Articles published in IJEER

Special Issue on Recent Developments in Communication Technology using Machine Learning Techniques


Effect of Machine Learning Techniques for Efficient Classification of EMG Patterns in Gait Disorders

Gait disorder is very common in neurodegenerative diseases and differentiating among the same kinematic design is a very challenging task. The muscle activity is responsible for the creation of kinematic patterns. Hence, one optimal way to monitor this issue is to analyse the muscle pattern to identify the gait disorders. In this paper, we will investigate the possibility of identifying GAIT disorders using EMG patterns with the help of various machine learning algorithms. Twenty-five normal persons (13 male and 12 females, age around 28 years of age) and 21 persons having GAIT disorders (11 male and 10 females, age around 67 years of age). Four different machine learning algorithms have been used to identify EMG patterns to recognize healthy and unhealthy persons.

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Image Steganography Technique based on Singular Value Decomposition and Discrete Wavelet Transform

Steganography is a technique of hiding information in digital media. In recent years plenty of work has been done in this domain, and the work can be compared on various parameters such as high robustness and large capacity to achieve a goal. This paper proposed the method of steganography in digital media using Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT). The DWT is a frequency-domain technique comprising DWT which comparatively offers better robustness and high PSNR value of stego image over other techniques. The proposed method works well for information hiding against AWGN (additive white Gaussian noise) attack and fulfills the objective to achieve high robustness and high PSNR.

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On the Connection of Matroids and Greedy Algorithms

Matroids are the combinatorial structure and Greedy algorithmic methods always produces optimal solutions for these mathematical models. A greedy method always selects the option that looks best at each step of process of finding optimal solution. In other words, it selects a choice which is optimal choice locally in such a strategy that this locally chosen option may direct to a solution that will be globally optimal. It is true that while selecting locally optimal solution at each stage, Greedy algorithms may not always yield optimal solutions [1-2], but if we can transform an unknown problem into matroid structure, then there must be a greedy algorithm that will always lead optimal solution for that unknown problem. The range of solutions provided by Greedy is large as compared to the applicability of the Matroid structure.

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Optimization of Software Quality Attributes using Evolutionary Algorithm

Software quality is a multidimensional concept. Single attribute can’t define the overall quality of the software. Software developer aims to develop software that possesses maximum software quality which depends upon various software quality attributes such as understand ability, flexibility, reusability, effectiveness, extendibility, functionality, and many more. All these software quality attributes are linked with each other and conflicting in nature. Further, these quality attributes depend upon the design properties of the software. During the designing phase of software, developers must optimize the design properties to develop good software quality. To obtain the appropriate value optimization is done. This paper implemented two multi-objective evolutionary algorithms (NSGA-2 and MOEA/D) to optimize software design properties to enhance software quality.

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Free Hold Price Predictor Using Machine Learning

People who want to buy a new home tend to save more on their budgets and market strategies. The current system includes real estate calculations without the necessary forecasts for future market trends and inflation. The housing market is one of the most competitive in terms of pricing and the same has varied greatly in terms of many factors. Asset pricing is an important factor in decision- making for both buyers and investors in supporting budget allocation, acquisition strategies and deciding on the best plans as a result, it is one of the most important areas in which machine learning ideas can be used to maximize and accurately anticipate prices. As a result, in this paper, we present the different significant factors that we employ to accurately anticipate property values.

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A Cost-Effective and Scalable Processing of Heavy Workload with AWS Batch

Recent technological advancements in the IT field have pushed many products and technologies into the cloud. In the present scenario, the cloud service providers mainly focus on the delivery of IT services and technologies rather than throughput. In this research paper, we used a scalable cost-effective approach to configure AWS Batch with AWS Fargate and CloudFormation and implemented it in order to handle a heavy workload. The AWS service configuration procedure, GitHub repository, and Docker desktop applications have been clearly described in this work.

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A New Fail-Stop Group Signature over Elliptic Curves Secure against Computationally Unbounded Adversary

If an adversary has unlimited computational power, then signer needs security against forgery. Fail Stop signature solves it. If the motive of the signature is to hide the identity of the signer who makes signature on behalf of the whole group then solution is Group signature. We combine these two features and propose “A new Fail Stop Group Signature scheme (FSGSS) over elliptic curves”. Security of our proposed FSGSS is based on “Elliptic curve discrete logarithm problem” (ECDLP). Use of elliptic curve makes our proposed FSGSS feasible to less bandwidth environment, Block chains etc. Due to security settings over elliptic curves, efficiency of proposed scheme increases in terms of computational complexity.

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