Fourth International Congress on Information and Communication Technology

Online healthcare and wellness applications are one of the alternatives to expensive face-to-face medical services. By using online healthcare applications, patients are able to access clinical interventions at a lower cost and at their convenience. However, the lack of rigorous evaluation system becomes a barrier to further growth. To address the shortcoming, in this paper, we propose the privacy-preserving evaluation system for online healthcare applications, in short PESOHA. The proposed system helps non-IT healthcare professionals easily to create their clinical studies on their online applications and assess the performance of online health applications by using privacy-preserving online monitoring. In this paper, we present the structural architecture of PESOHA and demonstrate its operations for healthcare researchers and patients. The systematic evaluation method of PESOHA will expand the role of online healthcare and wellness applications.

Construction sites are among the most hazardous venues. While most of the previous research has shed light on the human aspect, we propose to utilise the fast R-CNN object detection method to detect the construction hazard on sites and employ mixed reality to enable the artificial intelligence to detect the hazard. Fast region-based convolutional neural network object detection acquires expert knowledge to identify objects in the image. Unlike image classification, the complexity of object detection always implies an increase in complexity which demands solutions with regard to speed, accuracy and simplicity.

Nowadays, in wireless sensor networks (WSNs) field, the advances in electronics, wireless communications, and data processing have become an important reality. Wireless sensor networks are employed to eliminate problems occurred in health care, monitoring, agriculture, etc. Data reduction is considered as the best method that reduces dimensionality of the database. So, it helps outlier detection technique to classify data during training. In our work, we have constructed a newest data reduction process using non-negative matrix factorization (NMF). This method finds out the nature of data it is regular or outlier. Also, it can give a highest performance. Compared to various methods like Fisher Discriminant Analysis (FDA) and Principal Component Analysis (PCA), our method based NMF are considered as the most efficient and accurate for detecting outlier in WSNs. As real datasets, we use LUCE, Intel Berkeley and Grand St-Bernard. So based on the obtained results, our method is considered as a perfect one used in wireless sensor networks.

Overall customer satisfaction is the most critical quality measure for all bespoke and commercial-off-the-shelf (COTS) software systems deployed in user environments. This study measured the satisfaction of users of a student administration software which was acquired and reconfigured by the IT department at an African university. As a student administration software, the product has all the necessary functionalities. However, most users of the software struggle to effectively and confidently use the software on their own without assistance from the IT department of the university. Although the software is required for capturing and processing students’ academic records by all academic staff, it appears there is a general feeling of boredom by many staff toward the software usage at this university. Therefore, this study aims to investigate the level of satisfaction of users of the software and if there is any correlation between satisfaction of use and user participation in the requirements engineering or reconfiguration process of the software. The study was conducted purely as a private academic initiative undertaken in order to investigate the perceived complaints among the users of the software and to propose professional solution to the problem. The study observed all the necessary steps required for conducting a scientific survey study of this nature including ethical considerations of confidentiality of information obtained from participants, optional user participation in the study (as indicated on the survey instrument), and proper acknowledgment of all literature sources referenced in the present study. The study employed a quantitative approach with a sample of 40 participants who were randomly selected from four different faculty units of the university. The Qualification Weighted Customer Opinion with Safeguard (QWCOS) model was used as initial external measurement (EM) of customer satisfaction. A Flexible Qualification Weighted Customer Opinion with Safeguard (FQWCOS) model was also applied to measure EM. Both results of EM were compared. In all, results from the study suggest that 85% of the sample were not very satisfied with the software, 62.5% will not recommend the software to others, and over 80% of the users did not participate during the requirements reconfiguration of the software before its deployment. There is a significant relationship between participating in requirements configuration and user satisfaction. Furthermore, both QWCOS and FQWCOS yield the same EM values. To improve customer satisfaction on software products and services, a 2D4E framework based on requirements and reconfiguration engineering (RRE) and total quality management (TQM) is proposed in this study.

Recently, the environmental pollution has seriously affected the production and life of people in many countries in the world and especially in Vietnam. In particular, aquaculture has been severely affected by the polluted environment, the waste from factories, agricultural production, or even from aquaculture itself. That raises the questions “how to monitor the water quality of aquaculture areas 24/7 and to promptly alert aquaculture farmers and managers to take appropriate response?” Facing this situation, Can Tho University has invested to conduct research and to develop a system to monitor water quality of aquaculture areas based on the actual conditions of the Mekong Delta. In this presentation, we will first describe the agent-based environment monitoring system (AEMS) model, combined triple technologies (sensor, IoT, and agent-based) model. The second introduction is the water quality monitoring system for aquaculture settings based on the AEMS model, we proposed. Thanks to the implement of AEMS model, it not only helps managers and farmers to monitor indicators of water quality at any time, but it also helps to analyze collected data from IoT Agent, send warning messages, and solutions deal with the situation of water environment in each aquaculture farms.

While there exist many systems in the literature for detecting unsafe behaviors of workers in buildings such as staying-in or stepping into unauthorized locations called intrusions using spatio-temporal data. None of the current approaches offer a mechanism for detecting intrusions from the perspective of a dynamic environment where the building locations evolve over time. A spatio-temporal data model that is required to store worker trajectories should have a capability to track a building evolution and seamlessly handles the enrichment of stored trajectories with the relevant geographical and application-specific information sources for studying the worker behaviors using a building or a construction site context. To address this requirement of maintaining the information, which is generated during the building evolution and for constructing semantically enriched worker trajectories using the stored building information. This work reports a system which offers the ability to perform user profiling for detecting intrusions in dynamic environments using semantic trajectories. Later, Building information modeling (BIM) approach is used for visualizing the intrusions from a standpoint of a building environment so that necessary actions can be performed proactively by the safety managers to avoid unsafe situations in buildings.

The amount of knowledge in organic chemistry grows exponentially inducing a need for robust intelligent systems that can promote the process of R&D. Although the methods of intelligent system design vary significantly juxtaposing expert systems, neural networks, genetic algorithms, and fuzzy logic, effective intelligent system development can start only after answering the following essential questions: “How is the application area structured? What is its ontology?”. Ever since the DENDRAL Project, the challenge of knowledge representation has been embraced by the scientific community. The notion of ontology has appeared in knowledge engineering delivering a possible solution. As the practice shows, taxonomies provide little expressiveness. Therefore, we suggest that the ontological approach advocates consider applied logic methodology. This framework proposes that complex-structured domains, such as organic chemistry, be represented as interconnected modules of applied logic theories. Employing the described technique, we introduce the model of organic chemistry intelligent system. Most special aspects of this methodology are depicted together with a historical overview of intelligent systems and the roots of knowledge representation models.

This paper is mainly proposed to identify the text given in the input handwritten object format (irregular shape), i.e., recognized/unrecognized patterns by utilizing chain code using SASK algorithm. To find out the output of the written object, we would need to identify the hulls and layer of it using the layered algorithm which is explained in detail in further sections. After identifying the layer, we are supposed to get out the list of outcomes that suits for the output pattern, and hence, that will be provided as the recognized object. It helps us to easily identify the outcome of the text provided in handwritten, where the input is either in recognized or unrecognized format. Another advantage apart from that is by identifying structured layers will be helpful in the traffic to define the traffic time/list of vehicles waiting on the queue to get way further.

The use of electronic voting is gradually replacing the traditional one. However, the issue of creating an honest and reliable electronic voting system is still open. Creating a reliable e-voting system that meets all security requirements is a difficult task. The paper presents an electronic voting system based on the principle of blind intermediaries using multiple ballots. The basic requirements for systems are described: eligibility, fairness, individual verifiability, universal verifiability, vote-privacy, receipt-freeness, coercion-resistance, vulnerable software/hardware resistance. Blind intermediaries allow you to exclude the user’s vote from communicating with any authentication data. The principle of multiple ballot casting allows verification of the correctness of the ballot without the use of evidence with zero disclosure, as well as ensures compliance with other requirements for electronic voting systems. The system architecture is described, and the components involved in the voting process are described. The voting process is described, consisting of several stages: preparation, voting, counting of results. Cryptographic protocols are used, which are used for data transfer between system components. The compliance with the security requirements of the system is justified.

It has become a significant research direction to resist cyberattacks through traffic identification technology. Traditional traffic identification technology is often based on network port or feature matching, which has become inefficient in the increasingly complex network environment. Nowadays, the malicious cyberattacks usually encrypt their traffic to escape the traditional traffic identification, and the most common encryption method is the SSL/TLS encryption. In response to this phenomenon, this paper proposes an encrypted malicious traffic identification method based on the random forest, which uses features based on packet information, time, TCP Flags field, and application layer payload information. We designed the technology and application framework to ensure the success of the experiment and collected a large amount of SSL/TLS encrypted traffic as datasets. Benefit from model optimization by parameter adjusting, the experimental results showed that final model had highly accurate and predictive ability.

The startingMarksteiner, Stefan point of securing a network is having a concise overview of it. As networks are becoming more and more complex both in general and with the introduction of IoT technology and their topologicalJandl-Scherf, Bernhard peculiarities in particular, this is increasingly difficult to achieve. Especially, in cyber-physical environments, such as smart factories, gaining a reliable picture of the network can be, due to intertwining of a vast amount of devices and different protocols, a tedious task. Nevertheless, this work is necessary to conduct security audits, compare documentation with actual conditions or find vulnerabilities using an attacker’s view, for all of which a reliable topology overview is pivotal. For security auditors; however, there might not much information, such as asset management access, be availableLernbeiß, Harald beforehand, which is why this paper assumes network to audit as a complete black box. The goal is, therefore, to set security auditors in a condition of, without having any a priori knowledge at all, automatically gaining a topology oversight. This paper describes, in the context of a bigger system that uses active scanning to determine the network topology, an approach to automate the first steps of this procedure: passively scanning the network and determining the network’s scope, as well as gaining a valid address to perform the active scanning. This allows for bootstrapping an automatic network discovery process without prior knowledge.

Over the years, a clear and steady rise can be seen in the interest in veganism. Although research has been conducted to determine the reasons why veganism has grown, ultimately there is still a necessity for further research on how social networks affect its growth. This paper aims to provide a possible explanation for the rise in interest, using computational analysis based on a temporal-causal network model focussing on social contagion. This model portrays a simulation of a sample size population on Instagram, showing how a social influencer can influence the opinions of people directly (influencers’ followers) and indirectly (followers of the influencers’ followers), and how this compares to a situation in which this influencer is not there.

Driver’s fatigue causes fatal road crashes and disrupts transportation systems. Specially in developing countries, drivers take more working hours and drive longer distances with short breaks to gain more money. This paper develops a new real time, low cost, and non-intrusive system that detects the features of the drivers’ fatigue. More specifically, the system detects fatigue from the eye closer and yawning. First, the face landmarks are extracted using the histogram of oriented gradients (HOG). Then, the support vector machine (SVM) model classifies the fatigue state from the non-fatigue. The accuracy of the SVM model presented by the area under the curve (AUC) is 95%. The system is evaluated with 10 participants in conditions that can affect the detection of the face. These conditions are different light conditions, gender, age groups, people wearing reading glasses, and males with beard and moustache around their mouth. The results are very promising, and it is 100% accurate.

This document is intended to provide an overview on decentralized autonomous corporations (DACs) based on the blockchain technology. We provide definitions of (1) DACs, (2) secure multiparty computation—a secure multiparty protocol for authorizing transactions, (3) autonomous agents—a set of computer programs that carry out some set of operations on behalf of users. We conclude the document with an application for financial-portfolio management.

As the value of bitcoin increases, more incidents such as those involving Mt Gox and Bitfinex will occur in standard centralised systems. The addition of group-based threshold cryptography with the ability to be deployed without a dealer and which supports the non-interactive signing of messages provides for the division of private keys into shares that can be distributed to individuals and groups to provide additional security. This scheme creates a distributed key generation system for bitcoin that removes the necessity for any centralised control list minimising any threat of fraud or attack. In the application of threshold-based solutions for DSA to ECDSA, we have created an entirely distributive signature system for bitcoin that mitigates against any single point of failure. When coupled with retrieval schemes involving CLTV and multisig wallets, our solution provides an infinitely extensible and secure means of deploying bitcoin. Using group and ring-based systems, we can implement blind signatures against issued transactions.

With the rapid growth in network-based applications, new risks arise, and different security mechanisms need additional attention to improve speed and accuracy. Although many new security tools have been developed, the fast growth of malicious activities continues to be a severe issue, and the ever-evolving attacks create serious threats to network security. Network administrators rely heavily on intrusion detection systems to detect such network intrusive activities. Machine learning methods are one of the predominant approaches to intrusion detection, where we learn models from data to differentiate between abnormal and normal traffic. Though machine learning approaches are used frequently, a deep analysis of machine learning algorithms in the context of intrusion detection is somewhat lacking. In this work, we present a comprehensive analysis of some existing machine learning classifiers regarding identifying intrusions in network traffic. Specifically, we analyze classifiers along various dimensions, namely feature selection, sensitivity to hyperparameter selection, and class imbalance problems that are inherent to intrusion detection. We evaluate several classifiers using the NSL-KDD dataset and summarize their effectiveness using a detailed experimental evaluation.

Obesity is becoming one of the prevalent lifestyle diseases across the globe; need to be deal with behavioral intervention through self-management and motivation in line with rigorous physical exercise. When it is a matter to handle obesity to regain the health, parameters like: self-motivation, self-control, guided treatment, counseling, monitored exercise, and medical assistance are essential in consideration list. Paper proposes the model encompassing three-dimensional care including nutritional intake, counseling, and gait monitoring during exercise. Considering the effect of obesity on biomechanics of foot, gait pattern analysis of obese person provides greater information regarding variations in spatio-temporal parameters. Ever-increasing contribution of behavioral intervention will maintain the line of action in the perfect direction. A selection of accelerometer, gyroscope, and electromyography sensors is appropriate for the cause to derive basic hardware. MSP430 processor and ZigBee module are used for processing information and establishing communication. Within close proximity and placement of nodes at a different level, nodes are able to achieve 90–94% packet delivery ratio in actual environment compare to the 100% packet delivery in simulation environment. Result suggests that with the adaption of accurate classification process, system could be useful for controlled exercise monitoring or for daily activity monitoring, which is working at low-power level with affordable wearable technology in achieving wellness.

In this paper, a temporal-causal network model is introduced for a burnout in relation to sleep. The network model approach shows the impact of different lifestyle, personal and job factors on the development of a burnout. This model, for instance, can be used to schedule night shifts in order to preserve the needed recovery of exhaustive, irregular sleeping patterns or to investigate the effects of certain in lifestyles induced triggers on burnout.

In this study, the forming of social communities and segregation is examined through a case study on the involvement in the queer community. This is examined using a temporal-causal network model. In this study, several scenarios are proposed to model this segregation and a small questionnaire is set up to collect empirical data to validate the model. Mathematical verification provides insight into the model’s expected behavior.

The number of end points connecting to the cloud can increase distributed attack vectors due to vulnerable devices connecting from the front end. The risk is also enhanced due to the technological abstractions associated with public cloud computing models at the back end. On the one hand, cloud service providers make sets of defined service criteria and supporting documentation, publicly available to assist customers with their public cloud deployments. However, on the other hand, a cacophony of security incidents over the past five years involving vulnerable cloud customer instantiations reveals that cloud security risks may not be completely comprehended. Essentially, the fundamental principle of cloud computing is the ‘shared security responsibility’ model. It is argued in this paper that from a cloud customer perspective, there is either too much reliance upon legacy risk assessment methods and/or standards orientated compliance-mapping approaches when trying to apply due diligence for cybersecurity. This can be amplified by different cloud service providers using terms like ‘core services’ and ‘managed services’ rather than traditional terms such as Infrastructure-as-a-Service and Platform-as-a-Service. This extended paper describes the myriad of techniques used to derive a conceptual framework through post-graduate research. Based around a defense-in-depth model, the proposed conceptual framework is a proof of concept to enable customers to focus on the contextualized risks when using the public cloud. A method of reducing the risks using mitigation categories is also proposed. Consequently, a method of calculating residual risk against the identified risks levels is theoretically defined and dependent upon the rigor of counter-measure selection.

Widespread interest in the emerging area of predictive analyticsAl-Aqrabi, Hussain is driving the manufacturing industry to explore new approaches to the collection and management of data through Industrial Internet of Things (IIoT) devices. Analytics processing for Business Intelligence (BI) is anHill, Richard intensive task, presenting both a competitive advantage as well as a security vulnerability in terms of theLane, Phil potential for losing Intellectual property (IP). This article explores two approaches to securing BI in the manufacturing domain. Simulation results indicate that a Unified Threat Management (UTM) model is simplerAagela, Hamza to maintain and has less potential vulnerabilities than a distributed security model. Conversely, a distributed model of security out-performs the UTM model and offers more scope for the use of existing hardware resources. In conclusion, a hybrid security model is proposed where security controls are segregated into a multi-cloud architecture.

The private sector constantly develops new innovative technologies and governmentsLõhmus, Kaisa are always eager to adopt emerging technological possibilities, in order to respond better to citizens needs. While this promises benefits, it can also bring uncertainty from a legal perspective. The implicationsNyman-Metcalf, Katrin of emerging technologies and how these can be regulated effectively are of great importance. The aim of this paper is to answer, how the legal frameworkAhmed, Rozha K. should be set up in order to shape the private sectors role and, in particular, how the responsibility between the statePappel, Ingrid and private enterprises should be divided. In service of this, we conducted six interviews with high-level stakeholders and experts from state agencies as well as enterprises and triangulated the insights with the relevant regulations and secondary literature. The analysis shows that, currently, public procurement laws play a pivotal role – the private sectors role is crucially determined in public development plans and whenDraheim, Dirk it comes to public procurement, the public sector critically relies on the know-how that is connected to delivered products and services.

Epidemic surveillance requires a rapid collection and integration of data and events related to the disease. Adequate measures, including education and awareness, must be rapidly taken to reduce the disastrous consequences of the disease. However, developing countries, especially those in West Africa, face a lack of real-time data collection and analysis system. This situation delays the analysis of risk and decision making. The aim of this research is to contribute to the surveillance of the meningitis epidemic based on Twitter datasets. The approach, we adopted in this research is divided into two parts. The first part consisted of investigating different methods to convert the tweet data into numerical data that will be used in machine-learning algorithms for the classification tasks. The second step is to evaluate these approaches using different algorithms and compare their performance in term of training time, accuracy, F1-score, and recall. As a result, we found that the SVM machine algorithm performed good with 0.98 of accuracy using the TF-IDF embedding approach while the ANN algorithm performed good with accuracy of 0.95 using the skip-gram embedding model.

With the popularity of mobile cloud computing and edge computing, the deployment of cloudlet technology in these modern computing paradigms has been affected by security issues. A cloudlet helps mobile devices in accessing the cloud by enhancing the processing time, lowering latency, and enabling better service time but it does not provide enough security assurance to its consumers. However, several studies have been conducted in this field and most of the proposed models mainly focus on either data transmission or data storage between the communication of mobile devices and cloudlets. In this study, we proposed a security model which addresses both transmission and storage of data, by protecting the information that is shared between the mobile devices and the cloudlet resources which is invariably linked to the cloud. The study considered two scenarios for the security model: Three-tier architecture and a Three-tier enhanced with Edge Orchestrator (EO) architecture. The security model used an encryption algorithm called hybrid cryptosystem to provide an efficient security solution which secures transmission and storage of data by addressing three security parameters: confidentiality, data integrity, and non-repudiation. EdgeCloudSim was used to implement and evaluate the proposed security model. The results obtained showed that the proposed security model between mobile devices and cloudlets communication performs better in Three-tier enhanced with EO architecture when considering security and performance metrics such as processing time, service time, and network delay.

A large amount of user-generated content on social media has led to the pursuitAly, Elton Shah of quickly and accurately mining through data and gathering useful insights. Text sentiment analysis has become a necessary tool in classifying user opinions within Web generated content. Due to the various ways, opinions can be conveyed, performing text sentiment analysis in specific domains becomes a difficult task. With an even greater degree of difficultyVan der Haar, Dustin Terence added when slang or colloquialisms are used. There is a great deal of research into investigating various classifiers in a traditional natural language processing setting each with their own merits and demerits. In this paper, we present a slang-based dictionary classifier with the objective of determining the sentiment of Instagram comments within the context of fashion, or more specifically sports shoes, and compare it with the performance of other classifiers such as a Naive Bayes, J48, lexicon and random forest. The dataset used for the benchmark was created from popular fashion Instagram accounts. Overall, the random forest classifier yields the best results with an accuracy of 88%, precision of 84% and a recall of 88%.

This paper presents the connection between exam or test anxiety and the illicit use of Ritalin (methylphenidate). Ritalin is used by students as a cognitive enhancer and thus to increase their learning and memory abilities. However, a side effect of Ritalin has altered fear reactions to stimuli. Thus, this paper aims to gain insight in the mechanisms of Ritalin on the anxiety system. The created network visualizes the overlapping systems of Ritalin and anxiety and possible outcomes of the use of Ritalin around exam weeks. First, the exam is noticed, with the induced (normal) stress response on having this exam. The wish to increase learning abilities during exam weeks surfaces, and Ritalin is administered as a result of cognitive decision making. Finally, the model shows how Ritalin can decrease test anxiety.

This paper proposes algorithm processing calculation of ASPECT ratio automatically for diagnosis of stroke. In this paper, we propose a CT image-based automatic ASPECT ratio determination algorithm to diagnose stroke, one of the central nervous system diseases. ASPECT ratio to be judged is classified grades and increases according to severity of the disease. The proposed method compares the correlation information of the left and right regions based on the detected gray matter regions. We propose the method to judge difference of ASPECT score according to difference of correlation information to be compared. This paper proposes an effective method featuring accuracy and simplicity.

Our cities are significantly changing their structure and organization. These changes have given rise to the need to introduce new services able to rationalize the activities present in such a complex context. Within this scenario, one of the most important services is related to transport management. A proper transport system management can significantly improve the overall quality of life of citizens, in terms of improving air quality, reducing road traffic and ensuring the public transport schedule. In addition to the traditional services and urban traffic management approaches, a significant contribution may come from the adoption of all those IT systems, which are part of the so-called Internet of things. According to this paradigm, it is possible to design an added value and pervasive services in order to assist the users involved in the system. Although this approach could be considered interesting and promising, it is necessary to introduce methodologies able to manage data coming from several heterogeneous sensors in order to process and propose coherent information. In this paper, we propose the using of three graphic approaches able to process the information coming from various sources in order to manage urban transport systems. The three models of representation, on which to conduct inference processes are context dimension tree, ontology and Bayes network. These three approaches allow the creation of inference processes, which represent the basis of value-added services to be offered to several users. The aim of this paper is to present a service that through a multilevel approach, which takes advantage of three models of graphic representation, is able to analyse data from various sensors in an urban area in order to predict the bicycle-sharing public service usage in the city of London. Through the intersection and analysis of data from cameras, weather and transport sensors, it will be possible to establish in which condition there will be an increase or decrease of bicycle rental in order to manage the service. The results obtained on data collected in real scenarios are very satisfying.

Through this paper, we aim at investigating the impact of artificial intelligence technologies on cultural heritage promotion and long-term preservation in terms of digitization effectiveness, attractiveness of the assets, and value empowering. Digital tools have been validated to yield sustainable and yet effective preservation for multiple types of content. For cultural data, however, there are multiple challenges in order to achieve sustainable preservation using these digital tools due to the specificities and the high-quality requirements imposed by cultural institutions. With the rise of machine learning and data science technologies, many researchers and heritage organizations are nowadays searching for techniques and methods to value and increase the reliability of cultural heritage digitization through machine learning. The present study investigates some of these initiatives highlighting their added value and potential future improvements. We mostly cover the aspects related to our context which is the long-term cost-effective digital preservation of the Qatari cultural heritage through the CEPROQHA project.

This paper examinesPapageorgiou, George the field of business models for successfully developing a smartphone application in order to promote walkability in urban environments. Carrying out a review of the literature on the theory of businessBalamou, Eudokia models, it was revealed that successful business development today should be accompanied by a holistic view of the organization and its relationships with multipleMaimaris, Athanasios stakeholders at all sides: supply, demand and partners sides. As a result, we propose a suitable framework for creating an effective business model—system architecture for a Smart Pedestrian Network (SPN) application. Due to the complexity and dynamic nature of the SPN domain, it is necessary to adopt an open innovation (OI) approach, where end users–citizens, businesses, application programming interface (API) gateways, system integrators (SI), external service providers, external service developers, well as municipalities–city authorities work together in a co-creation atmosphere.

Climate change is impacting milk production worldwide. For instance, increased heat stress in cows is causing average-sized dairy farms losing thousands of milk gallons each year; drastic climate change, especially in developing countries, pushing small farmers, farmers with less than 10–25 cattle, below the poverty line and is triggering suicides due to economic stress and social stigma. It is profoundly clear that current dairy agriculture practices are falling short to counter the impacts of climate change. What we need are innovative and intelligent dairy farming techniques that employ best of traditional practices with data-infused insights to counter negative effects of climate change. We strongly believe that “climate” is a data problem and the democratization of artificial intelligence-based dairy IoT devices to farmers is not only empowers farmers to understand the patterns and signatures of climate change but also provides the ability to forecast the impending climate change adverse events and recommends data-driven insights to counter the negative effects of climate change. With the availability of new data tools, farmers can not only improve their standard of life but, importantly, conquer perennial “climate change-related suicide” issue. It’s our staunch believe that the gold standard for the success of the democratization of artificial intelligence is no farmer life loss due to negative effects of climate change. In this paper, we propose an innovative machine learning edge approach that considers the impact of climate change and develops artificial intelligent (AI) models that is validated globally but enables localized solution to thwart impacts of climate change. The paper presents prototyping dairy IoT sensor solution design as well as its application and certain experimental results.

Internet of Medical Things (IoMT) is a fast-emerging technology in healthcare with a lot of scope for security vulnerabilities. Like any other Internet-connected device, IoMT is not immune to breaches. These breaches can not only affect the functionality of the device but also impact the security and privacy (S&P) of the data. The impact of these breaches can be life-threatening. The proposed methodology used a stakeholder-centric approach to improve the security of IoMT wearables. The proposed methodology relies on a set of S&P attributes for IoMT wearables that are identified to quantify S&P in these devices. This work aimed to (1) Guide hesitant users when choosing a secure IoMT wearable device, (2) Encourage healthier competition among manufacturers of IoMT wearables, and therefore, (3) Improve the S&P of IoMT wearables.

Online Labs (OLabs) is a major Digital India initiative with over 135 online experiments mapped to high school curriculum. For each experiment, OLabs provides background on the theory, animations, simulations, videos, viva voce questions, and links to additional resources. OLabs has been translated to multiple Indian languages. As part of scaling OLabs to the nation, over 16,000 teachers in all Indian states have been trained across India. The current manuscript presents a survey of 112 teachers who attended OLabs workshops and uses OLabs in the classroom. The study’s purpose is to understand the effective implementation of teacher training, to understand how OLabs is used in school laboratory experiments, and to understand how OLabs can supplement or replace real laboratories. A majority of teachers agree that repetition of OLabs experiments helps improve understanding of the concepts. There exists a strong correlation between the teachers’ perception of the quality of videos and animations and the teachers’ attitude on the usefulness of virtual laboratory software. Whether or not teachers feel that virtual laboratory software is useful to students is strongly associated with whether or not teachers feel that software is sufficiently fast and responsive. With regard to the workshops, teachers place high emphasis on the importance of establishing a clear agenda during the workshops. Finally, almost all teachers agree that OLabs can be an effective supplement to real laboratories.

Despite the high prevalence of reading disabilities among Indian children, many school teachers are not adept at identifying and assessing these difficulties. Screening tools for reading disabilities are available in English but are unavailable in many Indian languages. Reading disabilities manifest differently depending on the characteristics of the language being studied. This paper compares reading difficulties that arise when studying English and Malayalam. In a previous study, we designed a bilingual screening test in English and Malayalam and tested it with 135 school children in Kerala. In the current study, the screening test was modified in light of the findings from our previous study. We administered our updated bilingual screening test to 25 second grade children, ages 7–8, who were studying at two other schools in Kerala. Student errors were classified into multiple categories. Similarities and differences between errors in English and Malayalam were identified, and the errors that were specific to Malayalam were analyzed in further detail.

It is turned out to be obvious in current trend that the curriculums are not adequately covering up the skills that a fresh graduate should possess to become employable. In order to become “corporate-ready,” the industry expectations have to be incorporated into the curriculum. Curriculum enrichment supports cognitive development of the student fraternity through well-planned lessons spread throughout the period of education. This study focuses on bridging the gap of academic-industrial interface with necessary skill sets between the seekers and the employees and molding individuals as “corporate-ready.” Once the Board of Study (BOS) identifies the existing gap, they will be able to incorporate the skills sets that are required to make the fresh graduates “corporate-ready.” The results were very optimistic showing 85% of the study group members welcoming the prerequisite in comparison with only 15% for the control group members opting for other dimensions of skill sets.

It was analyzed the information of databases, encryption algorithms, and information security that are the main components of a Smart City. The problem is the lack of security of public or secret data that is stored in a repository, where there is access to the citizen, local government, and central government. The objective is to define a security prototype and adopt a cryptographic algorithm to mitigate the risks of the database in the management of a Smart City. It was used the deductive method and exploratory research to analyze the information of the reference articles. It turned out a general prototype of security; an algorithm to implement a database for Smart City; an algorithm of data protection expressed in flowcharts. It was concluded that information protected with an encryption algorithm, is a support to be more efficient, reduce costs, reduce the environmental footprint and improve the management of a Smart City.

The analysis of the appropriate bandwidths was made based on the electoral processes in countries with availability of electronic voting and these have been adapted to the situation in the electoral processes of Ecuador. The problem is the low importance given and the recidivism in the scarce bandwidth used in the current electoral processes. The objective reflects the problems caused by a mediocre bandwidth for events of such magnitude and importance in any country and creates awareness of the appropriate conditions to guarantee all aspects of the electronic process. The method used is deductive to analyze the data that were used as parameters to calculate the bandwidth in the electoral process in Nigeria. The analysis shows that the voting processes up to the 2017 period have not been optimal, but they are sustainable and acceptable despite the fact that there were setbacks when issuing the results of the votes. It is concluded that there must be simulations to avoid failures at the time of the actual electoral process and improve resources for the implementation of electoral processes.

Data cleansing is a critical step for data preparation. The values lost in the database are a common problem faced by data analysts. Missing values in data mining are continual troubles that can grounds errors in data analysis. Randomly missing elements in the attribute/dataset make data analysis complicated and also confused to consolidated result. It affects the accuracy of the result and intermediate queries. By using statistical/numerical methods, one can recover the missing data and decrease the suspiciousness in the database. The present paper gives an applied approach of Newton forward interpolation (NFI) method to recover the missing values.

In the present era, the world becomes global village, and all the business-related activities are now entered in the open market. Small businesses are also managing their product and services selling throughout the world with the help of online marketing. Such kind of product promotion activities with the help of internet and search engine is now subject of the digital marketing. At present, there are numerous methods and promoting schemes available with text, image, audio, and video promoting platform. These all activities are empowered by the information communication technology (ICT) and recent advance software development advancements. The present study provides a small insight into online product promoting scenario and its statistics. For that purpose, we are using Google Adwords search engine marketing journey analysis and its effect on Indian market. The whole process gives a view of search engine marketing and its growth.

Multispectral face recognition has been an interesting area of research where images obtained from different bands are matched. There are many face image datasets available which contain infrared and visible images. In most face recognition applications, the IR image taken in different circumstances is matched against the visible image available in the application database. High computational cost is required for processing these images. In the literature, there is no guideline about the optimal number of features for dealing with multispectral face datasets. Thus, in this paper, we will perform image matching using infrared and visible images for face recognition and establish the threshold of the optimal number of features required for multispectral face recognition. The experiments conducted are on SCFace—surveillance cameras face database. The experimental setup for multispectral face recognition using LBP and PCA feature sets and the experimental results are discussed in the paper.

Universities and colleges in the UK welcome about 30,000 students with special needs each year. Research shows that the dropout rate for disabled students is much higher at 31.5% when compared with about 12.3% for non-disabled students in the EU. Supporting young students with special educational needs while pursuing higher education is an ambitious and important role, which needs to be adopted by tertiary education providers worldwide. We propose, MALSEND, a conceptual platform based on human-machine intelligence (HMI), a collective intelligence of human and machine to understand patterns of learning of disabled students in higher education. This platform aims to accommodate and analyse data sets features of universities activities to discover trends in performances with regards to subject areas for autistic students, dyslexic students and students having attention deficit hyperactive disorder (ADHD), among others. Analysis of variables, such as students’ performances in modules, courses and other engagement-indices will give new insights into research questions, career advice and institutional policymaking. This paper describes the developmental activities of the MALSEND concept in phases.

Nowadays, urban and vehicularHammoudi, Karim surveillance systems are collecting large amounts of image data for feeding recognition systems, for example, toward proposing localization or navigation services. In many cases, these imageAbu Taha, Mohammed data cannot directly be processed in situ by the acquisition systems in reason of their low computational capabilities. The acquired images are transferred to remote computing servers throughBenhabiles, Halim various computer networks, and then analyzed in details toward object recognition. The objective of this paper is twofold (i) presenting image-based ciphering methods that can efficiently beMelkemi, Mahmoud applied for securing the image transfer against consequences of image interceptions (e.g., man-in-the-middle attacks) (ii) presenting generic image-based analysis techniques that can be exploitedWindal, Feryal for object recognition. Experimental results show end-to-end image-basedEl Assad, Safwan solutions for fostering developments of surveillance systems and services in urbanQueudet, Audrey and vehicular environments.

Mediated communication has shrunk the world into “customized cottages”. At the click of a button, we laugh, work, and even think together. Such digital interaction allows a fantastic mix of media usage. The best appears to be the combination of folk and mass media, where we have the intimacy and familiarity of the former and the outreach of the latter. Yet, challenges like illiteracy and problems of denial and deprivation of marginalized existence rampant in developing contexts necessitate the adoption of the futuristic “immersive technology” to communicate critical social messages.