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Rearing of silkworm is highly dependent on environmental variations. To have a healthy cocoon production, it is necessary to have a proper temperature and humidity controlled house for silkworm rearing. Temperature, humidity and fresh air should be managed to get a wonderful silk product. An ideal temperature of 23°C to 28°C and humidity in between 65% to 85% is to be maintained. IoT based silkworm rearing house consists of sensors and actuators, which are interfaced with a low power controllers. The Sericulture unit can be equipped with a wireless sensor node to sense the real time Temperature and Humidity [1], also necessary actuators to control these environmental parameters. The color change in the body of the worms indicates the different stages and the light yellowish indicates that they have reached to the cocoon stage and the morphological changes in silkworm structure can be used to detect abnormal worms[2].The proposed framework introduces an Internet of Things (IoT) empowered Wireless Personal Area Network (WPAN) system. The received image is first segregated into two classes as diseased or healthy by analyzing the histogram of the background removed image based on thresholding. Again the diseased class will be sub classified into 2 diseases as either Flacherie or Pebrine by applying suitable mask for extracting worm and obtaining the histogram of the worm and analyzing it. The result will be sent to the farmer via E-mail. The proposed system could be a probable solution for productivity in silkworms. View Article DOI: 10.47856/ijaast.2021.v08i9.004

The efficiency of selecting the cluster head plays a major role in resolving the complexities faced in network management aiming to improve the longevity of sensors in the network. The clustering process is followed by selecting proper cluster heads with the consideration of energy conservation among participant nodes. While coming to security concept on WSN, the trust based cluster head selection is significant with the assumption of cooperation of all sensor nodes. In view of this assumption, the traditional methods could not help in defining the ideal cluster head of the network. This work proposes Voronoi Clustered Secure Contextual Cryptographic Algorithm (VC-SCCA) by combining Voronoi method for clustering process and cryptographic algorithm for secure data transmission. This is considered as two-tier architecture whereas, clustering takes place in first tier and encryption along with decryption takes place in the second tier. The proposed algorithm is compared with two state-of-art methods such as, Secured WSN (SeC‐WSN) and Taylor based Cat Salp Swarm Algorithm (Taylor C-SSA) in terms of energy consumption, Packet Delivery Ratio (PDR), network lifetime, encryption time and decryption time. As a result, the proposed VC-SCCA achieves 53.2% of energy consumption, 98.6% of packet delivery ratio, 97.5% of network lifetime, 62.8sec of encryption time and 71.2sec decryption time.

Named Entity Recognition (NER) also known as entity extraction plays an important role in identifying and classifying the named entities into different categories like name of person, place, organization, things, quantity, monetary value etc. that appear in a document. NER has applications in various Natural Language Processing (NLP) tasks such as information retrieval, question answering system, Machine Translation, Sentiment Analysis. NER systems can be developed using rule based, machine learning or hybrid approach. Now Deep learning is being used to develop efficient NER as these models are capable of learning patterns easily and efficiently. Quite a large number of work has been done in English compared to the work done for Indian Languages. The focus of this paper is to highlight the challenges in building an efficient NER for one of the south Indian language namely Malayalam. The different issues that we need to address in order to develop an efficient NER for Malayalam is presented.

A robot is a machine which is programmed by a computer and the movements and functions of which are controlled by an external or an embedded control. It has dynamic uses in all domains of life. A robot in a university setting can be used as an attender for passing circulars around instead of multiple attenders doing the task manually which is a cost and time consuming process. Parents often find it difficult to navigate through the unfamiliar university. In this review paper, we have focused on a voice based attender robot with line following capabilities along with speech to text recognition that can be used for a variety of purposes at universities like passing around the circulars; interacting with parents and helping them navigate through the university. The aim of this work is to introduce a robot that it is able to interact with human through the Spoken Natural Language. Here, the focus will be on two languages; English and Kannada. This system uses an application to recognize human voice and is converted to text; text is further processed and is used to act or reply according to the user’s command. In this way, the voice based attender robot finds its applications in the university setting. But it is not limited to only universities. It can also be further implemented in places like railway stations, bus stations, big factories and other similar surroundings.

Healthcare Analytics is process of analyzing huge volume of healthcare data such as claims data, Electronic Health Records (EHR) pharmacy data, clinical data, patient behavior data etc. to leverage insights for making more informed decisions, improve patient care and services at various levels of the organization. Traditional Machine Learning techniques are limited in their ability to process and extract feature from raw data. Over the last couple of years this field has seen a huge transformation with advancements in Deep Learning, making it an ideal tool for healthcare analytics, where a huge amount of raw unlabeled and complex healthcare data can be processed faster and with higher accuracy. This chapter focuses on different deep learning applications, techniques, framework and challenges in dealing with Healthcare data.

Predictive models are used to predict the unknown future events using a set of relevant predictors or variables by studying both present and historical data. Predictive modeling is also known as predictive analytics that uses the techniques of statistics, data mining, and artificial intelligence that can be applied to a wide set of applications. A predictive model in healthcare learns the historical data of patients to predict their future conditions and determine the treatment. In this review, the use of deep learning models such as LSTM/Bi-LSTM (Long Short-Term Memory/Bi-directional LSTM), RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), RBM (Restricted Boltzmann Machine) and GRU (Gated Recurrent Unit) on different healthcare applications are highlighted. The results indicate that the LSTM/Bi-LSTM model is widely used in time-series medical data and CNN for medical image data. A deep learning model can assist healthcare professionals to make decisions regarding medications, hospitalizations quickly and thus save time and also serve the healthcare industry better. This paper analyzes the various predictive models used in healthcare applications using deep learning.

Immersive technology refers to technology that enhances reality by blending the physical environment with virtual content or by completely taking the user to a virtual world far away from reality. Different immersive technologies are augmented reality (AR), virtual reality (VR), and mixed reality (MR). As immersive technology is becoming more affordable, user-friendly, pervasive, and ubiquitous, it's been adopted and embraced by several industries. Though its early adopters were from the gaming industry, now it's explored and used by many other industries such as mining, healthcare, and medicine, retail, education, automotive, manufacturing, etc. Using these technologies, medical professionals can improve their competencies, and they will be able to effectively transfer the skill acquired through simulations to the operation theatre. This chapter focuses on uses, benefits, and adoption challenges of Immersive technologies with specific reference to healthcare training.

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed.

Protecting sensitive data and maintaining the confidentiality is the most important role in this world. Especially many areas like Space, Defense, Education, Finance, Communications, international affairs sectors. The Multilevel Security (MLS) approach provides a relevant framework for the analysis of security systems regarding cross-level approach through the cyber-physical system. We propose a new key management system for multilevel security in the threat model security policies- security mechanism. The new approach is to construct the MLS through Cyber Physical Systems (CPS) in association with advanced cryptographic tools to protect the defense confidential data from unauthorized, and also to make sure that the data is not released to any counterfeit users. In this paper, a prototype is selected to explain the security of cryptographic strategy like symmetric encryption, message authentication codes, public key encryption, key agreement protocols, and digital signature schemes.

A robot is a machine which is programmed by a computer and the movements and functions of which are controlled by an external or an embedded control. It has dynamic uses in all domains of life. A robot in a university setting can be used as an attender for passing circulars around instead of multiple attenders doing the task manually which is a cost and time consuming process. Parents often find it difficult to navigate through the unfamiliar university. In this paper, we have focused on a voice based attender robot with line following capabilities along with speech recognition that can be used at universities for a variety of purposes like passing around the circulars, interacting with parents and helping them navigate through the university through Spoken Natural Language. The main objectives of the proposed work is to reduce the burden of passing circulars, calling a student/faculty on the attender by designing a robot that is also competent enough to connect with human through spoken natural language such as English or Kannada, so that it interacts with parents who are new to the institution and do not know whom to approach. The main aim of this work is to introduce a robot that it is able to interact with human through the Spoken Natural Language. Here, the focus is on two languages; English and Kannada. This system uses a voice recognition module to recognize human voice and a voice playback module is used to reply back in either English or Kannada according to the user’s command. It can work in two modes, the voice recognition mode to answer to user queries or in line following mode to pass circulars, call student/faculty. In this way, the voice based attender robot finds its applications in the university setting. But it is not limited to only universities. It can also be further implemented in places like railway stations, bus stations, big factories and other similar surroundings.

The exigent problem faced by the paralyzed people is their independent ambulation. They need an external help to perform their daily activities. The person affected by Hemorrhagic disease, a type of stroke which leads to Quadriplegia. These kinds of patients cannot access their limbs where only head tilting is possible. Assist devices used by quadriplegic patients are very complicated, rarely available and expensive. The main objective of this project is to propose an idea for an automated system for the disabled people. The ambulating chair will work based on the head movement of the user. The recognized gestures are used as an input to the controller so that the assist device can be controlled according to the user intension. The wheel chair system is divided into two main units: MEMS Sensor which consists of accelerometer that detects direction of motion and Controller. The MEMS sensor senses the change in direction of head and accordingly the signal is given to microcontroller. Depending on the direction of the Acceleration, microcontroller controls the wheel chair directions like LEFT, RIGHT, FRONT, and BACK with the aid of DC motors.

Mobile ad hoc networks (MANETs) require a unique, distinct, and persistent identity per node in order for their security protocols to be viable, Sybil attacks pose a serious threat to such networks. Fully self-organized MANETs represent complex distributed systems that may also be part of a huge complex system, such as a complex system-of-systems used for crisis management operations. Due to the complex nature of MANETs and its resource constraint nodes, there has always been a need to develop security solutions. A Sybil attacker can either create more than one identity on a single physical device in order to launch a coordinated attack on the network or can switch identities in order to weaken the detection process, thereby promoting lack of accountability in the network. In this research, we propose a scheme to detect the new identities of Sybil attackers without using centralized trusted third party or any extra hardware, such as directional antennae or a geographical positioning system. Through the help of extensive simulations, we are able to demonstrate that our proposed scheme detects Sybil identities with 95% accuracy (true positive) and about 5% error rate (false positive) even in the presence of mobility.

In this study, we describe the design and successful implementation of the smart healthcare monitoring device. This design helps to view the Electrocardiogram signal and heart beat count can measure at very low cost. The real time ECG signal is viewed on laptops and transmitted to mobile phone through Bluetooth with the help of android software. AD8232 is Electrocardiogram sensor which amplifies the signal extracted with the help of ECG electrode. This design helps in monitoring ECG of patients in remote area.

A Blockchain is basically a decentralized, distributed ledger of all the transactions or events which takes place within a network that involves multiple parties. The transactions that happen are anonymous hence it has very high level of security. Any transaction that takes place is verified by all the members present in the chain. However, there are some drawbacks as well like scalability. This technology can be used in different areas and one of them is the travel industry which can change the whole transaction dynamics of the traditional way the travel sector works. This paper will give the outline or an idea of the underlining concepts about this new technology and how it can be used in travel industry.

Humans have a pair of lungs which is one of most vital and sensitive organs of the human body. Computed tomography (CT) is a diagnostic medical test that uses x-ray equipment to detect various health conditions. Also, this method is regarded as painless, noninvasive and accurate. Thus, we have obtained CT scanned images of three healthy subjects and in this paper we aim at obtaining signal to noise ratio after image extraction and segmentation. Then, filtering (using five filters) followed by enhancement using Adaptive Histogram Equalization is carried out. After enhancement, we calculate the signal to noise ratio again. Later, we discuss which filter gave the best result among the five filters. Signal to noise ratio is a measure to know the signal power present to the noise power included.

Internet of Things or IoT is an ecosystem of different physical objects provided with unique identifiers embedded with electronics, software, sensors and network connectivity which enables these objects to collect and exchange data without human intervention. The different technologies comprising IoT are Wireless sensor network, Cloud Computing, Micro-electromechanical systems (MEMS), Semantic technologies and future Internet. This concept makes it possible for the devices to be connected all the time everywhere, so it can also be referred to as Internet of Everything. Health care or Healthcare is the improvement of health in human beings by diagnosis, treatment and prevention of diseases, injury, and accidents, physical and mental impairments. It helps in the general physical, mental health and well being of people around the world. It comprises all the work done by Health professional in improving the primary care, secondary care and tertiary care of public. This chapter focuses on how the capabilities of Internet of things (IoT) can be leveraged in providing better Healthcare. In this chapter, various applications of IoT in healthcare as well as the challenges in the implementation are highlighted.

Global Navigation Satellite System (GNSS) is a satellite-based navigation and spatial positioning system with global coverage. Out of the many satellite navigation systems developed, the best possible in practice is Global Positioning System (GPS) which is composed of a total of 24 satellites (21 operational and 3 backup). The accuracy of GPS can be improved by Satellite Based Augmentation System (SBAS). The Indian SBAS is GAGAN (GPS Aided GEO Augmented Navigation). It supports wide area or regional augmentation using additional satellite broadcast messages. GAGAN is a planned implementation of SBAS by the Indian government. It is a system to improve the accuracy of GNSS receiver by providing reference signals. In this work, experimental data is obtained from JUNO SA single frequency GPS/GAGAN receiver under various conditions such as during morning, afternoon, evening, open-sky, half-sky, heavy canopy and under portico (half open sky). The work is carried out at Department of Electronics & Communication Engineering (ECE), University College of Engineering (UCE), Osmania University (OU), Hyderabad (17.4135° N, 78.5287° E). The user position is obtained separately using standalone GPS and GAGAN. It is observed that the parameters such as Accuracy, PDOP (Positional Dilution Of Precision), HAE (height above ellipsoid), etc. differ under various environmental conditions such as during day/night, open sky/heavy canopy, within closed structures etc.

Commodity trading is one of the most popular resources owning to its eminent predictable return on investment to earn money through trading. The trading includes all kinds of commodities like agricultural goods such as wheat, coffee, cocoa etc., and hard products like gold, rubber, crude oils etc.,. The investment decision can be made very easily with the help of the proposed model. The proposed model correlation based multi layer perceptron feed forward adaline neural network is an integrated method to forecast the future values of all commodity trading. The correlation based adaline neuron is used as an optimized predictor in the multi layer perceptron feed forward neural network. The correlation is used for feature selection before building the predictive model. The main aim of the paper is to build the predictive model for commodity trading. The model is created using correlation based feature selection and adaline neural network to prognosticate all future values of commodities. The adaptive linear neuron is formed with the help of linear regression. To implement the proposed model the live data is captured from mcxindia. The mcxindia is considered as one the popular website for doing commodities and derivatives in India. To train the proposed model, few random samples are used and the model is evaluated with the help of few test samples from the same data set.

Data Anonymization is one of the globally accepted mechanisms for the protection of privacy of individuals in data publishing scenario. Normally the data anonymization impacts on the quality of data especially critical to the success of knowledge-based applications. An intelligent approach based on association mining namely, Adaptive Utility-based Anonymization (AUA) has been proposed in order to deal with this issue. Initially the model is tested with sample instances of original data set National Family Health Survey (NFHS-3) and this paper includes performance evaluation of AUA model using data sets and proves that the data anonymization can be done without compromising the quality of data mining results.

The rapid growth of internet has paved the way for recommender systems as users are inundated with a variety of choices. A computer system that provides recommendations/suggestions to the users is called Recommender Systems (RS). These systems are a subclass of information filtering systems that provides suitable recommendations to users about a product, movie, song, mobile applications etc. RS are being widely used in social networking sites like Facebook, Twitter, LinkedIn etc. to help users choose product/friend/job from the various recommendations. These systems analyze patterns of user interest in posting information, sharing posts or joining a new group to provide personalized recommendations that suit user’s taste. RS are popular in many areas especially for predicting/suggesting what the users want in advance and thus help in reducing search costs. Recommendations are done using either collaborative, Content-based filtering or hybrid approach. Content-based recommendation system recommends based on user profile created by analyzing the items the user has liked/rated in the past. A collaborative recommendation system, on the other hand, does suggestions based on the preferences/ratings of our previous collaborators (users whose liking was similar to ours). The popularity of social networking sites is increasing with more number of users being added daily. This paper discusses the various classification and applications of RS in some of the popular networking sites.

The return on investment of stock market index is used to estimate the effectiveness of an investment in different savings schemes. To calculate Return on Investment, profit of an investment is divided by the cost of investment. The purpose of the paper is to perform empirical evaluation of various multilayer perceptron neural networks that are used for obtaining high quality prediction for Return on Investment based on stock market indexes. Many researchers have already implemented different methods to forecast stock prices, but accuracy of the stock prices are a major concern. The multilayer perceptron feed forward neural network model is implemented and compared against multilayer perceptron back propagation neural network models on various stock market indexes. The estimated values are checked against the original values of next business day to measure the actual accuracy. The uniqueness of the research is to achieve maximum accuracy in the Indian stock market indexes. The comparative analysis is done with the help of data set NSEindia historical data for Indian share market. Based on the comparative analysis, the multilayer perceptron feed forward neural network performs better prediction with higher accuracy than multilayer perceptron back propagation. A number of variations have been found by this comparative experiment to analyze the future values of the stock prices. With the experimental comparison, the multilayer perceptron feed forward neural network is able to forecast quality decision on return on investment on stock indexes with average accuracy rate as 95 % which is higher than back propagation neural network. So the results obtained by the multilayer perceptron feed forward neural networks are more satisfactory when compared to multilayer perceptron back propagation neural network.

The digitization of medical data, field of genomics and use of wearable sensors to monitor patient health are some of the factors that have dramatically increased the growth of Big Data in Health Care/Biomedicine. Big data in healthcare actually refers to electronic health data sets which are large and complex that is very difficult to manage with traditional/conventional data management tools and techniques. Big data analytics in healthcare is cumbersome not just because of its volume but also because of the diversity of data types and the speed at which it is generated and must be managed/analyzed. Rapid progress is to be made for analyzing this data and for gleaning new insights for making better informed decisions. There are unprecedented opportunities to use big data. The Health Care Industry should find methods to properly analyze this Big HealthCare Data generated and stored around the world each seconds in order to discover associations, understand the patterns and trends which will provide significant opportunities for real-time tracking of diseases, predicting disease outbreaks, to improve care, save lives and lower costs. Extraction, integration and analysis of heterogeneous, enormous and complex HealthCare data captured from various Electronic Health Care sources are a major challenge. New methods, applications and tools that are used by Healthcare industries, practitioners and researchers to tackle the big data challenges are discussed in this paper.

Critical challenge in developing a privacy protection mechanism is to preserve maximum information because protection mechanisms normally impact on the quality of data and which are served not accordingly with the data utility. Practical solutions to address various socio-economic needs with special emphasize on the utility of data have not been devised yet. To publish maximum information while protecting the privacy, we propose an intelligent mechanism and this paper includes a comprehensive study and explores how effectively the privacy of individuals can be protected with minimum information loss. Empirical evaluations on original health care data related to Indian Population show the effectiveness of the new approach, namely Adaptive Utility-based Anonymization (AUA).

Parts Of Speech (POS) tagging is one of the basic text processing tasks of Natural Language Processing (NLP). It is a great challenge to develop POS tagger for Indian Languages, especially Kannada due to its rich morphological and highly agglutinative nature. A Kannada POS tagger has been developed using Conditional Random Fields (CRFs), a supervised machine learning technique and it is discussed in this paper. The results presented are based on experiments conducted on a large corpus consisting of 80,000 words, where 64,000 is used for training and 16,000 is used for testing. These words are collected from Kannada Wikipedia and annotated with POS tags. The tagset from Technology Development for Indian Languages (TDIL) containing 36 tags are used to assign the POS. The n-gram CRF model gave a maximum accuracy of 92.94 %. This work is the extension of “Parts of Speech (POS) Tagger for Kannada Using Conditional Random Fields (CRFs).

Data in the healthcare sector is growing beyond dealing capacity of the health care organizations and is expected to increase significantly in the coming years. Majority of the Healthcare data is often unstructured, exists in silos and resides in imaging systems, medical prescription notes, insurance claims data, EPR (Electronic Patient Records) etc. integrating these heterogeneous data and factoring it in to advance analytics is critical to improve healthcare outcomes. Either because data are isolated in disparate or incompatible formats or due to the lack in processing capability to load and query large datasets in a timely fashion the Healthcare organizations are not in a position to leverage the benefits of the vast data they have. With convergence of advanced computing and numerous Big Data technological options like commercial solutions, Open Source, Cloud etc. it is now possible to attain high performance, scalability at a relatively low cost. Big data solutions often come with set of innovative data management solutions and analytical tools, when effectively implemented can transform the healthcare outcomes.

Domain — specific search focuses on one area of knowledge. Applying broad based ranking algorithms to vertical search domains is not desirable. The broad based ranking model builds upon the data from multiple domains existing on the web. Vertical search engines attempt to use a focused crawler that index only relevant web pages to a predefined topic. With Ranking Adaptation Model, one can adapt an existing ranking model of a unique new domain. The binary classifiers classify the members of a given set of objects into two groups on the basis of whether they have some property or not. If it is property of relevancy, it is returned to the search query of that particular domain vertical. Sponsored ads are then placed alongside the organic search results and they are ranked with the help of bid, budget and quality score. The ad with the highest bid is placed first in the ad listings. Later, the ad with a maximum quality score is found by click through logs which is replaced in first position. Thus, both organic search and sponsored ads are returned for the specific domain, making it easy for the users to get access to real time ads and connect directly with advertisers as well as to get information on the search query.

Along with rapid technological advancements, the need for developing suitable frameworks for protecting privacy of individuals becomes essential for the wide-spread acceptance of knowledge-based applications. Privacy Preserving Data Mining has become an active area of research recently to address privacy issues whenever the data is to be provided for a variety of purposes like survey, research etc. Several remarkable frameworks are being developed, but there is not enough sensible solution for considering both privacy and information evenly. Privacy mechanisms which compromise with the information usually weaken the quality of data mining results. An intelligent framework to address this issue is proposed in this paper which also discusses empirical results on classification using original health care data related to Indian population, namely NFHS-3 and shows the effectiveness of our approach.

This paper proposes a dynamic watermarking model for the purpose of medical authentication. While transferring the data through a public network there is jittering or tampering of data. This is a matter of concern as any jitter or tampered data is not desirable in the medical field. It is noted that there is loss of life due to corrupted data received leading to wrong diagnosis. The proposed dynamic model proves that the medical image watermarked with the proposed system provides near lossless original image. Since the watermark is generated dynamically it is unique to the images considered therefore enhances the security of the images. The Proposed scheme is in the TIFF (Tagged Image File Format) using RGB colour space. The given watermark

Project scoping of features and functionalities required to meet the system objectives is critical for project success. An accurate determination of the system boundary to develop software has long been identified as a challenge that practitioners have been attempting to overcome. It is, therefore, important to understand the causes along with the consequences of poor project scope. This study presents the findings pertaining to project scope that influence the requirements elicitation activity and thereby impact the overall requirements engineering process. The causes that lead to such scope issues and related consequences are summarized. The challenges encountered due to poor scope are depicted using a cause-and-effect diagram. This study provides guidance that can assist practitioners to identify areas for improvements in determining project scope and that which can have a positive influence on the overall requirements elicitation process.

Software requirements elicitation is an important and essential pre-requisite to the subsequent phases in software development lifecycle. There is an increasing focus on how industry performs elicitation as this has a direct influence on the overall project success. It is important to understand broader elicitation issues and challenges, and address them on a large-scale, especially on geographically distributed software development. There are studies focusing on elicitation, but they are relatively small. There is no substantial research related to providing a comprehensive view of elicitation issues along with its causes and effects. This paper attempts to provide a summary of the systematic literature review (SLR) findings from 81 papers. The findings are based on causes of poor elicitation, elicitation issues and challenges, consequences of poor elicitation, advisable practices and classification of elicitation issues. The authors have leveraged cause-and-effect diagrams to draw conclusions on SLR.

Software testing is a method of determining problems before an application is promoted into production. It is the process used to measure the quality of the software developed. It is expensive, difficult, inaccurate, and time consuming to test an application using purely manual methods. As technology has developed there has been a desire to move away from manual testing and adapt automated testing. But finding a test tool that will meet our entire testing requirement is difficult, at best. Especially Graphical User Interface (GUI) tools that are available in the market is not very easy to use and flexible to perform stress automation. This paper looks at the problems persisting in on commercially available automation tools and describes the proposed new techniques and solutions which will be implemented on GUIRobo.

Digit(al)isation of Cultural Heritage is a multidimensional process that helps in the rescue of European Cultural Identity, and the paradigm of Digital Cultural Heritage (DCH) is a valid instrument for social and cognitive inclusion of museum visitors. In light of disseminating and validating new paradigms for the enjoyment and exploitation of Cultural Heritage (CH) artifacts, this chapter shows main first results from CIVITAS (ChaIn for excellence of reflectiVe societies to exploit dIgital culTural heritAge and museumS). The project develops virtual/augmented environments, through the multisensorial interaction with virtual artworks, to satisfy needs and overcome limitations in a larger CH scenario, applying a bottom-up approach. The research presented show a robust and interdisciplinary approach applied to Ducal Pace at Urbino: key activities and faced challenges demonstrated to test cross-fertilization strategies, involving multilayered issues.