Sea Surface Temperature plays a crucial role in the Earth's climate system, impacting weather patterns, ocean circulation, and marine habitats. Accurate SST forecasting is vital for various environmental and climatic applications, aiding in informed decision-making. This research proposes two neural architectures for automated time series forecasting of sea surface temperature, SeaNet-1 which is a combination of Long Short-Term Memory and Gated Recurrent Unit networks, and SeaNet-2, which is a transformer-based model with a relatively low number of parameters as compared to SeaNet-1. Experimental findings showed that the suggested models can predict sea surface temperature effectively and precisely. The experimental results achieved a remarkable R-squared score of 95.16% and an incredibly low RMSE of 27.28%. The proposed models perform better in terms of performance and computational efficiency when compared with other existing state-of-the-art models. The proposed models offer promising tools for oceanography, climate studies, and environmental management, supporting informed decision-making and sustainable resource utilization.

Sea Surface Temperature (SST) is a key indicator of the global climate system. It is an essential factor in simulations of atmospheric models, weather predictions, and the study of marine ecosystems. Of these interests, one of the most important is studying changes in sea surface temperatures that result from the anthropogenic forcing of climate, a process known as global warming. An accurate prediction of sea surface temperature is highly beneficial towards understanding climate change, preserving marine ecosystems, etc. This research focuses on processing sea surface temperature data to perform a time series forecasting using a unique long short-term memory neural architecture to predict sea surface temperature in Bay of Bengal region. Given its reasonable high accuracy and low error rate, the assessment metrics show that the recommended neural network for SST prediction can be employed for real-time applications.

Drones have many applications in our daily lives and can be employed for agricultural, military, commercial, disaster relief, research and development, and many other purposes. There has been a significant increase in the usage of small drones/unmanned aerial vehicles in recent years. Consequently, there is a rising potential for small drones to be misused for illegal activities, such as terrorism and drug smuggling. Hence, there is a need for accurate and reliable UAV identification that can be used in various environments. In this paper, different versions of the current state-of-the-art object detection model, i.e., YOLO models, are used, by working on the principles of computer vision and deep learning to detect small UAVs. To improve the accuracy of small UAV detection, this paper proposes the application of various image-processing techniques to the current detection model, which has resulted in a significant performance increase. In this study, a mAP score of 96.7% was obtained for an IoU threshold of 50% along with a precision value of 95% and a recall of 95.6%. Distance-wise analysis of drones (i.e., for close, mid, and far ranges) was also performed to measure distance-wise accuracies.

Brain is an organ which defines individuality of a human being.It is quite evident to make use of it for human biometric identification.The electrical impulses generated by the human brain while performing various tasks and showing behavior have been studied conventionally for human biometric authentication.But researchers have shown that the behavioral biometrics are very unstable and cannot guarantee successful authentication all the time.Thus, it is necessary to devise biometric features from the brain structure which is comparatively stable at macro level.Brain structural information can be identified from magnetic resonance imaging (MRI) images but for biometrics we require portable and low-cost machines for MRI imaging.Feasibility of the brain structural biometrics in near future has been established through a ladder of review of portable MRI machines.We have proposed a novel secure approach covering optimal structural information of the 3D brain which mitigates the unaddressed problems by previous approaches of brain biometric using structural information of the brain.The robustness and scalability of the template has been investigated thoroughly and found to be optimistic to establish a novel biometric modality in near future.

The brain structure is unique, hidden and comparatively stable over time than the brain's electrical signals for authentication in high-security areas.Unlike conventional biometric modalities, brain structure needs more security since the theft of the human brain's actual structure is irreversible.Issues such as scalability, uniqueness, robustness against MRI acquisition noise and template security have been insufficiently addressed by the previous methods.The useful brain structures have been segmented using an adaptive segmentation and boundary extraction algorithm.The proposed angular transformation of the original multipronged slices in the coronal, sagittal and horizontal planes, increases the effective surface area and optimizes the structural information in the brain print.Subsequent application of the irreversible layered encryption of the multipronged slices increases the effective number of final brain structural curves in the final brain print.Due to irreversibility, it is impossible to obtain the original brain structure from the final brain print.We have currently tested for 3D brain maps of 209 normal subjects.Template matching has been done through Hausdorff distance among templates.This is also the first method being reported to perform with high accuracy of 99.94% even during noisy MRI acquisition of 10% pixels.The false acceptance rate and false rejection rate in the noisy conditions are 0% and 1.1% respectively.The equal error rate is 8.4%.

Identification of a subject suffering from Bipolar Disorder (BD) is still an open end problem in present time even after they undergo effective prescribed stages of treatments. Review of literature towards existing investigation in BD shows presence of different variants of approaches in order to diagnose BD. However, the prominent research challenge is to consider the non-verbal behavioral signals and perform investigation in order to accurately identify the actual state of diagnosis by explicitly differentiating the normal user (control group) from the subjects suffering from different variant of BD. Implemented on Python, the proposed system make use of Bayesian Neural Network to showcase it as a suitable machine learning approach for correctly classifying the type of BD using statistical inference for better modelling purpose.

This chapter discusses the various impacts of industrial automation and artificial intelligence in supply chains with the onset of COVID-19. The term industrial automation is influenced by rapid globalization and the various industrial revolutions that have caused the dire need for automation of industrial tasks to reduce human efforts. The chapter dives into the multiple fields affected by COVID-19 and how automation was used to deal with the situation, stabilize the supply chains, and maintain the profitability of organizations. Digital globalisation has led to the development of global supply chains. The use of technologies and cognitive automation and its effects have been discussed in the chapter. Machine learning has been used to get insight into the factors that affect supply chains and help their functioning.

In the present state of health and wellness, mental illness is always deemed less importance compared to other forms of physical illness. In reality, mental illness causes serious multi-dimensional adverse effect to the subject with respect to personal life, social life, as well as financial stability. In the area of mental illness, bipolar disorder is one of the most prominent type which can be triggered by any external stimulation to the subject suffering from this illness. There diagnosis as well as treatment process of bipolar disorder is very much different from other form of illness where the first step of impediment is the correct diagnosis itself. According to the standard body, there are classification of discrete forms of bipolar disorder viz. type-I, type-II, and cyclothymic. Which is characterized by specific mood associated with depression and mania. However, there is no study associated with mixed-mood episode detection which is characterized by combination of various symptoms of bipolar disorder in random, unpredictable, and uncertain manner. Hence, the model contributes to obtain granular information with dynamics of mood transition. The simulated outcome of the proposed system in MATLAB shows that resulting model is capable enough for detection of mixed mood episode precisely

Bipolar disorder is one of the most challenging illnesses where medical science is still struggling to achieve its landmark therapies. After reviewing existing prediction-based approaches towards investigating bipolar disorder, it is noted that existing approaches are more or less symptomatic and relates depression as sadness. It implies various theories that don't consider many precise indicators of confirming bipolar disorder. Therefore, this manuscript presents a novel framework capable of treating the dataset of depression and fine-tune it appropriately to subject it further to a machine learning-based predictive scheme. The proposed system subjects its dataset for a series of data cleaning operations followed by data preprocessing using a standard scale of rating bipolar level. Further usage of feature engineering and correlation analysis renders more contextual inference towards its statistical score. The proposed system also introduces a Recurrent Decision Tree that further contributes towards the predictive outcome of bipolar disorder. The outcome obtained showcases that the proposed scheme performs better than the conventional decision tree.

The paper involves critical evaluation of all the significant encryption, decryption, and cryptography techniques that are being used for DNA (deoxyribonucleic acid) data Storage. This paper covers the basics of Data storage in DNA and how it can be used to stockpile data and how it is highly promising in changing the data storage methods of the world in the foreseeable future. All the vital methods which are being used for DNA data storage have been discussed. These methods are also be applicable for data storage density and graphs are plotted. This paper also examines how DNA is being used as a tool for cryptography along with the fundamental limitations of DNA storage. Towards the end, the future scope of DNA data storage is questioned, and with the help of a density graph the research predicts whether "DNA data storage has a future scope or not"ƒ

Emotions play a very significant role in the daily life of the individuals, in decision-making processes and in the understanding of the surrounding world. The emotional state of the human has become a priority due to the growing interest of the research community in determining the emotional interactions between humans and computers. There are multiple measures to achieve the same, and one such promising tool is electroencephalography (EEG). EEG is an electrophysiological monitoring method to record electrical activity of the brain which is noninvasive, with the electrodes placed on the scalp to measure the real-time changes in voltage caused by brain’s electrical activity, providing good temporal resolution. This paper presents a neurophysiological survey of research performed on emotions (basically valence, arousal and dominance) using EEG signals. The main focus is to compare the conventional methods carried out on the aspects of recognition process like the number and type of the subjects preferred, the features extracted and the classifiers chosen. The review summarizes a set of current practices and performance outcomes providing practical suggestions that research scholars can follow to achieve validated and high-quality results.

In this paper the possibility of using digital fingerprints to estimate age-groups of human being, particularly children is investigated. To our knowledge, age-group estimation in humans, using digital fingerprints have not been addressed formally. Age-group estimation can be applied in many areas like on-line child protection, access control and customized internet services etc. Motivated by the fact that human digital fingerprint vary in texture as the person ages, a multi-resolution texture approach for automatic age-group estimation has been presented in this paper. Three standard classifiers were used to judge the accuracy of the proposed method. In the process of this research study, a novel method for digital fingerprint reference point generation was developed, which provides reference point for very poor quality images also. The proposed reference point generation method is compared with core-point method using FG-NET DB1 dataset. Experimental results proves that a digital fingerprint can be used to identify age-groups, particularly children. A classification accuracy of 80 percent was achieved for children below the age of 14 by using the aforesaid method.

Thyroid disease is a major cause of formation in medical diagnosis and in theprediction, onset to which it is a difficult axiomin the medical research. Thyroid gland is one of the most important organs in our body. The secretions of thyroid hormones are culpable in controlling the metabolism. Hyperthyroidism and hypothyroidism are one of the two common diseases of the thyroid that releases thyroid hormones in regulating the rate of body's metabolism. Data cleansing techniques were applied to make the data primitive enough for performing analytics to show the risk of patients obtaining thyroid. The machine learning plays a decisive role in the process of disease prediction and this paper handles the analysis andclassificationmodels that are being used in the thyroid disease based on the information gathered from the dataset taken from UCI machine learning repository. It is important to ensure a decent knowledge base that can be entrenched and used as a hybrid model in solving complex learning task, such as in medical diagnosis and prognostic tasks. In this paper, we also proposed different machine learning techniques and diagnosis for the prevention of thyroid. Machine Learning Algorithms, support vector machine (SVM), K-NN, Decision Trees were used to predict the estimated risk on a patient's chance of obtaining thyroid disease.

In this paper we investigate whether human digital fingerprints can be used to estimate human age-groups. To our knowledge, human age-group estimation using digital fingerprints have not been addressed formally. Human age-group estimation can be applied in the areas of online child protection, age based access control or customized services based on estimated age groups. Motivated by the fact that human digital fingerprint vary in width ranging from birth to adulthood but pattern remains the same, we have developed a procedure to extract discriminating features using Curve let Transform to classify fingerprints into three age groups. Experimental results show the feasibility of our method which can be used to protect children over cyberspace by automatically customizing their access according to their age group.

Mobile ad hoc network, is nowadays becoming extremely famous in research vicinity. A range of protocols and methodology is coming into account to resolve an assortment of issues associated with a mobile ad hoc network. The nodes in mobile ad hoc network are present in ad hoc fashion i.e.; they are connecting to each other without using wires. In addition, they do not necessitate any central authority for performing their tasks. This thing creates mobile ad hoc network active and disseminated in character. In this paper, we implemented improved Watchdog protocol called as I-Watchdog protocol with Destination-Sequenced Distance-Vector Routing (DSDV) routing protocol that provides efficient and secure routing with prevention of denial of service attack as well as detection of congestion in the network background. In this paper, proposed I-Watchdog procedure does proficient recognition of the presence of malicious nodes in a mobile ad hoc network as well as it finds the genuine reason of the happening of loss of packets. Additionally, we will analysis the improved performance of mobile ad hoc network in the presence of DSDV with I-Watchdog protocol in provisions of packet drop ratio (PDR), throughput along with end-to-end delay.

This paper aims at the development of a cloud services provisioning framework by developing a dynamic priority job scheduler cum load-balancer for the cloud. The main motive of this paper is to provide a means of managing the job requests in a flexible and cost-effective way, both for the customer and the cloud service provider. In order to make the cloud scalable and adaptable to the changing needs and the increasing number of the users, proper and judicious allocation of resources is the utmost demand. A load balancer plays a very critical role in the scheduling of services into the cluster of virtual machines formed inside the cloud and also ensures optimum utilization of the processing power of various virtual systems. The paper presents a hierarchical approach to give a scalable model for task scheduling. It lists a primitive three tier and an improved four tier architecture mitigating various provisioning concerns like optimal utilization of resources, handling the large number of requests and providing reliable cost effective services. In the scheduler module, is implemented an adaptive dynamic priority scheduling scheme. The scheduler framework takes into account the client - cloud interface and the inner mobility of the job request. The hierarchy takes care of the two important concerns in cloud provisioning that are task scheduling and virtual resource allocation.

In this paper we investigate whether human digital fingerprints can be used to estimate human age-groups. To our knowledge, human age-group estimation using digital fingerprints have not been addressed formally. Human age-group estimation can be applied in the areas of online child protection, age based access control or customized services based on estimated age-groups. Motivated by the fact that human digital fingerprint vary in width ranging from birth to adulthood but pattern remains the same, we have developed a procedure to extract discriminating features using Curvelet Transform to classify fingerprints into three age groups. Experimental results show the feasibility of our method which can be used to protect children over cyberspace by automatically customizing their access according to their age-group.

In this paper we investigate whether human digital fingerprints can be used to estimate human age-groups. To our knowledge, human age-group estimation using digital fingerprints have not been addressed formally. Human age-group estimation can be applied in the areas of online child protection, age based access control or customized services based on estimated age-groups. Motivated by the fact that human digital fingerprint vary in width ranging from birth to adulthood but pattern remains the same, we have developed a Gabor based method to classify fingerprints into three age groups. Experimental results show the feasibility of our method which can be used to protect children over cyberspace by automatically customizing their access according to their age-group.