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Millimeter wave (mmWave) bands attract large research interest as they can potentially lead to data rate of almost 10Gbits/sec and huge available bandwidth where as the microwave frequencies are limited to 1Gbits/s. This paper presents a comprehensive review of millimeter wave communications, frequency bands proposed by ITU, applications of mmWaves, advantages, limitations, challenges and research directions. Various antennas proposed by researchers for mmWave applications are described in detail. The described models are analyzed and compared with common antenna parameters. Applications like HD gaming, ultra high definition multimedia, security and surveillance demand for high data rates and more bandwidth. These all applications will drive mmWvaes technology to develop and offer wide bandwidths with high speed data.

Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent years, and also discusses the fundamentals of deep learning concepts applicable to medical image segmentation. The study of deep learning can be applied to image categorization, object recognition, segmentation, registration, and other tasks. First, the basic ideas of deep learning techniques, applications, and frameworks are introduced. Deep learning techniques that operate the ideal applications are briefly explained. This paper indicates that there is a previous experience with different techniques in the class of medical image segmentation. Deep learning has been designed to describe and respond to various challenges in the field of medical image analysis such as low accuracy of image classification, low segmentation resolution, and poor image enhancement. Aiming to solve these present issues and improve the evolution of medical image segmentation challenges, we provide suggestions for future research.

Twitter is a social media that developed rapidly in today’s modern world. As millions of Twitter messages are sent day by day, the value and importance of developing a new technique for detecting spammers become significant. Moreover, legitimate users are affected by means of spams in the form of unwanted URLs, irrelevant messages, etc. Another hot topic of research is sentiment analysis that is based on each tweet sent by the user and opinion mining of the customer reviews. Most commonly natural language processing is used for sentiment analysis. The text is collected from user’s tweets by opinion mining and automatic sentiment analysis that are oriented with ternary classifications, such as “positive,” “neutral,” and “negative.” Due to limited size, unstructured nature, misspells, slangs, and abbreviations, it is more challenging for researchers to find sentiments for Twitter data. In this paper, we collected 600 million public tweets using URL-based security tool and feature generation is applied for sentiment analysis. The ternary classification is processed based on preprocessing technique, and the results of tweets sent by the users are obtained. We use a hybridization technique using two optimization algorithms and one machine learning classifier, namely particle swarm optimization and genetic algorithm and decision tree for classification accuracy by sentiment analysis. The results are compared with previous works, and our proposed method shows a better analysis than that of other classifiers.

Neonatal orbital cellulitis is an uncommon but serious infection that can lead to an orbital abscess, often requiring surgical intervention. We report a rare case of a term neonate presenting on day 20 of life with persistent fever and irritability without initial orbital signs. Blood culture revealed methicillin-resistant Staphylococcus aureus (MRSA), and intravenous vancomycin was initiated. By day 3 of admission, the infant developed periorbital swelling; imaging confirmed a retro-orbital abscess. With timely antibiotic therapy, the infection resolved without the need for surgical drainage. Notably, there was a history of inappropriate infant care practices, including bottle feeding and external application of native substances, which may have contributed to the infection. This case underscores the importance of considering orbital infections in neonates with sepsis of unclear origin, initiating early MRSA-targeted therapy and promoting good neonatal care practices. Conservative management may be successful in uncomplicated cases with early diagnosis and close monitoring.

A subset of Wireless Sensor Networks, Wireless Body Area Networks (WBAN) is an emerging technology. WBAN is a collection of tiny pieces of wireless body sensors with small computational capability, communicating short distances using ZigBee or Bluetooth, an application mainly in the healthcare industry like remote patient monitoring. The small piece of sensor monitors health factors like body temperature, pulse rate, ECG, heart rate, etc., and communicates to the base station or central coordinator for aggregation or data computation. The final data is communicated to remote monitoring devices through the internet or cloud service providers. The main challenge for this technology is energy consumption and secure communication within the network and the possibility of attacks executed by malicious nodes, creating problems for the network. This system proposes a suitable trust model for secure communication in WBAN based on node trust and data trust. Node trust is calculated using direct trust calculation and node behaviours. The data trust is calculated using consistent data success and data aging. The performance is compared with an existing protocol like Trust Evaluation (TE)-WBAN and Body Area Network (BAN)-Trust which is not a cryptographic technique. The protocol is lightweight and has low overhead. The performance is rated best for Throughput, Packet Delivery Ratio, and Minimum delay. With extensive simulation on-off attacks, Selfishness attacks, sleeper attacks, and Message suppression attacks were prevented.

Investors depend on various sources for decision-making in trading, with maximum profit earning as the primary objective. A predictive model with experience is essential in developing an automated trading system to make wise decisions and avoid risky situations. The present research aims to investigate an artificial agent simulation model that maximizes trading profit. We have designed an innovative IASMFT (Intelligent Agent Simulation Model for Future Trading) for stock trading and profit maximization. IASMFT integrates Fuzzy-c means clustering, GAN (Generative Adversarial Network), and Reinforcement learning. The experimental data consists of historical datasets of six stocks from 8th August 2016 to 31st March 2023. The existing and proposed models are evaluated based on Domain–specific metrics and General regression metrics. The proposed model, IASMFT, maximized the trading profit with an RMSE of 10.14 and MAE of 2.75 and outperformed the models in the recent literature. The findings indicate that the combination of Domain–specific and General regression metrics is a perfect fit to evaluate trading profit and maximization models. IASMFT maximizes the trading profit and is a reliable approach that can be implemented in a real-time scenario.

INTRODUCTION: Protein structure prediction is critical for recommendation personalized medicine and drug discovery. This paper introduces a robust approach using 3D Convolution Neural Networks (3D CNN’s) to improve the accuracy of the structure of protein structure thus contributing for the drug recommendation system. OBJECTIVES: In contrast to conventional techniques, 3D CNNs are able to identify complicated folding patterns and comprehend the subtle interactions between amino acids because they are able to capture spatial dependencies inside protein structures. METHODS: Data sets are collected from Protein Data Bank, including experimental protein structures and the drugs that interact with them, are used to train the model. With the efficient processing of three-dimensional data, the 3D CNNs exhibit enhanced capability in identifying minute structural details that are crucial for drug binding. This drug recommendation system novel method makes it easier to find potential drugs that interact well with particular protein structures. RESULTS: The performance of the proposed classifier is compared with the existing baseline methods with various parameters accuracy, precision, recall, F1 score, mean squared error (MSE)  and area under the receiver operating characteristic curve (AUC-ROC). CONCLUSION: Deep learning and 3D structural insights work together to create a new generation of tailored and focused therapeutic interventions by speeding up the drug development process and improving the accuracy of pharmacological recommendations.

Machine learning has become a great attention to find optimize solutions in different areas and is anticipated to play a vital role in our upcoming technologies. This paper presents a comprehensive review on basic optimization algorithms for micro-strip patch antenna design using machine learning. Classification of machine learning based algorithms: deterministic, stochastic and surrogate model assistant is discussed. Further machine learning models training for optimizing output and for prediction of antenna parameters is presented in this paper. This paper is useful to the readers who work on a particular antenna using the Machine Learning Techniques.

Dynamical and Statistical models are operationally used by Snow and Avalanche Study Establishment (SASE) for winter precipitation forecasting over the Northwest Himalayas (NWH). In this paper, a statistical regression model developed for seasonal (December–April) precipitation forecast over Northwest Himalaya is discussed. After carrying out the analysis of various atmospheric parameters that affect the winter precipitation over the NWH two parameters are selected such as North Atlantic Oscillation (NAO) and Outgoing Long wave Radiation (OLR) over specific areas of North Atlantic Ocean for the development of statistical regression model. A set of 27 years (1990–1991 to 2016–2017) of observed precipitation data and parameters (NAO and OLR) are utilized. Out of 27 years of data, first 20 years (1990–1991 to 2009–2010) are used for the development of regression model and remaining 7 years (2010–2011 to 2016–2017) are used for the validation purpose. Precipitation over NWH mainly associated with Western Disturbances (WDs) and the results of the present study reveal that NAO during SON has negative relationship with WDs and also with the winter precipitation over same region. Quantitative validation of the multiple regression model, result shows good Skill Score and RMSE-observations standard deviation ratio (RSR) which is 0.79 and 0.45 respectively and BIAS − 0.92.

Background and objectivesPreterm neonates are at risk of impaired growth. Hyponatraemia in preterm neonates is due to renal immaturity and can lead to growth failure. We aimed to assess the effect of early enteral sodium supplementation on weight gain velocity from birth to 34 weeks postmenstrual age in preterm neonates.MethodsIn this double-blinded randomised controlled trial, we recruited neonates born between 25 weeks and 30 weeks+6 days of gestation who received a minimum feed volume of 100 mL/kg/day before 10 days of life. In the intervention group, 4 mEq/kg/day of oral sodium was administered using 15% saline and, in the control group, normal saline was given as a placebo. The primary outcome was mean weight gain velocity in g/kg/day.ResultsA total of 104 neonates were recruited with 52 infants in each group. Mean (SD) weight gain velocity (g/kg/day) was significantly greater in intervention than in control group (18.0±4.4 vs 14.4±3.9; mean difference 3.6 with 95% CI 2.04 to 5.27, p<0.001). Linear growth (cm/week) was greater in intervention than in control group (0.88±0.18 vs 0.73±0.28 cm/week; mean difference 0.14 with 95% CI 0.05 to 0.24, p=0.04). Subgroup analysis in extreme preterm showed similar results. There was no significant difference in head growth or clinical outcomes between the groups.ConclusionEarly postnatal sodium supplementation improves in-hospital weight gain and linear growth in very preterm neonates.