed that the school already has supported the computer-assisted learning process and they are interested in integrating ICT in classroom activities. This research can be a basis for educators in identifying the extent to which students, teachers and school facilities are prepared to support computer-based learning. Given that computers are one of the technologies that can be used for learning activities and have been empirically proven to be able to make it easier for students to understand learning material.

Electronic mail (Email) is one of the official and very common way of exchanging data and information over digital and electronic devices. Millions of users worldwide use email to exchange data and information between email servers. On the other hand, unwanted emails or spam became phenomenon challenging major companies and organizations due to the volume of spam which is increasing dramatically every year. Spam is annoying and may contain harmful contents. In addition, spam consume computers, servers, and network resources, causes harmful bottleneck, effect on computing memory and speed of digital devices. Moreover, the time consumed by the users to remove unwanted emails is huge. There are many methods developed to filter spam like keyword matching blacklist/whitelist and header information processing. Though, classical methods like blocking the source to prevent the spam are not effective. This study demonstrates and reviews the performance evaluation of the most popular and effective machine learning techniques and algorithms such as Support Vector Machine, ANN, J48, and Naïve Bayes for email spam classification and filtering. In con conclusion, support vector machine performs better than any individual algorithm in term of accuracy. This research contributes on the for the development of methods and techniques for better detection and prevention of spam.

Feature driven development (FDD) is an agile process model that develops software according to the client features. The FDD consists of five processes, several practices and both are providing benefits to improve the software development. Although the FDD provides lots of benefits, but still endures many flaws. In previous research, there have been made numerous modifications in FDD with different aspects. These modifications could not fix all type of flaws and FDD requires improvements in many aspects. These flaws reduce the agility to deliver increments continuously and make an inverse relationship between quality and agility. Due to this relationship, the FDD does not utilize enough time on making extensive documentation, robust design, client or user involvement, and efficient testing. To overcome these issues, an enhanced feature driven development model is proposed. EFDD introduces best practice of agile manifesto named as behavioral driven development used in FDD. In this way, the focus on delivering increments quickly is achieved without affecting the quality of the software. The proposed model provides maximum agility with continuous delivery according to client features and efficient testing strategy which have asses every feature according to client specified functionality.