Contamination of drinking water sources by potentially toxic elements (PTEs) poses critical threats to human health via carcinogenic and non-carcinogenic pathways, yet comprehensive assessments targeting arsenic, selenium, cadmium and lead in Garhwal Himalayan groundwater systems have been absent. This study contributes to address this knowledge gap by evaluating As, Se, Cd and Pb concentrations in 88 groundwater samples from the Chamoli district of Uttarakhand, India, a tectonically active region where populations depend almost exclusively on groundwater for drinking purposes. It integrated pollution indexing, health risk assessment, spatial mapping and statistical analyses to comprehensively characterize contamination status, identify spatial patterns and apportion potential sources. The arithmetic mean of the elemental concentrations was in the sequence Cd (0.18 µg L⁻1) < Se (0.26 µg L⁻1) < As (3.66 µg L⁻1) < Pb (12.81 µg L⁻1). While Se and Cd concentrations fell within their permissible limits, As and Pb exceeded their regulatory thresholds in specific samples. The heavy metal pollution index ranged from 5.649 to 134.2 (mean = 27.875), with majority of water samples are suitable for drinking. Non-carcinogenic risk assessment revealed that hazard indices exceeded unity in 33 samples for children versus one sample for adults, hinting on age-specific vulnerabilities. Carcinogenic risk estimates for majority of samples exceeded the acceptable threshold of ≤ 1.0 × 10⁻⁶ for the ingestion pathway, however they remained within the acceptable limit for the dermal exposure pathway. Correlation analysis identified moderate positive association between Cd and Pb, suggesting common sources or co-mobility, while Se and Pb exhibited weak negative correlation, indicating independent origins. These findings establish critical baseline data for the Chamoli district, inform targeted remediation strategies and support sustainable groundwater management aligned with multiple SDG targets. To address its limitations, future studies should incorporate temporal monitoring, expand parameter coverage and employ advanced source apportionment techniques to strengthen contamination origin characterization.

The advent of internet and social media has brought ample pros and cons, but the foremost concern is security. There are numerous options available for providing security in data transmission, but one that really counts is DNA cryptography. This paper explores the integration of DNA cryptography with chaotic functions, a class of mathematical models characterized by sensitive dependence on initial conditions and inherent unpredictability. In this paper, a hybrid encryption scheme - VG6 Cipher is proposed that combines an innovative labyrinthine DNA-based Homophonic Substitution cipher with non-linear dynamics of chaotic systems (Discrete Hénon Map) to enhance the security of cryptographic protocols. The enormous key space ensures its robustness against the brute-force attacks, along with faster encryption and decryption. The quantitative metrices like NPCR, UACI, MSE & PSNR demonstrate the enhanced performance abilities of proposed cipher along with strong resistance against statistical and differential attacks.

Machine learning algorithms have been extensively employed in multiple domains, presenting an opportunity to enable privacy. However, their effectiveness is dependent on enormous data volumes and high computational resources, usually available online. It entails personal and private data like mobile telephone numbers, identification numbers, and medical histories. Developing efficient and economical techniques to protect this private data is critical. In this context, the current research suggests a novel way to accomplish this, combining modified differential privacy with a more complicated machine learning (ML) model. It is possible to assess the privacy-specific characteristics of single or multiple-level models using the suggested method, as demonstrated by this work. It then employs the gradient values from the stochastic gradient descent algorithm to determine the scale of Gaussian noise, thereby preserving sensitive information within the data. The experimental results show that by fine-tuning the parameters of the modified differential privacy model based on the varied degrees of private information in the data, our suggested model outperforms existing methods in terms of accuracy, efficiency and privacy.

More accessible data and the rise of advanced data analysis contribute to using complex models in decision-making across various fields. Nevertheless, protecting people’s privacy is vital. Medical predictions often employ decision trees due to their simplicity; however, they may also be a source of privacy violations. We will apply differential privacy to this end, a mathematical framework that adds random values to the data to provide secure confidentiality while maintaining accuracy. Our novel method Dual Noise Integrated Privacy Preservation (DNIPP) focuses on building decision forests to achieve privacy. DNIPP provides more protection against breaches in deep sections of the tree, thereby reducing noise in final predictions. We combine multiple trees into one forest using a method that considers each tree’s accuracy. Furthermore, we expedite this procedure by employing an iterative approach. Experiments demonstrate that DNIPP outperforms other approaches on real datasets. This means that DNIPP offers a promising approach to reconciling accuracy and privacy during sensitive tasks. In DNIPP, the strategic allocation of privacy budgets provides a beneficial compromise between privacy and utility. DNIPP protects privacy by prioritizing privacy concerns at lower, more vulnerable nodes, resulting in accurate and private decision forests. Furthermore, the selective aggregation technique guarantees the privacy of a forest by combining multiple data points. DNIPP provides a robust structure for decision-making in delicate situations, ensuring the model's effectiveness while safeguarding personal privacy.

Since December 2019, the world has been facing a unique health crisis due to coronavirus (COVID-19). India, the country with the second-highest population globally, is also facing this unprecedented crisis. Several government efforts in the form of guidelines, stringent travel rules, and lockdown phases could not prevent disease spread. These efforts, however, had a positive impact on the environment; but they had a very negative affect on the country's social and economic front. Around May 18, 2020, approximately one lakh people got infected, and by July, in just two months, this number reached eight lakhs. This paper presents the disease spread trend, along with efforts to curb the spread. The report aims to quantify the influence of mobility habits on the progression of COVID-19 in India. The ARIMA model is used to estimate the spread and also future mobility trends. Estimation results clearly indicate that apart from the environmental variables, mobility habits are other essential variables that influence the disease spread. The paper also estimates the transitions in mobility habits with the level of COVID-19 spread in the vicinity. The result presented here is original, and the validity of the findings may pave ways to deal with health emergencies and curb the possible return of the virus in the country.

CNN of deep learning has become a de-facto standard for binary and multiclass image classification. To assist drivers, autonomous system classification of traffic signs is an essential component. Due to its fast execution and high testing accuracy, CNN is the first choice of most computer vision tasks. CNNs have been gaining popularity for a couple of years because of their ability to generalize and classify data with towering accuracy. In this model, we implemented a traffic signs recognition algorithm using a CNN, and it is done using python and the deep learning framework Keras. Training of 43 traffic signs was done using CNN and Keras and tested under various parameters and scenarios, such as network depth, filter size, dropout rate, preprocessing, and segmentation techniques were used for training. The research work reached test accuracies of above 95%, and the experimental results confirmed a high efficiency.

Due to the ongoing development of computer and communication abilities, the IoT is becoming more and more significant in many smart applications. As a result, IoT devices produce plenty of data every day, which provides a strong basis for ML to succeed. The IoT data's strict privacy standards, however, make its machine learning extremely challenging. Numerous privacy-based ML strategies have been developed to safeguard the privacy of data. The majority of current schemes do not provide generic answers and only focus on specific models, which is not the best option for engineering practice. To solve this issue, we proposed a powerful machine learning model that protects privacy within a fog computing scenario. The software service provider (SSP) can train models while protecting the privacy of the data on the fog nodes using the APPML (Advanced Privacy Preserving Machine Learning) framework. Only SSP may access the model parameters, and it is possible to maintain the privacy of data stored at the fog nodes. Experimental results demonstrates that, when compared to the existing systems, our approach lowers the processing and communication overhead and provides the highest privacy preservation.

An epidemic model is employed to examine the dynamics of infectious disease transmission. They make predictions about the rate at which an epidemic will spread, how severe the disease will be, and other factors. The Susceptible-Infected-Recovered (is also known as SIR) model, is a straightforward epidemic model [1]. SIR models codify the most straightforward method to conceptualize an epidemic. The Susceptible-Exposed-Infected-Recovered model (known as SEIR) just expands on the Susceptible-Infected-Recovered model by including a further equation of exposed individuals. Persons get contaminated but are not yet contagious throughout a long time of isolation for some serious contaminations. The person is in compartment E (for exposed) at this time[3]. The impact of social estrangement has been examined in the research.

The current research on the topic of machine learning and especially the domain of natural language processing has gained much popularity in the modern era. One such framework for attaining NLP tasks is word embedding, which represents data as vectors, i.e., real numbers rather than words of natural language because neural networks do not understand them naturally. Word embeddings try to capture both syntactic and semantic information of words and capture relationships according to context and morphology. This paper reviews each word embedding technique available in the contemporary world ranging from traditional embeddings based on the frequency of terms to pre-trained embeddings like prediction-based embeddings. The goal of this paper is to present the myriad methods available for word embedding, classify their working patterns, also identify their pros and cons for working on text classification and detect their hegemony over the traditional methods of NLP.

Attribute-Based Encryption (ABE) is one of the new dreams for fine grained access control in cloud computing. A lot of exploration work has been done in both academic and industrial communities. Be that as it may, before ABE can be conveyed in information outsourcing frameworks, efficient enforcement of authorization policies and strategy refreshes are the principal obstacles. Consequently, to take care of this issue, efficient and secure attribute and client revocation ought to be proposed in unique ABE plot, which is yet a test in existing work. In this paper, they propose another cipher text-strategy ABE (CP-ABE) development with efficient attribute and client revocation, which generally eliminates the overhead calculation at information administration chief and information proprietor. Additionally, we present an efficient access control component based on the CP-ABE development with one outsourcing calculation specialist co-op.

Aspect-based opinion mining is one among the thought-provoking research field which focuses on the extraction of vivacious aspects from opinionated texts and polarity value associated with these. The principal aim here is to identify user sentiments about specific features of a product or service rather than overall polarity. This fine-grained polarity identification about myriad aspects of an entity is highly beneficial for individuals or business organizations. Extricating these implicit or explicit aspects can be very challenging and this paper elaborates copious aspect extraction techniques, which is decisive for aspect-based sentiment analysis. This paper presents a novel idea of combining several approaches like Part of Speech tagging, dependency parsing, word embedding, and deep learning to enrich the aspect-based sentiment analysis specially designed for Twitter data. The results show that combining deep learning with traditional techniques can produce excellent results than lexicon-based methods.

A new model that offers IT technology and services as a commodity is cloud computing. Cloud computing is innovative computing that has consequences, population groups, systems, and brings with it a multitude of benefits, among which is easier and faster storage and access to data from anywhere and anytime. Move technology and sensitive data from the jurisdiction of confidence from the owner of the information to the public cloud to expose them to threats to security and privacy. That is why access control and data protection remain the key issue in cloud computing. Focus includes the challenges for ABAC implementation in Cloud infrastructure as a service.

High transmissibility and lack of effective vaccine have made the control of disease spread a challenge, and eventually, they take the form of a pandemic. The uncontrollable spread of virus creates challenges not only for the social or health front but also on the global economy. This chapter discusses the social, health and economic difficulties due to pandemic. The claims are validated with reference to the present-day epidemic the COVID-19. Technology promises to make life easier. The role of technology in government coordinated efforts to the response and preparedness is discussed in the chapter. Forecasting the spread pattern helps in readiness to handle the severity. Undoubtedly the technological advancements have led several changes to the society leading to a comfortable lifestyle even during the challenging times. The discussion here outlines the role of these advancements during the time of the pandemic. As digital technology intervenes to control the disastrous effect on humankind, it simultaneously introduces several challenges that are highlighted in the chapter. © 2021 Scrivener Publishing LLC.

When it comes to providing security to information systems, encryption emerges as an indispensable tool, as it has been used extensively in the past few decades for securing stationary data as well as data in motion.With the rapid data transmission techniques and multimedia options available for data representation, the field of information security has become very significant.The state-of-art cryptographic technique is DNA encryption, which uses biological principles for safeguarding data.The use of Bio-inspired ciphers is becoming the de-facto safety standard, especially for digital images as they are a key source of extracting crucial information.Hence, image encoding becomes of ultimate importance when there is a need to send them via an insecure communication channel.The purpose of this research paper is to present a DNA-inspired cryptosystem that can be employed in the domain of image encryption that provides superior security with enhanced efficiency.The experimental outcomes prove that this novel cryptographic algorithm not only provides better security but also at a reasonable pace.

Cryptography is the elementary tool nowadays where information and data security are the keys to success either for the organization or its rivals. Cryptography has undergone myriad changes in the last few decades that include Asymmetric encryption, Elliptic Curve Cryptography, and Quantum Cryptography to name a few. A contemporary crypto approach that arrived at the horizon and has revolutionized the security sphere is DNA cryptography. DNA cryptography surfaced in the mid 90s and still under a lot of research that is bound to give robust and secure ways for confidential data. This paper details one such fine work that defines how to safeguard a digital image using DNA encryption while sending it through an insecure channel. This paper also discusses the various types of possible attacks and how the proposed cipher will counter these. The analysis of the proposed work demonstrates that it is not only stronger but also faster than the existing standards.

The concept is based on interpreting positive or negative sentiments expressed by human beings where the subject of climate change or global warming is concerned. The sentiment analysis was done using machine learning and as well been done using deep learning, with python. An existing dataset on climate change is used. After cleaning and processing the data, a dataset having the comments classified into positive and negative sentiment remains. This paper proposes to train the algorithm to interpret positive or negative sentiments expressed by human beings where the subject of climate change or global warming is concerned and thus using the multinomial Naïve Bayes algorithm and consecutively the long short-term memory algorithm to be able to classify the intentions given a new piece of data once the model is trained, tested and validated.

One of the most significant challenges that have compromised cloud figuring and caused its moderate appropriation is security. Since clouds have various gatherings of users with various arrangements of security requirements, confining the users' accesses and shielding data from unapproved accesses have become the most troublesome assignments. To address these critical challenges, this paper initially formalizes Attribute-Based Access Control (ABAC) and proposes another access control model, called Attribute-Rule ABAC (AR-ABAC), for cloud processing to meet critical access control requirements in clouds. Our model backings the attribute decides that manage the association among users and objects, just as the capability for accessing objects based on their affectability levels. The attribute decides to indicate an understanding that figures out what sort of attributes ought to be utilized and the number of attributes considered for settling on access choices. Likewise, our model guarantees secure asset sharing among potential un confided in tenants and supports distinctive access permissions to a similar client at a similar meeting.

When it comes to providing security to information systems, encryption emerges as an indispensable tool, as it has been used intensively in past few decades for securing stationary data as well as data in motion. Earlier, the security of an encryption algorithm lied in the manipulation of characters among a word or group of words, which is called the classical age of cryptography. This age ended when indigenous mathematical equations came into play and modern ciphers like DES and RSA were designed to mark the modern cryptographic era. This period witnessed two world wars and rise of machines instead of manual calculation for data secrecy. But, the time kept moving on and new advances in the information security field surfaced like Elliptical Curve Cryptography and Quantum Cryptography which added new dimensions to the secret world of confidential communication over unsecure channels. The latest addition in this count is the DNA cryptography which has combined the laws of biology with computing to form unbreakable ciphers at least theoretically. This paper introduces a new DNA cipher which is bound to provide more robust ways to safeguard vital data.

Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data. They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This paper will introduce the core components of GANs. This will take you through how every part function and the significant ideas and innovation behind GANs. It will likewise give a short outline of the advantages and downsides of utilizing GANs, comparison of architectures of various GANs and knowledge into certain true applications.