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Detection and classification of cancerous tissue from histopathologic images is quite a challenging task for pathologists and computer assisted medical diagnosis systems because of the complexity of the histopathology image. For a good diagnostic system, feature extraction from the medical images plays a crucial role for better classification of images. Using inappropriate or redundant features leads to poor classification results because classification algorithm learns a lot of unimportant information from the images. We propose hybrid feature extractor using different feature extraction algorithms that can extract various types of features from histopathological image. For this study, feature fused Convolution Neural Network, Gray Level Cooccurrence Matrix, and Local Binary Pattern algorithms are used. The texture and deep features obtained from these methods are used as input vector to classifiers: Support Vector Machine, KNearest Neighbor, Naïve Bayes and Boosted Tree. Prediction results of these classifiers are combined using soft majority voting algorithm to predict final output. Proposed method achieved an accuracy of 98.71%, which is quite high as compared to previous similar research works. Proposed method was capable of identifying most of cancerous histopathology images. The combination of deep and textural features can be potentially used for creating computer assisted medical imaging diagnosis system that can detect cancer from histopathology images timely and accurately.

Cancer is one of the most deadly diseases diagnosed among the population across the globe so far. The number of cases is increasing at a high pace each year that subsequently leads to the advancement in different diagnosis tools and technologies to handle this pandemic. Significant increase in the mortality rate worldwide leads tremendous scope to device and implement latest computer aided diagnostic systems for its early detection. The one among such techniques is machine learning coupled with medical imaging modalities. This combination has proven to be efficient in diagnosing various medical conditions in cancer diagnosis. Current study presents a review of different machine learning techniques applied on imaging modalities for cancer diagnosis from 2008 to 2019. This study focuses on diagnosis of five most prevalent and deadly cancers i.e., cervical cancer, oral cancer, breast cancer, brain cancer and skin cancer. Extensive and exhaustive review was carried out after going through different research papers, research articles and book chapters published by reputed international and national publishers such as Springer Link, Science Direct, IEEE Xplore Digital library and PubMed. A number of conference proceedings have also been included subject to the fulfilling of our quality evaluation criteria. This review article provides a comprehensive overview of machine learning approaches using image modalities for cancer detection and diagnosis with main focus on challenges being faced during their research. Majority of the challenges are identified based on the use of potential machine learning based approaches, image modalities, features and evaluation metrics. This review not only identified challenges but also ear mark and present the new research opportunities for researchers working in this field. It has been widely observed that traditional machine learning algorithms Like SVM, GMM performed excellent in classification whereas the deep learning has dominated the field of medical image analysis to a greater extent. It is evident from the literature survey that the researchers have achieved the accuracies of 100% in classification of cancerous and normal tissue images using different machine learning techniques. This article will provide an insight to the researchers working in this domain to identify which machine learning technique work best on what type of data set, selection of features, various challenges and their proposed solutions in solving this complex problem. Limitations and future research opportunities in the field of implementing different machine learning techniques in cancer diagnosis and classification is also presented at the end of this review article.

Drug discovery and development process is very lengthy, highly expensive and extremely complex in nature. Traditional methods involve expensive techniques and take many years to bring a new drug to the market. With the advent of new tools and technologies in this field, the major challenge is to reduce the time and cost required for the development of a new drug. These complex problems involve extremely high computations and can be addressed with the help of Artificial Intelligence based techniques. In this paper, we have broadly discussed different emerging applications of artificial intelligence in the field of drug discovery and development including identification of gene targets for diseases, repurposing of existing drugs through pathway networks, improvements in structure modelling, virtual screenings and hit identification, ADMET prediction, lead identification, clinical trials etc. using various artificial intelligence methods and their inter comparisons. This review presents the literature survey of different research articles published in reputed journals of international publishers such as Springer, Science Direct, IEEE Xplore, Elsevier etc. This is a systematic review of 143 publications to provide an organized summary. In addition to the in-depth analysis the foreseen challenges and existing limitations associated with drug discovery and development process are also pointed out in bold and humble suggestions have been made for necessary improvements. Readers, who are new to the field, will find it useful for enhancing their view about the field.

In the developing world, parasites are responsible for causing several serious health problems, with relatively high infections in human beings. The traditional manual light microscopy process of parasite recognition remains the golden standard approach for the diagnosis of parasitic species, but this approach is time-consuming, highly tedious, and also difficult to maintain consistency but essential in parasitological classification for carrying out several experimental observations. Therefore, it is meaningful to apply deep learning to address these challenges. Convolution Neural Network and digital slide scanning show promising results that can revolutionize the clinical parasitology laboratory by automating the process of classification and detection of parasites. Image analysis using deep learning methods have the potential to achieve high efficiency and accuracy. For this review, we have conducted a thorough investigation in the field of image detection and classification of various parasites based on deep learning. Online databases and digital libraries such as ACM, IEEE, ScienceDirect, Springer, and Wiley Online Library were searched to identify sufficient related paper collections. After screening of 200 research papers, 70 of them met our filtering criteria, which became a part of this study. This paper presents a comprehensive review of existing parasite classification and detection methods and models in chronological order, from traditional machine learning based techniques to deep learning based techniques. In this review, we also demonstrate the summary of machine learning and deep learning methods along with dataset details, evaluation metrics, methods limitations, and future scope over the one decade. The majority of the technical publications from 2012 to the present have been examined and summarized. In addition, we have discussed the future directions and challenges of parasites classification and detection to help researchers in understanding the existing research gaps. Further, this review provides support to researchers who require an effective and comprehensive understanding of deep learning development techniques, research, and future trends in the field of parasites detection and classification.

In classification and prediction of different types of medical disorders the neuro-fuzzy systems (NFS) are playing vital and significant role. To avoid false diagnosis the NFS assists medical practitioners to a greater extent in automating the domain dealing with medical disorders. With the passage of time the NFS approach has become apparent to enhance accuracy in dealing with a wide range of complicated research problems in the field of medical diagnosis. In this paper the author presents the literature review of the research done in implementing NFS in the field of medical diagnosis for current decade. Total of 100 publications in chronological advancement and up-gradation in models are considered for the time period of 10 years. A detailed study of each disease is carried out to discuss how NFS methodologies have been applied for classification and prediction in the diagnosis of different types of medical disorders. Ten (10) most severe medical disorders i.e. cancer, cardiovascular, depression and anxiety, diabetes, communicable, kidney, liver, neuro-degenerative, respiratory and thyroid has been undertaken for the study. Based on the study carried out it has been observed that NFS found to be effective as compared to the application of other AI techniques in medical diagnosis. Study reveals that effectiveness of NFS increases significantly when integrated with other AI approaches. This review adds into the knowledge of different researchers working in the field of medical diagnosis and will also give the comprehensive view of the effectiveness of the NFS techniques being used in medical diagnosis. The paper also incorporates a few research publications that were submitted in 2019 to incorporate the latest advances in medical science implementation of NFS.

Bacteria are important in a variety of practical domains, including industry, agriculture, medicine etc. A very few species of bacteria are favourable to humans. Whereas, majority of them are extremely dangerous and causes variety of life threatening illness to different living organisms. Traditionally, this class of microbes is detected and classified using different approaches like gram staining, biochemical testing, motility testing etc. However with the availability of large amount of data and technical advances in the field of medical and computer science, the machine learning methods have been widely used and have shown tremendous performance in automatic detection of bacteria. The inclusion of latest technology employing different Artificial Intelligence techniques are greatly assisting microbiologist in solving extremely complex problems in this domain. This paper presents a review of the literature on various machine learning approaches that have been used to classify bacteria, for the period 1998-2020. The resources include research papers and book chapters from different publishers of national and international repute such as Elsevier, Springer, IEEE, PLOS, etc. The study carried out a detailed and critical analysis of penetrating different Machine learning methodologies in the field of bacterial classification along with their limitations and future scope. In addition, different opportunities and challenges in implementing these techniques in the concerned field are also presented to provide a deep insight to the researchers working in this field.

Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995–2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.

Advancement in technology has helped people to live a long and better life. But the increased life expectancy has also elevated the risk of age related disorders, especially the neurodegenerative disorders. Alzheimer’s is one such neurodegenerative disorder, which is also the leading contributor towards dementia in elderly people. Despite of extensive research in this field, scientists have failed to find a cure for the disease till date. This makes early diagnosis of Alzheimer’s very crucial so as to delay its progression and improve the condition of the patient. Various techniques are being employed for diagnosing Alzheimer’s which include neuropsychological tests, medical imaging, blood based biomarkers, etc. Apart from this, various machine learning algorithms have been employed so far to diagnose Alzheimer’s in its early stages. In the current research, authors compared the performance of various machine learning techniques i.e., Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF) and Multi Layer Perceptron (MLP) on Alzheimer’s dataset. This paper experimentally demonstrated that normalization exhibits a predominant role in enhancing the efficiency of some machine learning algorithms. Therefore it becomes imperative to choose the algorithms as per the available data. In this paper, the efficiency of the given machine learning methods was compared in terms of accuracy and f1-score. Naïve Bayes gave a better overall performance for both accuracy and f1-score and it also remained unaffected with the normalization of data along with LDA, DT and RF. Whereas KNN, SVM and MLP showed a drastic (17% to 86%) improvement in the performance when they are given normalized data as compared to un-normalized data from Alzheimer’s dataset.

In automated screening of cervical cytology, the morphological features of cell play a determining role. To avoid false diagnosis, urgent need of precise extraction of these features led to emergence of new segmentation models. In this paper author aspire to present literature review of research done in the field of segmentation stage in automatic screening of cervical smear images. Total of 78 publications are considered for the time period of 40 years. A detailed study of segmentation technique proposed in each publication is considered, which presents a chronological development and up-gradation of segmentation models. This review assist researcher to have thorough knowledge of various state-of-art segmentation models and the problems and complexities required to be tackled, for unambiguous determination of malignancies in cervical cytology.

Over the past few decades, the artificial intelligence is being employed in diverse fields like pattern classification, image processing, object identification, recommender systems, speech recognition, etc. Machine learning has made it possible to develop intelligent systems through training that equip machines to handle different tasks, exactly on the analogy similar to humans. In medical field, machine learning algorithms are being used for prediction, early detection and prognosis of various diseases. These algorithms suffer a certain threshold due to their inability to handle large amount of data. Deep learning based techniques are emerging as efficient tools and can easily overcome the above difficulties in processing data related to medical imaging that includes mammographs, CT scans, MRIs and histopathology slide images. Deep learning has already achieved greater accuracy in early detection, diagnosis and prognosis of various diseases especially in cancer. Dysplasia is considered to be a pathway that leads to cancer. So, in order to diagnose oral cancer at its early stage, it is highly recommended to firstly detect dysplastic cells in the oral epithelial squamous layer. In our research work, we have proposed a deep learning based framework (convolutional neural network) to classify images of dysplastic cells from oral squamous epithelium layer. The proposed framework has classified the images of dysplastic cells into four different classes, namely normal cells, mild dysplastic cells, moderate dysplastic cells and severe dysplastic cells. The dataset undertaken for analysis consists of 2557 images of epithelial squamous cells of the oral cavity taken from 52 patients. Results show that on training the proposed framework gave an accuracy of 94.6% whereas, in testing it gave an accuracy of 90.22%. The results produced by our framework has also been tested and validated by comparing the manual results recorded by the medical experts working in this area.

The work done in this paper exhibits an expert system based ensemble model in diagnosing type-II diabetes. Diabetes Mellitus is a disease with high mortality rate that affects more than 60% population. The mindset of this task is to analyze various machine learning techniques for binary classification concerning with illness i.e. to diagnose whether a subject is suffering from disease or not. There are in total fifteen classifiers considered and out of them five major techniques namely: ANN, SVM, KNN, Naive Bayes and Ensemble are used. For achieving the desired goals the tools that were employed namely matrix laboratory (MATLAB) and WEKA 3.6.13. In Ensemble method the predictive potentials of various individual classifiers are fused together. Using Ensemble method, it increases the performance by combining the classifying ability of individual classifiers and the chances of misclassifying a particular instance are reduced significantly, this provides a greater accuracy to the overall classification process. It is the enhancing technique that does the majority voting and gives us the percolated results. The medical database analysed in this study includes a rich database of about 400 people from across a wide geographical region and ten physiological attributes. Furthermore, this diagnostic tool is examined by verifying denary cross attestation; on top of that the outcome has been confronted along the truly existing real interpretation about the cases. A GUI based diagnostic tool founded upon ensemble classifier is developed in such a manner it would be able to predict whether a patient is enduring against the disease or not when it is fed with all the 10 attributes from user through a user friendly GUI (Graphical User Interface).The development of this diagnostic tool is done using MATLAB 2013a. Out of 10 parameters that the user needs to enter as input in GUI based diagnostic tool five are numeric and the rest are nominal values. The diagnostic tool in execution is demonstrated below in Fig. 3. The main objective of this manuscript is to propose an intelligent framework that will act as a useful aid for doctors for correct and timely biopsy can be done at early stage. The result indicated that ensemble technique assured an accuracy of 98.60% that clubs the predictive performance of multiple AI based algorithms and are superior in comparison with all other individual counterparts. The algorithms with better exactness than others are followed by Artificial neural network (ANN), Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN).

In recent times, Internet brought revolution by connecting the whole world to share the information at one platform. Since data is the most valuable asset, every organization is putting its best effort and spending a lot of money on various security solutions like firewall, antiviruses, etc. to prevent its data and resources from unauthorised access and cyber-attacks like phishing, hacking, eavesdropping, etc. In spite of bulk of these security mechanisms, hackers are still able to exploit the vulnerabilities in the web applications to steal user’s credentials. Intrusion detection system (IDS) is proposed by researchers to detect malicious activity in the network to mitigate the cyber-attacks. In this paper, different techniques of machine learning namely K-nearest neighbor, multilayer perceptron, decision tree, Naive Bayes and support vector machine have been evaluated for implementation of IDS to classify network connections as normal or malicious. Four measures, i.e., accuracy, sensitivity, precision and F-score, have been taken to assess ability of machine learning techniques under study. Experimental results have shown that decision tree is best classifier for IDS.

Use of computer-aided diagnosis systems has been increasing in the medical domain due to the rising complexity and amount of medical data. Well-defined feature descriptors are must for computer-aided diagnostic systems. In this research paper, we attempt to create an ensembled feature extractor and selector for better classification of normal and abnormal medical images using different machine learning algorithms. In this research paper, two different data sets will be used one for oral cancer histopathology images and one for brain tumor MR images. The comparison of various feature extraction techniques will be done, and result analysis will also be provided at the end.

Machine learning techniques play an important role in solving real world problems. Prediction in the field of medical science using these techniques has helped the experts in making their diagnosis accurate in recent years. Implementation of these techniques leads to the advancement in the production of efficient diagnostic procedures. In our study, four machine learning techniques: Logistic Regression, k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Neuro-Fuzzy System (NFS) have been evaluated on the basis of five most important parameters, i.e., RMSE (Root Mean Square Error), Accuracy, Recall, Precision, and F-score. Experimentation is done to reveal the most accurate forecasting technique for dementia diagnosis. The data set containing 336 records was undertaken for the study and subsequently divided into three different ratios of 90:10, 75:25, and 60:40 for training and testing. Results obtained shows that SVM outperformed all other techniques in all cases, and optimum results were predicted for all the techniques in the selection of 75:25 split into train-test.

Website development is becoming a growing concern for all organizations in this world. However the process and lifecycle involved in their development is still uncertain. The Web based system development usually involves more heterogeneous stakeholders as compared to traditional software’s development construction. The process models used for the development of websites stands borrowed from the traditional software development approaches. The process of website development involves several fields of web engineering and multimedia applications which require altogether a different skill set and development processes. The growth of web based system has been exponential. An estimated size of public web and deep web has reached to 40 billion pages and where the pages are assembled on the fly in response to the user requests touches between 400 and 750 billion pages, respectively. The web based system size is continuously expanding like the universe after the big bang. Many researchers and practitioners have noted the rapid growth of internet for commercial purposes since its birth in 1990’s. The web presence allows small companies to compete comparatively at par with large enterprises which increases keenness for every organization to develop a website. An increase in the amount of web development work, forces the designers/webmasters working in different website development houses to carry out work in a well planned and systematic manner. The concerned organizations and different web development houses must, however follow specific methodologies/standards/guidelines recommended by various organizations for advancement in present web development process models. The adoption of traditional methodologies of web development leads to the production of poorly designed web applications which show variable performance across different browsing platforms and have high probability failure rate. This propagates in the lack of confidence during web development and ultimately moves towards Web-Crisis. In order to avoid Web-Crises, there is a desperate need of designing and developing the newer process models that are especially concerned with website development. As such there is a need of disciplined approach towards the development of web-based systems. Researches also suggest that there is a non uniform approach in web based systems development. The authors suggest that a specialized model for web development needs to be designed and developed to satisfy the dynamic categories of the stakeholders present worldwide. This paper discussed about the various traditional software and web development models popularly used in developing web applications and their suitability and effectiveness is compared which leads to the development of a successful web based applications. Various reasons which differentiate the traditional software development from web system development have also been worked out. The presences of specific features recommended in proposed framework are also tested by available methodologies. Finally, an initial framework is proposed which caters to the need of specific web development process model.

Twitter is an online news and person-to-person communication website, where users can post their thoughts and emotions as tweets concerning an issue, actuality, choice, and so on. Since its launch in 2006, Twitter has now become one of the most reputable and much publicized online tools for people to express their social and political thoughts. Since twitter enables its registered users to express their notions as tweets, which edify researcher to create methods that may utilize to foresee the result of the election based on people tweets. This paper discusses previous research works carried out in this field by different scholars related to election outcomes and tried to find the most suitable and appropriate methods in election predictions.

In today’s scenario, the websites play a very important and prime role in communicating organizational policies and vision to the entire globe. Rigorous and extreme efforts are required from different organizations to concentrate more on design part of the website to make them more beautiful and informative in nature. Websites are acting as online agent these days to facilitate the user groups with different types of activities without making them physically visiting the concerned organization. Webmasters plays pivotal role in designing websites and they are required to design websites after properly studying the user behavior. Different standards/guidelines have been recommended by various organizations to help webmaster in designing user-centric website compatible with all latest trends and technologies. Feedback facility on a website helps users to convey their grievances to the organization and at the same time helping organization in understanding user behavior in more close way. Authors have developed an online tool by using .NET Framework after thoroughly investigating different recommended guidelines to study feedback facility as Design issue while designing different kinds of websites. To study this, five different kinds of websites were undertook that includes Government, Commercial, Educational, Social networking, and Job portals. The automated tool developed by author function on the basis of W3C guidelines that are prescribed in document WAI 1.0 (Panta, Web design, development and security. Youngstown State University, 2009) [4]. The automated tool extracts the complete website code and then supplies that code to the parser for the purpose of rendering it thus by producing a result to determine the presence and absence of feedback facility in a given website. Results show that out of the five different websites undertaken for the study, the government websites shows the maximum positive results whereas other categories show the mild presences of feedback facility on their websites.

In the current era of technology, information systems have played a prominent role in problem solving. One of the key areas where information and communication technology can play a role is in healthcare. The utilization of electronic means for delivering or assisting in healthcare services to people can play a revolutionizing role in improving the living conditions of humanity. The main aim of involving the electronic means for healthcare is to improve the current state of services and facilities being provided to people. It includes the use of digital means for sharing information, use of intelligent applications and software tools for assisting health and social care workers, improving timely accessibility of essential medicines and health care information, tracking and predicting epidemic diseases, and strengthening national healthcare systems.

Websites are very important means of communication in this current era of information technology. Different institutions / organizations put lots of efforts to portray complete information on beautifully designed websites. Organizations these days concerned more in providing users with all facilities online through websites, which act as an interface through which a user can get his work done without physically visiting the organization. With this the responsibility of the designer and the concerned institutions / organizations increases manifold so that the websites behavior should remain interactive and quick enough for the user to avail all facilities through websites comfortably. Speed and size of a Website are directly related with each other. Size is very important when targeting users that don't have optimal Internet connections. Author in this paper developed an online tool using .NET Framework using C# to study webpage size as Design issue in various categories of the websites like Government, Commercial, Educational, Social networking and Job portals. The automated tool developed by author function on the basis of the different standards prescribed in W3C and prescribed in analysis performed in (2). The tool act like a parser and renders the complete code of the website and then produces result by examining the memory requirements of the component files that contribute to the total size of the website. The results produced shows that out of the five different categories of websites, it can be concluded that none of the website categories undertaken follows the recommended standards of the World Wide Web consortium showing huge violation as far as recommended page size for different websites is concerned.

The growth of web has been exponential. According to a study conducted in May 2005, there were a total of over 11.5 billion pages [1], documents on the World Wide Web, and most of them were in the invisible Web, or Deep Web. Same figure in March 2012 is around 55 billion pages [2]. The size of the web is expanding continuously like the universe after the big bang. The rapid growth of internet for commercial purpose has been noted by many researchers & practitioners & it has been almost impossible to escape its growth since its birth in 1990’s.The increasing amount of web development work being carried out in these organizations means that such work should be carried out in a well-planned & systematic manner. In designing Web pages, the background color combination is very important because it has a strong impact on the impression and accessibility of the information [3]. Web accessibility is becoming a prominent issue across the world, not only because of legal and compliance issues, but because of its impact on commercial opportunities [4]. Since the domain of a website is the whole world, we cannot ignore the people who are suffering from various Color impairments. An essential part of web design today is designing it in a way that it is equally viable to the individuals with limited abilities [5]. As such a website should be built in such a way as is equally accessible to all the people around the world—of all ages and abilities. The aim of the paper is to analyze the websites in order to check their accessibility to colorblind community. For the sake of study authors have selected a set of websites i.e websites of the various Universities in Jammu & Kashmir. The web sites of the universities are among the most accessed websites which involve heavy web traffic as such must be developed in such a manner that the format and design for the same be equally accessible and viable to the Color blind community as well.

With rapid changes in technology and its increased use in different organizations, the cybercrime and digital forensics methods are also making advancement in new ways to tackle the latest trends in cyber crime. Cybercrime refers to any crime that involves a computer network or any public or private system. Cyber crime is emerging as a serious threat worldwide. The government organizations, police departments and various intelligence units of different countries have started to act accordingly. To control and investigate cybercrime, the investigators use various Digital forensics methods and mechanisms. Digital forensics is the procedure of investigating computer crimes in cyber world. Many researchers have been done a lot in this area to help forensic investigators to resolve the existing challenges with different methodologies designed by them. Experts provided with different tools and technologies to resolve the threats related to cyber crime in a more efficient and speedy manner with minimum loss to the victim. Still the desired technologies and tools are not that much efficient that they can control the occurrence of different types of cyber crime activities. This paper reviews the complete details regarding the growth of cybercrime and its various modes of occurrence at different level. Authors in this paper tries to bring few facts and figures which would be an eye-opener for computer and internet users. Therefore, the current manuscript provides the understanding of various types of cyber crimes and its impact on different section of the society.

Web sites accessibility is a basic requirement to protect everyone's equal right of accessing the information in information society. These days it becomes mandatory that all kinds of vulnerable groups should be able to access information and acquire services from web sites. In the design process, enough attention has not been given to educational websites accessibility. After explaining the need of web engineering in websites design, the relation and the importance of web accessibility are studied and different kinds of vulnerable groups are identified. This paper introduces status of educational web accessibility and put forward the concept of educational web accessibility design. Firstly, role of web engineering in web accessibility is discussed; secondly, proposes five fundamental principals as different types of online learners and their relations with educational websites accessibility; thirdly, Function, design concept and seven principals of universal design are related with educational websites accessibility; fourthly, Design model of educational web accessibility is studied; lastly, it gives out basic design steps in designing accessible educational websites or in the end authors integrates the web accessibility into design process by using top down approach. Through these efforts of web engineering, design concept, principles and model of educational web accessibility, the levels of educational web accessibility will be improved.