Journal of Information Technology Management (مدیریت فناوری اطلاعات)

An administrator is employed to identify network security breaches in their organizations by using a Network Intrusion Detection and Prevention System (NIDPS), which is presented in this paper that can detect and preventing a wide range of well-known network attacks. It is now more important than ever to recognize different cyber-attacks and network abnormalities that build an effective intrusion detection system plays a crucial role in today's security. NSL-KDD benchmark data set is extensively used in literature, although it was created over a decade ago and will not reflect current network traffic and low-footprint attacks. Canadian Institute of Cyber security introduced a new data set, the CICIDS2017 network data set, which solved the NSL-KDD problem. With our approach, we can apply a variety of machine learning techniques like linear regression, Random Forest and ID3. The efficient IDPS is indeed implemented and tested in a network environment utilizing several machine learning methods. A model that simulates an IDS-IPS system by predicting whether a stream of network data is malicious or benign is our objective. An Enhanced ID3 is proposed in this study to identify abnormalities in network activity and classify them. For benchmark purposes, we also develop an auto encoder network, PCA, and K-Means Clustering. On CICIDS2017, a standard dataset for network intrusion, we apply Self-Taught Learning (STL), which is a deep learning approach. To compare, we looked at things like memory, Recall, Accuracy, and Precision.

In our research, we provide a statistical syntax parsing method experimented on Kannada texts, which is an official language of Karnataka, India. The dataset is downloaded from TDIL website. Using the Cocke-Younger-Kasami (CYK) parsing technique, we generated Kannada Treebank dataset from 1000 annotated sentences in the first stage. The Treebank generated in this stage contains 1000 syntactically structured sentences and it is used as input to train the syntax parser model in the second stage. We have adopted Probabilistic Context Free Grammar (PCFG) while training the parser model and extracting the Chmosky Normal Form (CNF) grammar from a Treebank dataset. The developed syntax parser model is tested on 150 raw Kannada sentences. It outputs with the most likely parse tree for each sentence and this is verified with golden Treebank. The syntax parser model generated 74.2% precision, 79.4% recall, and 75.3% F1-score respectively. The similar technique may be adopted for other low resource languages.

Women’s safety is very important for around the world and many anti-women safety incidents are happened in current decades. Women's criminality is on the rise in India, particularly on an hourly basis 1000 criminal cases are filed according to Indraprastha and Kannon organizations. The Internet of Things (IoT) application will assist women in difficult situations. This design with Dc-RFO-IoT has an emergency application that can be useful to provide critical thinking and suggestions to women in rescue time. When the emergency soft button is pushed, notifications are sent to registered contacts as well as to women's hotline lines with GPS and GSM. A GPS sensor is also used to transmit the position with longitude and latitude. Every one minute, the receiver sends a link to your location, updating them on your current position. The attacker may shut the victim's mouth and prevent her from requesting assistance. The speaker on this gadget generates high-frequency sound. It will raise the alarm in the surrounding area and make the attacker fearful. This IoT with deep learning application is giving accurate outcomes and measures are improved. The performance measures like accuracy 93.43%, sensitivity 92.87%, Recall 98.34%, safety ratio 97.34%, and F measure 97,89% had been improved these are outperformance the methodology and compete with present models.

On a global scale, one of the prevalent causes of dementia is Alzheimer’s disease (AD). It will cause a steady deterioration in the individual from the mild stage to the severe stage, and thus impair their capacity to finish any tasks with no aid. The diagnosis is done with the utilization of existing methods which include medical history; neuropsychological testing as well as MRI (Magnetic Resonance Imaging), a lack of sensitivity as well as precision does affect the consistency of efficient procedures. With the deep learning network’s utilization, it is possible to create a framework for detecting specific AD characteristics from the MRI images. While automatic diagnosis is done with the application of diverse machine learning techniques, the existing ones do suffer from certain constraints with regards to accuracy. Thus, this work’s key goal is to increase the classification’s accuracy through the inclusion of a pre-processing approach prior to the deep learning model. The Alzheimer's disease Neuroimaging Initiative (ADNI) database of AD patients was used to develop a deep learning approach for AD identification. In addition, this study will present ideas for Haralick features, feature extraction from Local Binary Pattern (LBP), Artificial Neural Network (ANN), and Visual Geometry Group (VGG)-19 network techniques. The results of the experiments show that the deep learners offered are more effective than other systems already in use.

Agriculture is one of the essential sources of occupation and revenue in India. Conferring to existing statistics, most agriculturalists are facing severe losses due to poor farming yield. Farming activities are challenged by various environmental factors that affect agricultural productivity to a greater extent. The present farming situation is above the average of the process involves more biochemical bases for managing the diseases and other destructing facts. The foremost problems they are facing in day-to-day farming tasks are crop or plant diseases affecting productivity. Also, the growth of weeds along with field crops has been another challenge.  The technology has developed to rectify the problems using some machine learning algorithms like Random Forest algorithms, Decision trees, Naïve Bayes, KNN, K-Means clustering, Support vector machines. The result has been evaluated and observed through the performance evaluation metrics using confusion matrix, accuracy, precision, Sensitivity, specificity with the observations, research, and studies. The statistics have expressed the overall accuracy of 98% by achieving the detection of diseases in plants and by removing the weeds that ruin the growth of plants.

The rapid emergence and evolution of technology have greatly impacted the way people live their lives. The internet has become a vital part of our daily lives. E-commerce is a type of technology development that enables customers to buy and sell products online. It is a unique form of transaction that connects people from all around the world. Today, many consumers shop for products online and present their products along with their specifications. This is becoming more prevalent. This increases the number of consumers online, which can result in a drop in growth. This is one of the main factors that a company uses to measure its success. Growing business success is revealed with retained customers. Satisfied consumers are the assets for a growing business. The study investigates the factors that influence people's decisions when it comes to buying items online. It shows that the experiences they have while shopping online can affect their decisions. The following statistical tools were applied for this study: percentage analysis, mean score with rank correlation, and t-Test. The results reveal that the factors quality, cost, product variety, uniqueness, and safety payments were highlighted as important indicators of performance and that the companies that do online businesses had to take care of their main goal as per the proposal.

The Analytic visualization suggests representing knowledge during a visual type that may be charts, graphs, lists, or maps. The COVID 19 detection and analysis of spreading is very important for countries. Database management with respect to virus deep analysis is a critical task to the researcher through conventional algorithms. The RNA, DNA, and biological data are helping to the bio-inspired algorithm but its implementation can be complex by software tools. Therefore, an effective technique is required to cross over the above limitations. So that covid 19 pandemic data analysis is performed through FCNN (Fully conventional Neural Network) pre-training network. The dataset is collected from social media, Kaggle, and GitHub databases. At 1st stage, the auto stack encoding process is applied later same data is processed with FCNN deep learning classifier. In this research work, covid-pandemic affects parameters like infected persons, deaths, active cases, and recovering cases. The FCNN is take care of feature extraction, training, testing, and classification. Finally using a confusion matrix accuracy of 98.34%, sensitivity 97.63%, Recall 98.26%, and F measure 98.83% had been estimated.

Most real-time speech signals are frequently disrupted by noise such as traffic, babbling, and background noises, among other things. The goal of speech denoising is to extract the clean speech signal from as many distorted components as possible. For speech denoising, many researchers worked on sparse representation and dictionary learning algorithms. These algorithms, however, have many disadvantages, including being overcomplete, computationally expensive, and susceptible to orthogonality restrictions, as well as a lack of arithmetic precision due to the usage of double-precision. We propose a greedy technique for dictionary learning with sparse representation to overcome these concerns. In this technique, the input signal's singular value decomposition is used to exploit orthogonality, and here the ℓ1-ℓ2 norm is employed to obtain sparsity to learn the dictionary. It improves dictionary learning by overcoming the orthogonality constraint, the three-sigma rule-based number of iterations, and the overcomplete nature. And this technique has resulted in improved performance as well as reduced computing complexity. With a bit-precision of Q7 fixed-point arithmetic, this approach is also used in resource-constrained embedded systems, and the performance is considerably better than other algorithms. The greedy approach outperforms the other two in terms of SNR, Short-Time Objective Intelligibility, and computing time.

The spread of COVID-19 pandemic starting in late 2019 has changed the way we conduct our teaching and learning activities especially in Higher Education Institutions (HEIs). Since March 2020, classes have been conducted via online platforms. As a consequence, students missed the campus life, teamwork has been given less emphasis, fieldwork, industry visits and community service have been put aside, and most importantly the achievement of the learning outcomes towards a certain extent has been compromised. The implications of these changes need to be highly considered as they might affect the quality of graduates. This paper intends to discuss the impact of COVID-19 pandemic on the education system and highlight some potential solutions that can be considered by the academics and the top management of HEIs to address the negative repercussions of the current practices. Some research implications are also highlighted in the paper.

Chatbots is a computer software powered by artificial intelligence designed to replicate human interaction. It is also possible to refer to them as digital assistants that comprehend the capacities of humans. The bot interprets the user's intent, then processes their queries and provides prompt responses. Chatbots perform their most crucial role: to analyse and detect the intent of the user's request to extract relevant entities. AI-powered chatbots were introduced to improve operational efficiency, eventually saving organisational costs. This study investigates the role of system and service quality in customer satisfaction in banking services. One hundred forty-five usable data were used for analysis. Data were analysed using the Smart PLS. The results revealed that response time, usability, adaptability, empathy and responsiveness were insignificant for customer satisfaction. The result is important as it gave the insight point of customers with regards to the new services. Business organisations may need to introduce chatbots and perhaps make some improvements from time to time to provide better services.

Over the last few decades, both developed and developing countries have an increasing trend in technology but with an enormous gap, particularly in the education sector. The e-learning following institutions can achieve benefits by evaluating their e-learning readiness through up-front analysis. Moreover, several models have been introduced to measure e-learning readiness for developed countries but these are not adequate for developing countries.  This paper introduced the e-learning readiness evaluation model for the developing country, Libya, by considering the primary education sector. Furthermore, this study examines the e-learning level of readiness in the staff of the primary school. The purpose of this study is to evaluate the e-learning readiness of staff by directing factors of e-learning readiness i.e. cultural readiness, content readiness, and technology readiness. To achieve this objective, this paper collects data through questionnaires, and respondents are 110 staff member of primary schools in Yefren, Libya. Therefore, the multivariate analysis shows that the e-learning readiness factors have e significant relationship with the adoption of e-learning because most teachers are well prepared and ready. Likewise, results indicate that technology is the most significant factor instead of other e-learning readiness factors. According to the views of staff, there should be more content development training required for primary school staff. Thus, the demographic structure is inadequate to enhance the e-learning but the staff is ready for e-learning. Consequently, this study emphasizes the significance of cultural readiness and its relationship with the adoption of e-learning in primary education sectors’ development in Yefren, Libya.

The bibliometric study aims to map and expand respective knowledge by establishing connections between important actors in academic research regarding the government venture capitals (GVCs). The scope is to analyze documents published on Scopus database starting from 2011 to 2020.  Accordingly, the United States (U.S.) is the top country in all categories with China catching up. Alperovych, Quas, and Colombo are top co-authors. On the other hand, Leleux, Grilli, Lerner and Cumming are prolific authors. Articles by Grilli and Li Y are two most cited documents. Investments, venture capital, economics, public policy, and government are most co-occurrence index keywords. Research policy, venture capital, and journal of technology transfer, journal of business venturing and small business economics are top sources of cited documents. Closely associated themes with respect to the study of GVCs are government role in venture capital support, effective Innovation financing policies, performance differential, performance of portfolio companies, funding challenges and investment strategy, decision making model and critical success factors for IT startups. The analysis generated gaps and directions for future research consisting of fund’s structure and characteristics, key personnel’s work experience and network, geographic location, investment horizon, shareholding rights.

The worldwide Covid-19 epidemic while affecting numerous places, has had a profound effect on virtual advertising and advertising and, globally, in the provinces and at the neighborhood level. except, this effect for the most element become positive, in contrast to what has seen in exclusive sectors which include economy, human sources, etc., whilst contamination reasons a variety of incapacity amongst clients and advertisers alike, in phrases of welfare, social work, inflation, business, and many others., the equal shifted conduct goes as a long way as running. , investing strength, getting into self-schooling, adopting new programs from the internet, expanding social and hygiene concerns, retaining distance strategies from complete regions, internet-based media willpower, get right of entry to online sources, etc., and this has greatly impacted the display and endorsed efforts. The moral movement has moved past the PC and digital international, which places open doors for advertisers and products to connect with clients more efficiently than ever before. With the arrival of expanded online media and the call for pc-generated content material, the evolved Media have given advertisers a part of the monetary freedom. At the equal time, this has, in turn, enabling advertisers to be extra proactive and to engage with the public at the same time as appearing excessive excellent demonstration programs. The purpose of this study is to explore, investigate, and recognize the effect of coronavirus on the digital market and businesses.

The global spread of internet technology and the associated advancements are making it easier for women entrepreneurs to manage the work-family boundary. However, there is a need for more research on digital entrepreneurship (DE), especially on how different degrees of DE influence the success of work-family boundary management (WFBM). This study explores the effect of extreme, moderate, and mild pursuit of DE on women’s abilities to manage the boundary between work and family. This study uses a quantitative research method and collected data from 312 women entrepreneurs. The results show that DE enables women entrepreneurs to manage the work-family boundary. We found that with extreme DE, women are more likely to experience high levels of cross-role interruption behaviours and perceived boundary control, while with moderate DE, women experience high levels of identity centrality of work and family roles. Therefore, this study contributes to the literature on women’s DE by investigating different degrees of DE and its effects on work-family boundary management. The study also contributes to the literature on WFBM through examining the dynamics of DE in enabling women entrepreneurs to manage work-family boundaries to different extents. Therefore, this study captures the interplay between DE and managing work-family boundaries, which facilitates our understanding of women entrepreneurship and the role DE has in enabling the agentic potential of entrepreneurial actors.