Recent Advances in Computer Science and Communications - Volume 13, Issue 5, 2020

Sustainability is defined as the practice of protecting natural resources for future use without harming the nature. Sustainable development includes the environmental, social, political, and economic issues faced by human being for existence. Water is the most vital resource for living being on this earth. The natural resources are being exploited with the increase in world population and shortfall of these resources may threaten humanity in the future. Water sustainability is a part of environmental sustainability. The water crisis is increasing gradually in many places of the world due to agricultural and industrial usage and rapid urbanization. Data mining tools and techniques provide a powerful methodology to understand water sustainability issues using rich environmental data and also helps in building models for possible optimization and reengineering. In this research work, a review on usage of supervised or unsupervised learning algorithms in water sustainability issues like water quality assessment, waste water collection system and water consumption is presented. Advanced technologies have also helped to resolve major water sustainability issues. Some major data mining optimization algorithms have been compared which are used in piped water distribution networks.

Introduction: Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) are leading linear and non-linear models in Machine learning respectively for time series forecasting. Objective: This survey paper presents a review of recent advances in the area of Machine Learning techniques and artificial intelligence used for forecasting different events. Methods: This paper presents an extensive survey of work done in the field of Machine Learning where hybrid models for are compared to the basic models for forecasting on the basis of error parameters like Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE). Results: Table 1 summarizes important papers discussed in this paper on the basis of some parameters which explain the efficiency of hybrid models or when the model is used in isolation. Conclusion: The hybrid model has realized accurate results as compared when the models were used in isolation yet some research papers argue that hybrids cannot always outperform individual models.

Background: In the process of volumetric evaluation of the damaged region in the human brain from a MR image it is very crucial to remove the non-brain tissue from the acquainted image. At times there is a chance during the process of assessing the damaged region through automated approaches might misinterpret the non-brain tissues like skull as damaged region due to their similar intensity features. So in order to address such issues all such artefacts. Objective: In order to mechanize an efficient approach that can effectively address the issue of removing the non-brain tissues with minimal computation effort and precise accuracy. It is very essential to keep the computational time to be as minimal as possible because the processes of skull removal is used in conjunction with segmentation algorithm, and if the skull scrapping approach has consumed a considerable amount of time, they it would impact the over segmentation and volume assessment time which is not advisable. Method: In this paper a completely novel approach named Structural Augmentation has been proposed, that could efficiently remove the skull region from the MR image. The proposed approach has several phases that include applying of Hybridized Contra harmonic and Otsu AWBF filtering for noise removal and threshold approximation through Otsu based approach and constructing the bit map based on the approximated threshold. Morphological close operation followed by morphological open operation with reference to a structural element through the generated bitmap image. Results: The experiment are carry forwarded on a real time MR images of the patient at KGH hospital, Visakhapatnam and the images from open sources repositories like fmri. The experiment is conducted on the images of varied noise variance that are tabulated in the results and implementation section of the article. The accuracy of the proposed method has been evaluated through metrics like Accuracy, Sensitivity, Specificity through true positive, true negative, False Positive and False negative evaluations. And it is observed that the performance of the proposed algorithm seems to be reasonable good. Conclusion: The skull scrapping through structural Augmentation is computationally efficient when compared with other conventional approaches concerning both computational complexity and the accuracy that could be observed on experimentation. The Adaptive Weighted Bilateral Filter that acquire the weight value from the approximated contra harmonic mean will assist in efficient removal of poison noised by preserving the edge information and Otsu algorithm is used to determine the appropriate threshold value for constructing the bitmap image of the original MRI image which is efficient over the earlier mean based approach for estimating the threshold. Moreover, the efficiency of the proposed approach could be further improved by using customized structural elements and incorporating the fuzzy based assignments among the pixels that belong to brain tissue and skull effectively.

Background: With technological advancements, the use of many kinds of computing devices has given birth to a new era of computing. RFID tags, smart cards, and wireless sensors are the low computing power devices, which are being used massively in sensitive applications. Therefore, securing these low computing environments has become a great concern. Proposed Work: In this paper, an elliptic curve signcryption based security framework for securing low computing power devices has been proposed which provides mutual authentication, confidentiality, non-repudiation, forward secrecy, integrity, availability, key privacy, and anonymity. In addition to this, the proposed security framework has the capability to resist replay attack, desynchronization attack, impersonation attack, key-compromise attack, location tracking attack, denial of service attack, and man-in-the-middle attack. Results: Results have revealed that the proposed framework is efficient in terms of computational time as compared to the other related schemes. Conclusion: The proposed protocol presented in this paper can be used as a building block in designing efficient security protocols for all kinds of low computing power devices including RFID, wireless sensors, and smart cards.

Background: The medical data, in the form of prescriptions and test reports, is very extensive which needs a comprehensive analysis. Objective: The gene expression data set is formulated using a very large number of genes associated to thousands of samples. Identifying the relevant biological information from these complex associations is a difficult task. Methods: For this purpose, a variety of classification algorithms are available which can be used to automatically detect the desired information. K-Nearest Neighbour Algorithm, Latent Dirichlet Allocation, Gaussian Naïve Bayes and support Vector Classifier are some of the well known algorithms used for the classification task. Nonnegative Matrix Factorization is a technique which has gained a lot of popularity because of its nonnegativity constraints. This technique can be used for better interpretability of data. Result: In this paper, we applied NMF as a pre-processing step for better results. We also evaluated the given classifiers on the basis of four criteria: accuracy, precision, specificity and Recall. Conclusion: The experimental results shows that these classifiers give better performance when NMF is applied at pre-processing of data before giving it to the said classifiers. Gaussian Naïve Bias algorithm showed a significant improvement in classification after the application of NMF at preprocessing.

Background: Automatic Machine Translation (AMT) Evaluation Metrics have become popular in the Machine Translation Community in recent times. This is because of the popularity of Machine Translation engines and Machine Translation as a field itself. Translator is a very important tool to break barriers between communities especially in countries like India, where people speak 22 different languages and their many variations. With the onset of Machine Translation engines, there is a need for a system that evaluates how well these are performing. This is where machine translation evaluation enters. Objective: This paper discusses the importance of Automatic Machine Translation Evaluation and compares various Machine Translation Evaluation metrics by performing Statistical Analysis on various metrics and human evaluations to find out which metric has the highest correlation with human scores. Methods: The correlation between the Automatic and Human Evaluation Scores and the correlation between the five Automatic evaluation scores are examined at the sentence level. Moreover, a hypothesis is set up and p-values are calculated to find out how significant these correlations are. Results: The results of the statistical analysis of the scores of various metrics and human scores are shown in the form of graphs to see the trend of the correlation between the scores of Automatic Machine Translation Evaluation metrics and human scores. Conclusion: Out of the five metrics considered for the study, METEOR shows the highest correlation with human scores as compared to the other metrics.

Background: A large number of communities and enterprises deploy numerous scientific workflow applications on cloud service. Aims: The main aim of the cloud service provider is to execute the workflows with a minimal budget and makespan. Most of the existing techniques for budget and makespan are employed for the traditional platform of computing and are not applicable to cloud computing platforms with unique resource management methods and pricing strategies based on service. Methods: In this paper, we studied the joint optimization of cost and makespan of scheduling workflows in IaaS clouds, and proposed a novel workflow scheduling scheme. Also, data placement is included in the proposed algorithm. Results: In this scheme, DPO-HEFT (Data Placement Oriented HEFT) algorithm is developed which closely integrates the data placement mechanism with the list scheduling heuristic HEFT. Extensive experiments using the real-world and synthetic workflow demonstrate the efficacy of our scheme. Conclusion: Our scheme can achieve significantly better cost and makespan trade-off fronts with remarkably higher hypervolume and can run up to hundreds times faster than the state-of-the-art algorithms.

Background: In recent time, people love online shopping but before any shopping feedbacks or reviews always required. These feedbacks help customers in decision making for buying any product or availing any service. In the country like India this trend of online shopping is increasing very rapidly because awareness and the use of internet which is increasing day by day. As result numbers of customers and their feedbacks are also increasing. It is creating a problem that how to read all reviews manually. So there should be some computerized mechanism that provides customers a summary without spending time in reading feedbacks. Besides big number of reviews another problem is that reviews are not structured. Objective: In this paper, we try to design, implement and compare two algorithms with manual approach for the crossed domain Product’s reviews. Methods: Lexicon based model is used and different types of reviews are tested and analyzed to check the performance of these algorithms. Results: Algorithm based on opinions and feature based opinions are designed, implemented, applied and compared with the manual results and it is found that algorithm # 2 is performing better than algorithm # 1 and near to manual results. Conclusion: Algorithm # 2 is found better on the different product’s reviews and still to be applied on other product’s reviews to enhance its scope. Finally, it will be helpful to automate existing manual process.

Background: The client-server operations involving the financial transactions are liable to be carried in secure and controlled environment which is provided by Secure Socket Layer protocol in order to vanquish possibility of threats and attacks. In this protocol, the handshake mechanism plays an imperative role, negotiating security policy between client and server. The consolidated security policy between the communicating parties depends upon the level of threat or an attack at an instance subject to change. Objective: Transformation of Secure Socket Layer protocol into the Adaptive model wherein the cryptographic algorithms are selected from the series at runtime depending upon the changing external factors. Further, the reoriented model can be used for web server load management as well. Method: Over-taking control of Renegotiation process by separating it from Web Service Configuration and perform renegotiations based on evaluated performance of cryptographic techniques. Results: Experiments to obtain performance of cryptographic algorithms were done using OpenSSL utility running in Ubuntu-64 bit on 8th generation, i3-8130U runs 2.20 GHz processor and 4 G.B RAM. We enunciated, Data Encryption Standard was slower but ideally secure symmetric, RSA- 512 outnumbers the verifications per second and Message Digest-4 is fastest Symmetric. Conclusions: In this paper, a legacy security system has been reshaped to adapt security at runtime. Further, the offline performance of cryptographic algorithms has been evaluated based on which third party makes decisions. Following this, a trade-off policy between security and performance is formulated such that the model can be optimized easily.

Background: Breast cancer is one of the diseases which cause number of deaths ever year across the globe, early detection and diagnosis of such type of disease is a challenging task in order to reduce the number of deaths. Now a days various techniques of machine learning and data mining are used for medical diagnosis which has proven there metal by which prediction can be done for the chronic diseases like cancer which can save the life’s of the patients suffering from such type of disease. The major concern of this study is to find the prediction accuracy of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest and to suggest the best algorithm. Objective: The objective of this study is to assess the prediction accuracy of the classification algorithms in terms of efficiency and effectiveness. Methods: This paper provides a detailed analysis of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest in terms of their prediction accuracy by applying 10 fold cross validation technique on the Wisconsin Diagnostic Breast Cancer dataset using WEKA open source tool. Results: The result of this study states that Support Vector Machine has achieved the highest prediction accuracy of 97.89 % with low error rate of 0.14%. Conclusion: This paper provides a clear view over the performance of the classification algorithms in terms of their predicting ability which provides a helping hand to the medical practitioners to diagnose the chronic disease like breast cancer effectively.

Background: TSP problem has been the part of literature from many decades; it’s an important optimization issue in operation research. TSP problem always remain greedy for the better results especially if chosen working field are Genetic Algorithms (GA). Objective: This paper presents a TSP solution, which performed the modified selection and crossover operations as well as takes advantage of Mendelian inheritance while producing the generations. Methods: GA has very broad resolution scope for optimization problems and it is capable enough for generating well-optimized results if right GA technique has been applied on right point of issue in controlled manner. here the proposed agenda is to utilize the GA concept for TSP by applying mendels rules which is never applied before for the same issue. Here the proposed scheme applies some modification in traditional Mendel process. In general, full chromosome window has been utilized in mendel inheritance process but in presented scheme we have utilizes Most Significant Bits (MSB) only which helps in to control the convergence aptitude of the process. Results: The scheme uses advanced modified Mendel operation which helps in to control convergence aptitude of the operation. It efficiently minimizes the total travelled distance of the graph which was the ultimate objective of the problem and that has been successfully achieved. Conclusion: The validation of the scheme has been confirmed from the obtained results, which are better enough as comparison to traditional TSP-GA.

Background: E-commerce/ M-commerce has emerged as a new way of doing businesses in the present world which requires an understanding of the customer’s needs with the utmost precision and appropriateness. With the advent of technology, mobile devices have become vital tools in today’s world. In fact, smart phones have changed the way of communication. The user can access any information on a single click. Text messages have become the basic channel of communication for interaction. The use of informal text messages by the customers has created a challenge for the business segments in terms of creating a gap pertaining to the actual requirement of the customers due to the inappropriate representation of it's need by using short message service in an informal manner. Objective: The informally written text messages have become a center of attraction for researchers to analyze and normalize such textual data. In this paper, the SMS data have been analyzed for information retrieval using Soundex Phonetic algorithm and its variations. Methods: Two datasets have been considered, SMS- based FAQ of FIRE 2012 and self-generated survey dataset have been tested for evaluating the performance of the proposed Soundex Phonetic algorithm. Results: It has been observed that by applying Soundex with Inverse Edit Term Frequency, the lexical similarity between the SMS word and Natural language text has been significantly improved. The results have been shown to prove the work. Conclusion: Soundex with Inverse Edit Term Frequency Distribution algorithm is best suited among the various variations of Soundex. This algorithm normalizes the informally written text and gets the exact match from the bag of words.

Background: In creating nations like India, there are in excess of 10 administrators giving versatile administration in each circle. With the presentation of number convenience portable client are progressively changing starting with one administrator then onto the next. This conduct is called beat. The explanation behind beat might be many like valuing isn't alluring, visit call drops, message drops, more client care calls and so forth. Presently the administrator in INDIA is aware of the need of client. At that point, it is past the point of no return as the client has officially settled on choice and hard to persuade and retain. So a robotized instrument is needed at administrator end to predict which client may beat with high exactness. Objective: With fast utilization of outfit classifiers to enhance exactness, we additionally propose a gathering cross breed classifier that predicts with more precision. Methods: Hybrid model contains regression, perceptron and confrontation both regression and perceptron run parallel after completion execution both the results will be compared in a confrontation level. Conclusion: The report of customer who are predicted to churn and the reason for churning if reported. Also it will store aggregate reporting HBASE database.

Background: Image retrieval has a significant role in present and upcoming usage for different image processing applications where images within a desired range of similarity are retrieved for a query image. Representation of image feature, accuracy of feature selection, optimal storage size of feature vector and efficient methods for obtaining features plays a vital role in Image retrieval, where features are represented based on the content of an image such as color, texture or shape. In this work an optimal feature vector based on control points of a Bezier curve is proposed which is computation and storage efficient. Aim: To develop an effective and storage, computation efficient framework model for retrieval and classification of plant leaves. Objectives: The primary objective of this work is developing a new algorithm for control point extraction based on the global monitoring of edge region. This observation will bring a minimization in false feature extraction. Further, computing a sub clustering feature value in finer and details component to enhance the classification performance. Finally, developing a new search mechanism using inter and intra mapping of feature value in selecting optimal feature values in the estimation process. Methods: The work starts with the pre-processing stage that outputs the boundary coordinates of shape present in the input image. Gray scale input image is first converted into binary image using binarization then, the curvature coding is applied to extract the boundary of the leaf image. Gaussian Smoothening is then applied to the extracted boundary to remove the noise and false feature reduction. Further interpolation method is used to extract the control points of the boundary. From the extracted control points the Bezier curve points are estimated and then Fast Fourier Transform (FFT) is applied on the curve points to get the feature vector. Finally, the K-NN classifier is used to classify and retrieve the leaf images. Results: The performance of proposed approach is compared with the existing state-of-the-artmethods (Contour and Curve based) using the evaluation parameters viz. accuracy, sensitivity, specificity, recall rate, and processing time. Proposed method has high accuracy with acceptable specificity and sensitivity. Other methods fall short in comparison to proposed method. In case of sensitivity and specificity Contour method out performs proposed method. But in case accuracy and specificity proposed method outperforms the state-of-the-art methods. Conclusion: This work proposed a linear coding of Bezier curve control point computation for image retrieval. This approach minimizes the processing overhead and search delay by reducing feature vectors using a threshold-based selection approach. The proposed approach has an advantage of distortion suppression and dominant feature extraction simultaneously, minimizing the effort of additional filtration process. The accuracy of retrieval for the developed approach is observed to be improved as compared to the tangential Bezier curve method and conventional edge and contour-based coding. The approach signifies an advantage in low resource overhead in computing shape feature.

Background: Nodes in the mobile Ad-hoc network (MANET) have the dynamic nature due to their vast separation distance. The dynamic nature of the MANET nodes changes the routing path established for the communication. The dynamic secured routing (DSR) has gained popularity since they provide the routing path based on the demand provided by the source and destination nodes. Various literature works have discussed the DSR strategy for routing path establishment, but faces challenges since it consumes high energy during the route discovery phase. Objectives: To overcome the challenges in the existing works, a DSR strategy based on the Fractional Whale optimization algorithm (FWOA) is introduced in this work. Methods: The proposed algorithm uses the C 2 TE based selection criteria which depend on the connectivity, congestion, trust, and energy for selecting the suitable nodes for the communication. The proposed FractWhale-DSR algorithm finds the secured routing path in three phases. Results: The parameters, such as throughput, delay, Packet Delivery Ratio (PDR), and the energy define the performance of the proposed model. From the simulation results, the proposed FractWhale- DSR algorithm has an overall better performance with the values of 0.57265, 0.005118, 0.786325, and 75.88636% for throughput, delay, PDR, and energy respectively at the round of 25 for the MANET with 100 nodes. Conclusion: The proposed DSR strategy has the advantage of adaptability and scalability. Also, the router selects the alternate paths, when there is a link failure in the current network.

Background: In recent years, the computational memory and energy conservation have become a major problem in cloud computing environment due to the increase in data size and computing resources. Since, most of the different cloud providers offer different cloud services and resources use limited number of user’s applications. Objective: The main objective of this work is to design and implement a cloud resource allocation and resources scheduling model in the cloud environment. Methods: In the proposed model, a novel cloud server to resource management technique is proposed on real-time cloud environment to minimize the cost and time. In this model different types of cloud resources and its services are scheduled using multi-level objective constraint programming. Proposed cloud server-based resource allocation model is based on optimization functions to minimize the resource allocation time and cost. Results: Experimental results proved that the proposed model has high computational resource allocation time and cost compared to the existing resource allocation models. Conclusion: This cloud service and resource optimization model is efficiently implemented and tested in real-time cloud instances with different types of services and resource sets.

Background: Age estimation using face images has become increasingly significant in the recent years, due to diversity of potentially useful applications. Age group feature extraction, the local features, has received a great deal of attention. Objective: This paper derived a new age estimation operator called “Gradient Dual-Complete Motif Matrix (GD-CMM)” on the 3 x 3 neighborhood of gradient image. The GD-CMM divides the 3 x 3 neighborhood in to dual grids of size 2 x 2 each and on each 2 x 2 grid complete motif matrices are derived. Methods: The local features are extracted by using Motif Co-occurrence Matrix (MCM) and it is derived on 2 x 2 grid and the main disadvantage of this Motifs or Peano Scan Motifs (PSM) is they are static i.e. the initial position on a 2 x2 grid is fixed in deriving motifs, resulting with six different motifs. The advantage 3 x 3 neighborhood approaches over 2x 2 grids is the 3x3 grid identify the spatial relations among the pixels more precisely. The gradient images represent facial features more efficiently and human beings are more sensitive to gradient changes than original grey level intensities. Result: The proposed method is compared with other existing methods on FGNET, Google and scanned facial image databases. The experimental outcomes exhibited the superiority of proposed method than existing methods. Conclusion: On the GD-CMM, this paper derived co-occurrence features and machine learning classifiers are used for age group classification.

Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.