Oriental Journal of Computer Science and Technology An International research journal of Computer Science and Technology

A vital step in automation of plant root disease diagnosis is to extract root region from the input images in an automatic and consistent manner. However, performance of segmentation algorithm over root images directly depends on the quality of input images.  During acquisition, the captured root images are distorted by numerous external factors like lighting conditions, dust and so on.  Hence it is essential to incorporate an image enhancement algorithm as a pre-processing step in the plant root disease diagnosis module. Image quality can be improved either by manipulating the pixels through spatial or frequency domain. In spatial domain, images are directly manipulated using their pixel values and alternatively in frequency domain, images are indirectly manipulated using transformations. Spatial based enhancement methods are considered as favourable approach for real time root images as it is simple and easy to understand with low computational complexity. In this study, real time banana root images were enhanced by attempting with different spatial based image enhancement techniques. Different classical point processing methods (contrast stretching, logarithmic transformation, power law transformation, histogram equalization, adaptive histogram equalization and histogram matching) and fuzzy based enhancement methods using fuzzy intensification operator and fuzzy if-then rule based methods were tried to enhance the banana root images. Quality of the enhanced root images obtained through different classical point processing and fuzzy based methods were measured using no-reference image quality metrics, entropy and blind image quality index. Hence, this study concludes that fuzzy based method could be deployed as a suitable image enhancement algorithm while devising the image processing modules for banana root disease diagnosis.

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Cloud powered plant image warehouse serves as an instrumental solution for various scientific and academic personnel involved in research, education and extension at NARES (National Agricultural Research and Education System), through providing a common image dataset that complements for efficient and more productive activities by saving them time, space, hassle and financial resources. This web based image repository enables the entire scientific community to access the freely available resources contributed by the fellow researchers who worked on common areas of interest, besides facilitating to acknowledge the one who originally contributed. This also enables them to have better control and use of meta data with tagging and custom theme usage.

The Plant image warehouse has been developed by using XAMPP an open source platform, which works on the Cent OS, using Apache Web server and MySQL a relational web based data management system and PHP, the object oriented scripting language. The third party software used in developing this image warehousing database is ZenPHOTO, a configurable software system wherein the users are able to upload, search and share the images. The graphical user interface is restricted to static webpages where, upon request from the user, server sends the response unchanged, unless modified by the uploader. This potential plant image warehousing technique will outstand as an authentic and reliable source of plant image database to the entire working community at NARES (National Agricultural Research and Education System).

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Deep learning is a very dynamic area in Sentiment Classification. Text analytics is the process of understanding text and making actionable decisions and acting on it. be it Amazon Alexa, Siri, Cortana everything is made up of Natural Language Processing. Text to speech and Speech to text are generating so many data sets every day. The internet has the largest repository of data, it is hard to define what to exactly do with it. sentiment are the opinions or the way of feelings of the public usually in the sequential form, in which many people face difficulty in living their daily life. Some are even ending their life just they are depressed. The approach here is to help the people suffering from depression with appropriate methodology to use in this work. Depressika: Early Risk of Depression Detection with opinions is a web application which detects the early risk of depression from the social media posts created by the users with appropriate Recurrent Neural Networks [RNN]. This is a classification problem of the Machine Learning [ML]. Depressika builds on Waterfall Methodology of application development using the Keras, Tensor  Flow, Scikit-Learn and Matplotlib to carryout and process sequential data and the overall process of development is carried out by Python programming Language.

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The sustainability and environmental considerations have slowly become divergences, but having greatest influence in the supply chain management that must be contemplates to examine the environmental and organizational factors. The research considers environmental and sustainable strategies within companies, the efficient supply chain management strategies for manufacturers and consumers, and to the environment friendly product design and services, taking a case-by-case perspective and concentrating on enterprise businesses scale. Our finding reveals that green supply chain management firms are delivering exuberant environmental efficiency at an added cost. Among the identified obstacles we identified different obstacles and conceptual relations and barriers are graded based on dependency and driving sand. In future, green policies have greater customer services avenues thereby, appeal for suppliers, manufacturers and officials.

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Cybersecurity systems are required at the application, network, host, and data levels. The research is purposed to evaluate Artificial Intelligence paradigms for use in network detection and prevention systems. This is purposed to develop a Cybersecurity system that uses artificial intelligence paradigms and can handle a high degree of complexity. The Pragmatism paradigm is elaborately associated with the Mixed Method Research (MMR), and is the research philosophy used in this research. Pragmatism recognizes the full rationale of the congruence between knowledge and action. The Pragmatic paradigm advocates a relational epistemology, a non-singular reality ontology, a mixed methods methodology, and a value-laden axiology. A qualitative approach where Focus Group discussions were held was used. The Artificial Intelligence paradigms evaluated include machine learning methods, autonomous robotic vehicle, artificial neural networks, and fuzzy logic. A discussion was held on the performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms.

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An Emotion monitoring system for a call-center is proposed. It aims to simplify the tracking and management of emotions extracted from call center Employee-Customer conversations. The system is composed of four modules: Emotion Detection, Emotion Analysis and Report Generation, Database Manager, and User Interface. The Emotion Detection module uses Tone Analyzer to extract them for reliable emotion; it also performs the Utterance Analysis for detecting emotion. The 14 emotions detected by the tone analyzer are happy, joy, anger, sad and neutral, etc. The Emotion Analysis module performs classification into the 3 categories: Neutral, Anger and Joy. By using this category, it applies the point-scoring technique for calculating the Employee Score. This module also polishes the output of the Emotion Detection module to provide a more presentable output of a sequence of emotions of the Employee and the Customer. The Database Manager is responsible for the management of the database wherein it handles the creation, and update of data. The Interface module serves as the view and user interface for the whole system. The system is comprised of an Android application for conversation and a web application to view reports. The Android application was developed using Android Studio to maintain the modularity and flexibility of the system. The local server monitors the conversation, it displays the detected emotions of both the Customer and the Employee. On the other hand, the web application was constructed using the Django Framework to maintain its modularity and abstraction by using a model. It provides reports and analysis of the emotions expressed by the customer during conversations. Using the Model View Template (MVT) approach, the Emotion monitoring system is scalable, reusable and modular.

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The security challenge on IoT (Internet of Things) is one of the hottest and most pertinent topics at the moment especially the several security challenges. The Botnet is one of the security challenges that most impact for several purposes. The network of private computers infected by malicious software and controlled as a group without the knowledge of owners and each of them running one or more bots is called Botnets. Normally, it is used for sending spam, stealing data, and performing DDoS attacks. One of the techniques that been used for detecting the Botnet is the Supervised Learning method. This study will examine several Supervised Learning methods such as; Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, k- Nearest Neighbors, Random Forest, Gradient Boosting Machines, and Support Vector Machine for identifying the Botnet in IoT with the aim of finding which Supervised Learning technique can achieve the highest accuracy and fastest detection as well as with minimizing the dependent variable.

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ERP, or Enterprise Resource Planning systems help business management, which consists of a well-designed interface that incorporates different programs to integrate and manage all company functions at intervals of a company, these sets incorporate applications for human resources, monetary and accounting, sales and distribution, project management, materials management, SCM, or Supply Chain Management and quality management. Currently, organizations are running to improve their ability to survive in the global market competitions of the 21st century. While the organizations try to advance in their level of agility, changing and modifying the process of decision-making to make it more efficient and effective to satisfy the successive variations of the market. Different views are gathered regarding ERP implementation of ERP in manufacturing. Even we have taken certain essential components of ERP for a better understanding of ERP. Ease of use, usefulness, quality, and trust on ERP services have been taken an independent variable that affects user’s decision to adopt ERP. The role of ERP technology in manufacturing facilities are broken into more categories for detail concept. Quantitative data analysis methods were usually used for questionnaire data analysis which was utilized to analyze statistical data and after that collection of interview data was done. A researcher has applied different statistical tools like Chi-Square Tests, Anova, etc. to analyze the collected data. A researcher essential portion is to analyze and interpret data that relates to modifying data which explains the solution to the research question with some additional future recommendation for more quality research.

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The purpose of this research was to develop a structure for a network intrusion detection and prevention system based on the Bayesian Network for use in Cybersecurity. The phenomenal growth in the use of internet-based technologies has resulted in complexities in cybersecurity subjecting organizations to cyberattacks. What is required is a network intrusion detection and prevention system based on the Bayesian Network structure for use in Cybersecurity. Bayesian Networks (BNs) are defined as graphical probabilistic models for multivariate analysis and are directed acyclic graphs that have an associated probability distribution function. The research determined the cybersecurity framework appropriate for a developing nation; evaluated network detection and prevention systems that use Artificial Intelligence paradigms such as finite automata, neural networks, genetic algorithms, fuzzy logic, support-vector machines or diverse data-mining-based approaches; analysed Bayesian Networks that can be represented as graphical models and are directional to represent cause-effect relationships; and developed a Bayesian Network model that can handle complexity in cybersecurity. The theoretical framework on Bayesian Networks was largely informed by the NIST Cybersecurity Framework, General deterrence theory, Game theory, Complexity theory and data mining techniques. The Pragmatism paradigm used in this research, as a philosophy is intricately related to the Mixed Method Research (MMR). A mixed method approach was used in this research, which is largely quantitative with the research design being a survey and an experiment, but supported by qualitative approaches where Focus Group discussions were held. The performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms was discussed. Alternative improved solutions discussed include the use of machine learning algorithms specifically Artificial Neural Networks (ANN), Decision Tree C4.5, Random Forests and Support Vector Machines (SVM).

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Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach.

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Digital images are a momentous part of today’s digital communication. It is very easy to manipulate digital images for hiding some useful information by image rendering tools such as Adobe Photoshop, Microsoft Paint etc. The common image forgery which is easy to carry out is copy-move in which some part of an image is copied and pasted on another part of the same image to hide the important information. In this paper we propose an algorithm to spot the copy-move forgery based on exact match block based technique. The algorithm works by matching the regions in image that are equivalent by matching the small blocks of size b b. The program is tested for 45 images of mixed image file formats by considering block sizes 2, 4, 6, 8, 10, 12, 14, and 16.  It is observed from the experimental results that the proposed algorithm can detect copy-move image forgery in TIF, BMP and PNG image formats only. Results reveal that as the block size increases, execution time (time taken by CPU to display output) also increases but the number of detected forged images increases till block size 10 and attains saturation thereafter. Consequently block size should be set to 10 for getting good results in terms of less execution time.

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The researcher have suggested real time depth based on frequency domain hole filling. It get better quality of depth sequence generated by sensor. This method is capable to produce high feature depth video which can be quite useful in improving the performance of various applications of Microsoft Kinect such as obstacle detection and avoidance, facial tracking, gesture recognition, pose estimation and skeletal. For stereo matching approach images depth extraction is the hybrid (Combination of Morphological Operation)  mathematical algorithm. There are few step like color conversion, block matching, guided filtering, minimum disparity assignment design, mathematical perimeter, zero depth assignment, combination of hole filling and permutation of morphological operator and last nonlinear spatial filtering. Our algorithm is produce smooth, reliable, noise less and efficient depth map. The evaluation parameter such as Structure Similarity Index Map (SSIM), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) measure the results for proportional analysis.

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Body modification (or body alteration) is the wilful altering of the human body by an individual in a way that lasts forever or for a very long time. This is usually for non-medical reasons that include sexual enhancement, a rite of passage, aesthetic reasons, denoting affiliation, trust and loyalty, religious reasons, shock value, and self-expression. It can range from the socially acceptable decoration (e.g., pierced ears or nose in many societies) to the religiously mandated. Body art is the modification of any part of the human body for artistic or aesthetic reasons.  Nanotechnology is currently available to implant biometric devices in human beings, which can be monitored by software, satellites and utilized by Government and Industry. In fact several developers are currently bringing these technologies to the public and private sector at affordable prices.  The context of “Technology Consumerism” compounded by Intentionality and Free-Will of its consumer’s results in many unintended consequences outlined in this paper. Geometry of Morphogenesis is the proposed theory for decoding body modification.

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E-learning system is designed to aid students build their comprehension towards their respected academic subjects to make their experience in learning more nourishing and engaging. The researchers decided to conduct this study to be au fait the benefits of online education and how the students accept the change this innovation gave. A survey questionnaire was distributed to the enrolled college students of AMA Computer College Year 2018-2019 to make the research more reliable, accurate and at the same time tackle the benefits of the said innovated education system. The said research was a success in defining what helps the students in their education as well as the improvement it can do in terms of catering different subjects and assessing the learners’ competency. The researchers recommend the future researchers to further expand and elaborate the topic in order to enhance the study. To further extend the scope and distribute further information regarding the tackled study.

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In 1943 Machculloch and Pitts create the formal neuron where many input signals are linearly composed with different weights on the neuron soma. When the soma electrical signal goes over a specific threshold an output is produced. The main topic in this model is that the response is the same response as in a Boolean function used a lot for the digital computer. Logic functions can be simplified with the formal neuron. But there is the big problem for which not all logic functions, as XOR , cannot be designed in the formal neuron. After a long time the back propagation and many other neural models overcame the big problem in some cases but not in all cases creating a lot of uncertainty. The model proposed does not consider the formal neuron but the natural network controlled by a set of differential equations for neural channels that model the current and voltage on the neuron surface. The steady state of the probabilities is the activation state continuous function whose maximum and minimum are the values of the Boolean function associated with the activation time of spikes of the neuron. With this method the activation function can be designed when the Boolean functions are known. Moreover the neuron differential equation can be designed in order to realize the wanted Boolean function in the neuron itself. The activation function theory permits to compute the neural parameters in agreement with the intention.

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One of the biggest problems in using artificial neural networks is to determine the best architecture. This is a crucial problem since there are no general rules to select the best architecture structure. Selection of the best architecture is to determine how many neurons should be used in the layers of a network. It is a well-known fact that using a proper architecture structure directly affect the performance of the method. Therefore, various approaches ranging from trial and error method to heuristic optimization algorithms have been suggested to solve this problem in the literature. Although there have been systematical approaches in the literature, trial and error method has been widely used in various applications to find a good architecture. This study propose a new architecture selection method based on statistical and machine learning. The proposed method utilizes regression analysis that is a supervised learning technique in machine learning. In this new architecture selection approach, it is aimed to combine statistical and machine learning to reach good architectures which has high performance. The proposed approach brings a new perspective since it is possible to perform statistical hypothesis tests and to statistically evaluate the obtained results when artificial neural networks are used. The best architecture structure can be statistically determined in the proposed approach. In addition to this, the proposed approach provides some important advantages. This is the first study using a statistical method to utilize statistical hypothesis tests in artificial neural networks. Using regression analysis is easy to use so applying the proposed method is also easy. And, the proposed approach saves time since the best architecture is determined by regression analysis. Furthermore, it is possible to make inference for architectures which is not examined. The proposed approach is applied to three real data sets to show the applicability of the approach. The obtained results show that the proposed method gives very satisfactory results for real data sets.

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Systematic pursuits are being developed to set forth the framework for the Fifth Generation (5G) wireless standards. This paper emphases on the most extensively deployed technology - Orthogonal Frequency Division Multiplexing (OFDM) that has outpaced other waveform aspirants for Fourth Generation (4G) communication standards. Irrespective of the beneficial features, it does possess a number of significant limitations that mark it as an incompatible candidate for the upcoming 5G standard. This paper highlights on its major drawback i.e high Peak-to-Average Power Ratio (PAPR). Results state that PAPR does cause sudden upsurge to the output signal envelope causing further other damages. There exists a need for more flexible waveforms to replace the conventional OFDM in order to address the unprecedented challenges. The future research directions in the domain are presented.

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In food processing industry, there arises a need to control a conveyor belt. currently industries are very necessary to use material handling system for to move materials from one place to another place continuously and to minimize operations time. Stepper Motor is suitable for controlling conveyor because of its high accuracy positioning over a short distance and provide high torque even at low speeds and it is also offer very low vibration and a wide range of features. This paper is focused on controlling the speed of the conveyor belt through the speed of stepper motor using the micro processor namely, Raspberry Pi 3B+’s (RP 3B+) GPIOs (General Purpose Input Output) and it can be generate sequence of control signals on the GPIO pins of RP 3B+. Interfacing the stepper motor with RP 3B+ using python programming language. The method is explained with the results of changing the weights the speed level is reduced through time variation on the conveyor belt and the model of working Conveyor belt with Stepper motor controlled by python and RP 3B+ with Easy Driver(A3967).

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Cyber attacks have become quite common in this internet era. The cybercrimes are getting increased every year and the intensity of damage is also increasing. providing security against cyber-attacks becomes the most significant in this digital world. However, ensuring cyber security is an extremely intricate task as requires domain knowledge about the attacks and capability of analysing the possibility of threats. The main challenge of cybersecurity is the evolving nature of the attacks. This paper presents the significance of cyber security along with the various risks that are in the current digital era. The analysis made for cyber-attacks and their statistics shows the intensity of the attacks. Various cybersecurity threats are presented along with the machine learning algorithms that can be applied to cyber attacks detection. The need for the fifth generation cybersecurity architecture is discussed.

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Investing in information technology is a requirement for enterprises to sustain their competitive advantage in a market that is described as changing and global. IT is a very important resource for enterprises to improve their organizational performance, but requires some justification for its costs and burdens. This study utilized an existing model and applied it on a case in Jordan by analyzing and exploring the implications of investing in IT projects. The case used is the Japan Tobacco International, where a survey was used to collect response from JTI personnel and the documents available on their portal. Two models are proposed to improve our understanding of topic and set the stage for future research. The detailed results of this study are reported with conclusions at the end.

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Edge Computing (EC) is an emerging technology that has made it possible to process the large volume of data generated by devices connected to the Internet, through the Internet of objects (IO). The article provides an introduction to EC and its definition. The integration of EC in those contexts would imply an optimisation of the processes that are normally executed in a cloud computing environment, bringing considerable advantages. The main contribution of EC is a better pre-processing of the data collected through devices before they are sent to a central server or the cloud.

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The following work presents a methodology of determining the economic value of the data owned by a company in a given time period. The ability to determine the value of data at any point of its lifecycle, would make it possible to study the added value that data gives to a company in the long term. Not only external data should be considered but also the impact that the internal data can have on company revenues. The project focuses on data-driven companies, which are different to the data-oriented ones, as explained below. Since some studies affirm that data-driven companies are more profitable, the indirect costs of using those data must be allocated somewhere to understand their financial value¹⁴ and to present a possible alternative for measuring the financial impact of data on the revenue of companies.

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Remote sensors from the Satellite or Aircrafts are generated by huge volume of data which can utilize for impending signification if collected data aggregated effectively incorporates by insight information. Data is collection from simple to hybrid devices, which are continuously working for technology around us and communicate with each other. These devices are transferring huge amounts of real time data daily. The transaction added to the synchronized inaccessible sensing data that is retrieving the useful information in the proficient way of classification in the direction of the severe computational challenges, analyze, the assortment, and accumulate, where gathered data is inaccessible. The real time sensing devices will continuously export data. In this work, we will implement the big data analytics on remote sensing datasets. We utilized BEST software for header analysis of the datasets and retrieving the full resolution image from the dataset. Then retrieved image is divided into smaller blocks for applying statistical. By applying certain rules and conditions in the form of algorithm, determine the land and sea blocks of image dataset.  Our end results are proficiently analyzing real-time remote sensing utilizing the land beacon structure. Finally, a comprehensive investigation of the remotely intelligence earth beacon massive information for earth and ocean space are available by utilizing- Hadoop.

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Notice boards are playing a very important role in our day to day life. By replacing conventional Analog type notice board with digital notice board we can make information dissemination much easier in a paperless community. Here the admin can control notice board through the internet. So information can be sent anywhere in the world and can be displayed within seconds. Information may be in the form of text, image, pdf etc. PC is used for sending information and Raspberry Pi is connected to the internet at the receiving side. In addition to this, an application which is installed on the admin’s mobile phone can serve the same purpose. This application also contains a speech to text converter. So the admin can send text messages through his/her own voice.

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Health Monitoring system using IOT describes the collection and interoperation of Patient data collected from the sensors from the hospitals through IOT Technology. The collected sensor data will support the doctor in the emergency situation for the betterment and improvement of Patient health. The hardware platform to implement the project consists of a sensor and Raspberry Pi 3 Model B equipped in a way to communicate with a doctor through the Internet and Smart Phone. This proposed idea will help doctors to know about the state of patient health and monitor anywhere in the world. In this proposed idea the sensors gather the medical information of the patient that includes patient’s heart rate, blood pressure, and pulse rate Then using the camera the patient is livelily monitored through the Raspberry kit and this information is sent to the Internet and stored in a medical server. The doctor and patient can monitor the patient data from any place of the world through the provided IP server address anytime. The emergency alert is sent to the patient if the sensor value is exceeded by the threshold data. Thus the patient's health parameters are watched lively and regular monitoring through the medical server to a doctor will help to make an effective diagnosis and almost accurate care can be given. The data collected through the IOT will help the patient to recover easily and also enhanced medical care can be given to the patients at a low cost.

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A wireless sensor network (WSN) can be employed in many application areas such as traffic control and industrial automation. In WSNs, clustering achieves energy efficiency and scalable performance. A cluster is formed by several sensors nodes, and one of them is elected as cluster-head (CH). A CH collects information from the cluster members and sends aggregated sensed data to the base station (BS) or another CH. The main task of a routing protocol in a WSN is to forward these sensed data to the BS. This paper analyses the advantages of cluster-based routing protocols vs. flat routing protocols in WSNs.

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The biometric system plays the most important role in this current century. Finger print identification is one among the foremost distinguished and familiar identity verification system due to its individuality. Security within the hostel is one of the foremost repetitive issues. To keep up day by day attendance verification is sophisticated and time consuming system for the hostel management. There are number of existing attending systems are available for college students, for hostel students it must improve. Within the existing system wardens are manually maintain the attendance for hostel students. This paper deals with, avoid of an entire problem in hostel management system together with this monitoring system also proposed. The administrator of this system was college principal or warden. Biometric system is used to accommodate a large number of students within the hostel. This system makes automatically to monitor the entry and exit of students from hostel and offers alert SMS to parents for their safety.

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In this research a developed web–based balanced scorecard performance evaluation system proposed for the case study of Ethiopian organizations and supports both English and Amharic languages. The system incorporates evaluations of individual performance (both in activity and behavior), major activity performance, objective performance, perspective performance, unit/ department performance, and the organization/institute as a whole based on time, cost, quality and quantity. Object oriented software engineering and ASP.NET 4.0 platform is employed to develop the system. Reviewing the overall results from the usability test, questionnaires and interviews, it is concluded that all production and extensive public service providing organizations prefer to use the implemented application but other organizations comparatively didn’t want to use it as it seems it doesn’t facilitate their daily duties. But the overall functionality of the system is being agreed by the users.

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In the past couple of decades, the number of vehicles has increased radically. A statistic which presents the number of cars sold worldwide from 1990 through 2017, forecasts for 2018, some 81.6 million automobiles are expected to be sold by the end 2018. With this continuous increase, it is becoming very tedious to keep track of each vehicle for the purpose of security, law enforcement and traffic management. This phenomenon of rapidly increasing vehicles on the road highlights the importance for a vehicle number plate recognition system. By recognizing the car plates, the drivers of the vehicle can be identified from the database. Number plate detection system are used in various applications like traffic law maintenance, traffic control, automatic toll collection, parking systems, automatic gate openers. This paper presents a unique algorithmic procedure for detecting vehicle plate number which is based on the concept of mathematical morphology. The developed algorithm is simple, efficient and flexible. The algorithm is capable of working satisfactorily even in different constraints such as like rain, smoke and shadow. This user-friendly software tool is developed on MATLAB platform which is one of the common and efficient image processing analysis tools.

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Recently, Kumari et al., pointed out that Chang et al.’s scheme “Untraceable dynamic-identity-based remote user authentication scheme with verifiable password update” has several drawbacks and does not provide any session key agreement. Hence, they proposed an improved remote user authentication scheme with key agreement based on Chang et al.’s protocol. They claimed that the improved method is secure. However, we found that their improvement still has both anonymity breach and smart card loss password guessing attack which cannot be violated in the ten basic requirements advocated for a secure identity authentication using smart card by Liao et al. Thus, we modify their protocol to encompass these security functionalities which are needed in a user authentication system using smart card.

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