Articles published in IJEER

Special Issue on Intervention of Electrical, Electronics & Communication Engineering in Sustainable Development


Optimizing QoS-Based Clustering Using a Multi-Hop with Single Cluster Communication for Efficient Packet Routing

Modern day communication systems have gained a revolutionized growth in long-distance wireless data transmission. High speed packet transfer impacts quality requirements. Critical factors that ruin service quality (Qos) are calculated by the primary factors involving power efficiency, packet delivery ratio, and overall transmission and reception delay. A well-developed routing protocol with unique attributes should be deployed to give improved QoS. The drawback of single path routing in delivering a packet at traffic is challenging since it does not have an alternative path in case of path failure. This problem can be targeted by a properly structured protocol with a multipath mechanism. In this article, Multi-hop with single cluster (SCMC) protocol is designed to increase the overall system efficiency by improving bandwidth, packet delivery ratio (PDR), reducing communication delay, and quality improvement.

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Object Detection and Recognition in Real Time Using Deep Learning for Visually Impaired People

This paper aims at combining object detection at real time and recognition with suitable deep learning methods in order to detect and recognize objects position as well as the names of multiple objects detected by the camera using an object detector algorithm. This is to aid the visually impaired user without the help of any other person. The image and video processing algorithms were designed to take real-time inputs from the camera, Deep Neural Networks were used to predict the objects and uses Google’s famous Text-To-Speech (GTTS) API module for the anticipated voice output precisely detecting and recognizing the category or class of objects and locations contained. Our best result shows that the system recognizes 91 categories of outdoor objects and produces the output in speech i.e. in an audio format even when a reduced amount of spectral information from the data is available.

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Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application

Cyber security comes with a combination of various security policies, AI techniques, network technologies that work together to protect various computing resources like computing networks, intelligent programs, and sensitive data from attacks. Nowadays, the shift to digital freedom had led to opened many new challenges for financial services. Cybercriminals have found the ability to leverage e- currency exchanges and other financial transactions to perform their fraudulent activities. The unregulated channel makes it essential for banks and financial institutions to deploy advanced AI & ML (DL) techniques to fight cybercrime. This can be implemented by deploying AI & ML (DL) techniques. Customers are experiencing an increase in the fraud-hit rate in financial banking operations. It is difficult to defend against dynamic cyber-attacks using conventional non- dynamic algorithms.

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Enhanced Visual Analytics Technique for Content-Based Medical Image Retrieval

Content-based image retrieval (CBIR) is a method for searching that finds related images in a medical database. Furthermore, a clinical adaptation of CBIR is hampered in part by a contextual gap that is the disparity among the person characterization of the picture and the framework characterization of the image. This technique makes it tough for the user to validate the fetched images that are similar to the query image in addition to that it only fetches the images of top-ranked and ignores the low-ranking ones. Visual Analytics for Medical Image Retrieval is a novel procedure for medicinal CBIR proposed in this research (VAMIR).

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Caries Detection from Dental Images using Novel Maximum Directional Pattern (MDP) and Deep Learning

Various machine learning technologies and artificial intelligence techniques were applied on different applications of dentistry. Caries detection in orthodontics is a very much needed process. Computer-aided diagnosis (CAD) method is used to detect caries in dental radiographs. The feature extraction and classification are involved in the process of caries detection in dental images. In the 2D images the geometric feature extraction methods are applied and the features are extracted and then applied to machine learning algorithms for classification. Different feature extraction techniques can also be combined and then the fused features can be used for classification. Different classifiers support vector machine (SVM), deep learning, decision tree classifier (DT), Naïve Bayes (NB) classifier, k-nearest neighbor classifier (KNN) and random forest (RF) classifier can be used for the classification process.

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Application of Chaotic Increasing Linear Inertia Weight and Diversity Improved Particle Swarm Optimization to Predict Accurate Software Cost Estimation

Nowadays usage of software products is increases exponential in different areas in society, accordingly, the development of software products as well increases by the software organizations, but they are unable to focus to predict effective techniques for planning resources, reliable design, and estimation of time, budget, and high quality at the preliminary phase of the development of the product lifecycle. Consequently, it delivered improper software products. Hence, a customer loses the money, time, and not belief on the company as well as effort of teamwork will be lost. We need an efficient and effective accurate effort estimation procedure.

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Compression of Medical Images Using Wavelet Transform and Metaheuristic Algorithm for Telemedicine Applications

Medical image compression becomes necessary to efficiently handle huge number of medical images for storage and transmission purposes. Wavelet transform is one of the popular techniques widely used for medical image compression. However, these methods have some limitations like discontinuity which occurs when reducing image size employing thresholding method. To overcome this, optimization method is considered with the available compression methods. In this paper, a method is proposed for efficient compression of medical images based on integer wavelet transform and modified grasshopper optimization algorithm. Medical images are pre-processed using hybrid median filter to discard noise and then decomposed using integer wavelet transform.

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Performance Analysis of MIMO System Using Fish Swarm Optimization Algorithm

During the signal identification process, massive multiple-input multiple-output (MIMO) systems must manage a high quantity of matrix inversion operations. To prevent exact matrix inversion in huge MIMO systems, several strategies have been presented, which can be loosely classified into similarity measures and evolutionary computation. In the existing Neumann series expansion and Newton methods, the initial value will be taken as zero as a result wherein the closure speed will be slowed and the prediction of the channel state information is not done properly. In this paper, fish swarm optimization algorithm is proposed in which initial values are chosen optimally for ensuring the faster and accurate signal detection with reduced complexity. The optimal values are chosen between 0 to 1 value and the initial arbitrary values are chosen based on number of input signals.

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FPGA Implementation of High-Performance s-box Model and Bit-level Masking for AES Cryptosystem

The inadequacies inherent in the existing cryptosystem have driven the development of exploit the benefits of cipher key characteristics and associated key generation tasks in cryptosystems for high-performance security systems. In this paper, cipher key-related issues that exists in conventional symmetric AES crypto system is considered as predominant issues and also discussed other problems such as lack of throughput rate, reliability and unified key management problems are considered and solved using appropriate hierarchical transformation measures. The inner stage pipelining is introduced over composite field based s-box transformation models to reduce the path delay. In addition to that, this work also includes some bit level masking technique for AES.

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A Survey on Various Machine Learning Techniques for an Efficient Brain Tumor Detection from MRI Images

On account of the uncontrolled and quick growth of cells, Brain Tumor (BT) occurs. It may bring about death if not treated at an early phase. Brain Tumor Detection (BTD) has turned out to be a propitious research field in the current decennia. Precise segmentation along with classification sustains to be a difficult task in spite of several important efforts and propitious results in this field. The main complexity of BTD emerges from the change in tumor location, shape, along with size. Providing detailed literature on BTD via Magnetic Resonance Imaging (MRI) utilizing Machine Learning (ML) methods to aid the researchers is the goal of this review. Diverse datasets are mentioned which are utilized most often in the surveyed articles as a prime source of Brain Disease (BD) data.

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Cross Layer Based Dynamic Traffic Scheduling Algorithm for Wireless Multimedia Sensor Network

The data traffic volume is generally huge in multimedia networks since it comprises multimodal sensor nodes also communication takes place with variable capacity during video transmission. The data should be processed in a collision free mode. Therefore, the packets should be scheduled and prioritized dynamically. Dynamic traffic scheduling and optimal routing protocol with cross layer design is proposed here to select the energy efficient nodes and to transmit the scheduled data effectively. At first, the optimal routes are discovered by selecting the best prime nodes then the packets are dynamically scheduled on the basis of severity of data traffic. The proposed method works in two stages such as (i) Selection of chief nodes and (ii) Dynamic packet scheduling.

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Auto-Threshold Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation

Discovering patterns from large datasets is inevitable in the modern data driven civilization. Many research works, and business models are depending on this data excavation task. An efficient method for identifying and categorizing different data patterns from an exponentially growing database is required to perform a clear data excavation. A set of fresh processes such as Repeat Pattern Finder, Repeat Pattern Table, Repeat Pattern Threshold Analyzer, and Repeat Pattern Node are conceptualized in this work named as Auto-Threshold Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation (AT-DME-FP).

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