Many countries around the world have been influenced by Covid-19 which is a serious virus as it gets transmitted by human communication. Although, its syndrome is quite similar to the ordinary flu. The critical step involved in Covid-19 is the initial screening or testing of the infected patients. As there are no special detection tools, the demand for such diagnostic tools has been increasing continuously. So, it is eminently admissible to find out positive cases of this disease at the earliest so that the spreading of this dangerous virus can be controlled. Although, some methods for the detection of Covid-19 patients are available, which are performed upon respiratory based samples and among them, a critical approach for treatment is radiologic imaging or X-ray imaging. The latest conclusions obtained from X-ray digital imaging based algorithms and techniques recommend that such type of digital images may consist of significant facts regarding the SARS-CoV-2 virus. The utilization of Deep Neural Networks based methodologies clubbed with digital radiological imaging has been proved useful for accurately identifying this disease. This could also be adjuvant in conquering the problem of dearth of competent physicians in far-flung areas. In this paper, a CheXImageNet model has been introduced for detecting Covid-19 disease by using digital images of Chest X-ray with the help of an openly accessible dataset. Experiments for both binary class and multi-class have been performed in this work for benchmarking the effectiveness of the proposed work. An accuracy of 100 is reported for both binary classification (having cases of Covid-19 and Normal X-Ray) and classification for three classes (including cases of Covid-19, Normal X-Ray and, cases of Pneumonia disease) respectively.

Mobility prediction and fault tolerance are extremely difficult due to underwater characteristics. Energy drain is one of the major causes for node faults.Hence, in this research article, a hybrid optimization algorithm is developed for fault-tolerant and accurate localization in UWSN. In this technique, Artificial Butterfly Optimization (ABO) algorithm is applied for finding the distance between the anchors and the sensors. Each non-localized node runs ABO algorithm for finding the distance amid the anchor or beacon nodes. Then, applying Quaternion-based Backtracking Search Optimization (QBSA) algorithm, non-localized sensor nodes are localized and to decrease the localization error based on the Received Signal Strength Indicator (RSSI), battery energy, and distance parameters. Aqua-Sim a tool kit of NS2 is an open-source software developed for Underwater Wireless Sensor Network research, and this proposed algorithm will be implemented using this software. By simulation results, it is shown that the proposed optimization algorithm reduces the localization error, latency, and cost and energy consumption.

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Machine learning (ML) techniques provide the learning capability to a system and encourage adaptation into the environment, based upon many logical and statistical operations. The prime goal of ML is to recognize the complex patterns and make decisions based on the results. There are various ML algorithms which are implemented to secure the mobile ad-hoc networks. The infrastructure-less environment of MANETs poses a great challenge in implementation of the security systems. The security approaches in MANETs mainly focus on intrusion detection, malicious attacks mitigation, elimination of outlier/misbehavior/selfish nodes and securing routing paths. The researchers have been using cutting edge technologies for providing efficient security solutions by taking into the consideration of dynamic environment of MANETs. These technologies include machine learning, Artificial Intelligence (AI), Genetic Algorithms based methods, biological-inspired algorithms and so on. This paper presents a comprehensive and systematic study of various modern approaches for intensifying security in MANETs.

The hand-written alphabet recognition and classification plays an important role in pattern recognition, computer vision as well as image processing. In last few decades, a plethora of applications based on this area are developed such as sign identification, multi lingual learning systems etc. This paper classifies samples of hand-written alphabets into different classes using various machine learning methods. The challenging factor in hand written alphabets recognition lie in variations of style, shape and size of the letters. In this paper a simplified and accurate methodology is proposed based upon engineered features which are evaluated and tested using MatLab tool in comparison to other existing methods. The proposed system achieves a substantial amount of accuracy of 98% as compared to the state of the art approaches.