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Due to the increasing number of reviews, it has become more important for businesses to analyze their customer's sentiments. This paper presents a framework that uses pre-trained language models such as BERT, XLNet, and Electra to analyze these sentiments. The framework is based on the Sentiment140 dataset which contains over 1.6 million tweets with tags. This collection of sentiments allows us to perform an evaluation of the models' performance. The goal of this paper is to analyze the effectiveness of these models in categorizing and understanding the sentiments in customer reviews. BERT, for instance, has demonstrated exceptional performance in various tasks related to natural language processing. Another model that is transformer-based is XLNet, which adds more capabilities by utilizing permutation-based learning. On the other hand, the new generation of model, known as Electra, focuses on the generator discriminator learning. Through the incorporation of these models, we can leverage the contextual understanding of the sentiments in the customer reviews. In this paper, we thoroughly examine the performance of the different models in the framework for sentiment analysis. We tested their precision, recall, F1-score, and accuracy in identifying and categorizing the sentiments in customer reviews. We also discuss the impact of adjusting the models on the task, as well as the tradeoffs between performance gains and computational resources. The findings of the study provided valuable information on the utilization of pre-trained models for analyzing customer reviews. We analyzed the performance of the different models BERT, XLNet, Electra, and BERT, revealing their weaknesses and strengths. This helps businesses identify the best model for their sentiment analysis needs. The study's findings have contributed to the advancement of sentiment analysis and natural language processing. It offers valuable recommendations that will aid in the future research efforts.

In the current era, to upgrade the facilities and features of UAVs, the implementation of IoT and AI is mandatory. It helps the drone to provide accurate data after analysing a particular situation. Moreover, it also helps to access the drone from any device with the help of an android app. The application of AI and IoT has enhanced the popularity of drones worldwide. This study has analysed the application of IoT and AI in UAVs to make them more efficient. This research has evaluated IoT and AI's positive and negative impacts on UAVs. Moreover, it has determined solutions to mitigate them effectively.

The 3-D seismic dataset is a key tool to analyze and understand the mechanism of structural and stratigraphic hydrocarbon (HC) trapping in the subsurface. Conventionally used subsurface seismic characterization methods for fractures are based on the theory of effective anisotropy medium. The aim of this work is to improve the structural images with dense sampling of 3-D survey to evaluate structural and stratigraphic models for reservoir development to predict reservoir quality. The present study of the Gullfaks Field, located in the Norwegian North Sea Gullfaks sector, identifies the shallowest structural elements. The steepness of westward structural dip decreases eastward during the Upper Jurassic to Lower Cretaceous deposition. Reservoir sands consist of the Middle Jurassic deltaic deposits and Lower Jurassic fluvial channel and delta plain deposits. Sediment supply steadily prevails on sea-level rise and the succession displays a regressive trend indicated by a good continuous stacking pattern. The key factor for the development of reservoirs in the Gullfaks Field is fault transmissibility with spatially distributed pressure. The majority of mapped faults with sand-to-sand contacts are non-sealing, which provide restriction for the HC flow between the fault blocks. The traps for HC accumulation occur between the post-rift and syn-rift strata, i.e. antiform set by extensional system, unconformity trap at the top of syn-deposition, and structural trap due to normal faults. Overall reservoir quality in the studied area is generally excellent with average 35% porosity and permeability in the Darcy range. Our findings are useful to better understand the development of siliciclastic reservoirs in similar geological settings worldwide.

Purpose Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis. Design/methodology/approach Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers. Findings Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails. Originality/value It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

A substantial proportion of proven oil and gas reserves of the world is contained in the carbonate reservoir. It is estimated that about 60% of the world’s oil and 40% of gas reserves are confined in carbonate reservoirs. Exploration and development of hydrocarbons in carbonate reservoirs are much more challenging due to poor seismic imaging and reservoir heterogeneity caused by diagenetic changes. Evaluation of carbonate reservoirs has been a high priority for researchers and geoscientists working in the petroleum industry mainly due to the challenges presented by these highly heterogeneous reservoir rocks. It is essential for geoscientists, petrophysicists, and engineers to work together from initial phases of exploration and delineation of the pool through mature stages of production, to extract as much information as possible to produce maximum hydrocarbons from the field for the commercial viability of the project. In the absence of the well-log data, the properties are inferred from the inversion of seismic data alone. In oil and gas exploration and production industries, seismic inversion is proven as a tool for tracing the subsurface reservoir facies and their fluid contents. In this paper, seismic inversion demonstrates the understanding of lithology and includes the full band of frequency in our initial model to incorporate the detailed study about the basin for prospect evaluation. 3D seismic data along with the geological & petrophysical information and electrologs acquired from drilled wells are used for interpretation and inversion of seismic data to understand the reservoir geometry and facies variation including the distribution of intervening tight layers within the Miocene carbonate reservoir in the study area of Central Luconia. The out-come of the seismic post-stack inversion technique shows a better subsurface lithofacies and fluid distribution for delineation and detailed study of the reservoir.

Bawang Member (Unit 4) of Late Eocene age is the fifth and the youngest member of Belaga Formation. In general, the Late Cretaceous to Late Eocene turbidite sequence of Rajang Group in the lower Rajang area (Central Sarawak) is characterized by Belaga Formation. This member is unique and is only exposed in the vicinity of the Tatau area (Arip valley and Tatau High region) with limited exposures. In this research we present new evidence, to support its position on the stratigraphic column, based on field observations (stratigraphic and sedimentological characteristics) and lab analysis (TOC). Numerous recently exposed sections were visited in the study area. Four litho-facies were identified in the Bawang Member based on sedimentological observations; (1) Massive sandstone facies (SF1), (2) Medium to thick-bedded sandstone facies (SF2), (3) Thin bedded heterolithic facies (SF3), and (4) Mudstone or Shale facies (SF4). Reddish maroon and green shales exposed in Tatau High region are only exposed in another member of Belaga Formation i.e. Kapit Member. The Bawang Member sediments differ from the Tatau High region sediments in absence of cleavage and internal sedimentary features (rip-up clasts), the occurrence of bioturbation, and with lesser exposures of mud facies. On the contrary, the Tatau High sediments show higher ratios of mud facies, presence of cleavage in shales and even in few sandstones, and absence of major bioturbation of strata. The presence of internal sedimentary features (rip-up clasts) in Tatau High and in Kapit Member supports the idea that these are the parts of the same rock unit. Furthermore, contrasting TOC values between these two units (Bawang and Tatau High), and the similarity of TOC among Tatau High and Kapit Member are also in support of the same idea. Based on the differences of facies (rock) types, the effect of cleavage, absence of rip-up clasts, the occurrence of bioturbations and different TOC values, we confirm that the Tatau High rocks must be older than most of Bawang Member exposures and are actually the faulted blocks of Kapit Member.

The advent of healthcare information management systems &#x0028 HIMSs &#x0029 continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale. Analysis of this big data allows for boundless potential outcomes for discovering knowledge. Big data analytics &#x0028 BDA &#x0029 in healthcare can, for instance, help determine causes of diseases, generate effective diagnoses, enhance QoS guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments, generate accurate predictions of readmissions, enhance clinical care, and pinpoint opportunities for cost savings. However, BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners. In this paper, we present a comprehensive roadmap to derive insights from BDA in the healthcare &#x0028 patient care &#x0029 domain, based on the results of a systematic literature review. We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on NoSQL databases. We also identify the limitations and challenges of these applications and justify the potential of NoSQL databases to address these challenges and further enhance BDA healthcare research. We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm. We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare. Finally, we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work. The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators, practitioners and professionals to successfully implement BDA initiatives in their organizations.

Offshore petroleum systems are often very complex and subtle because of a variety of depositional environments. Characterizing a reservoir based on conventional seismic and well-log stratigraphic analysis in intricate settings often leads to uncertainties. Drilling risks, as well as associated subsurface uncertainties can be minimized by accurate reservoir delineation. Moreover, a forecast can also be made about production and performance of a reservoir. This study is aimed to design a workflow in reservoir characterization by integrating seismic inversion, petrophysics and rock physics tools. Firstly, to define litho facies, rock physics modeling was carried out through well log analysis separately for each facies. Next, the available subsurface information is incorporated in a Bayesian engine which outputs several simulations of elastic reservoir properties, as well as their probabilities that were used for post-inversion analysis. Vast areal coverage of seismic and sparse vertical well log data was integrated by geostatistical inversion to produce acoustic impedance realizations of high-resolution. Porosity models were built later using the 3D impedance model. Lastly, reservoir bodies were identified and cross plot analysis discriminated the lithology and fluid within the bodies successfully.

Advances in Sedimentology and Coastal Marine Geology is a Special Volume of Journal of Marine Science and Engineering (MDPI), in which several geologic and sedimentological studies have been collected.

User Equipment (UE) is equipped with limited cache resources that can be utilized to offload data traffic through device-to-device (D2D) communications. Data caching at a UE level has the potential to significantly alleviate data traffic burden from the backhaul link. Moreover, in wireless networks, users exhibit mobility that poses serious challenges to successful data transmission via D2D communications due to intermittent connectivity among users. Users’ mobility can be exploited to efficiently cache contents by observing connectivity patterns among users. Therefore, it is crucial to develop an efficient data caching mechanism for UE while taking into account users’ mobility patterns. In this work, we propose a mobility-aware data caching approach to enhance data offloading via D2D communication. First, we model users’ connectivity patterns. Then, contents are cached in UE’ cache resources based on users’ data preferences. In addition, we also take into account signal-to-interference and noise ratio (SINR) requirements of the users. Hence, our proposed caching mechanism exploits connectivity patterns of users to perform data placement based on users’ own demands and neighboring users to enhance data offloading via cache resources. We performed extensive simulations to investigate the performance of our proposed mobility-aware data caching mechanism. The performance of our proposed caching mechanism is compared to most deployed data caching mechanisms, while taking into account the dynamic nature of the wireless channel and the interference experienced by the users. From the obtained results, it is evident that our proposed approach achieves 14%, 16%, and 11% higher data offloading gain than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of users, cache capacity, and number of contents, respectively. Moreover, we also analyzed cache hit rates, and our proposed scheme achieves 8% and 5% higher cache hit rate than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of contents and cache capacity, respectively. Hence, our proposed caching mechanism brings significant improvement in data sharing via D2D communications.

3D-seismic data have increasingly shifted seismic interpretation work from a horizons-based to a volume-based focus over the past decade. The size of the identification and mapping work has therefore become difficult and requires faster and better tools. Faults, for instance, are one of the most significant features of subsurface geology interpreted from seismic data. Detailed fault interpretation is very important in reservoir characterization and modeling. The conventional manual fault picking is a time-consuming and inefficient process. It becomes more challenging and error-prone when dealing with poor quality seismic data under gas chimneys. Several seismic attributes are available for faults and discontinuity detection and are applied with varying degrees of success. We present a hybrid workflow that combines a semblance-based fault likelihood attribute with a conventional ant-tracking attribute. This innovative workflow generates optimized discontinuity volumes for fault detection and automatic extraction. The data optimization and conditioning processes are applied to suppress random and coherent noise first, and then a combination of seismic attributes is generated and co-rendered to enhance the discontinuities. The result is the volume with razor sharp discontinuities which are tracked and extracted automatically. Contrary to several available fault tracking techniques that use local seismic continuity like coherency attributes, our hybrid method is based on directed semblance, which incorporates aspects of Dave Hale’s superior fault-oriented semblance algorithm. The methodology is applied on a complex faulted reservoir interval under gas chimneys in a Malaysian basin, yet the results were promising. Despite the poor data quality, the methodology led to detailed discontinuity information with several major and minor faults extracted automatically. This hybrid approach not only improved the fault tracking accuracy but also significantly reduced the fault interpretation time and associated uncertainty. It is equally helpful in detecting any seismic objects like fracture, chimneys, and stratigraphic features.

Evolution of technology has completely revolutionaries’ day to day life of common man. Technology has penetrated in our life like anything. These days everybody is using technology for their benefit’s and marketers are no an exception to it. They are using technology to reach to the customers. Days are far gone when people used to line up in stores to purchase the general product. These days, more and more individuals lean toward online shopping, which is presently a pattern of style and fashion. Nagpur, the center city of the country and world-famous for its oranges is advancing towards computerized explosion that makes high significance on the assessment of the present acknowledgment level of online shopping by the youngsters. In this way, understanding the by and a large state of customer's attitude towards web-based shopping is significant for the Nagpurians. In this study, 143 respondents took part in the survey. Respondents were selected through simple random technique. Data was analyzed using SPSS Version 22. This study found that online shopping is very common in this young generation of Nagpur. Major reason for Nagpurains to do online shopping is that it saves a lot of time. However, consumer those who do not shop online is only because of online fraud, lack of personal touch and no return policy. Nagpur consumers do prescribe online shopping as an elective path for shopping.

Today’s generation is of Networking. It is amazing to look how Internet is changing the lives of people. Social Networking is an international trend, and has spread its wings and has reached to that limit where we can find anyone from any part of the world and gain ample knowledge relating to anything. Social Media primarily originated to be connected with family and friends, but later it was adopted by business as well, as it wanted to take advantage of new and popular method of reaching to customer. On the same lines, students adopted Social Media to be in touch with colleagues and teachers as there is faster flowing of information and knowledge. The intensity of Social Media lies in its capacity to share information and connect with everyone simultaneously with everyone on the earth. The present investigation attempts to go around the impact of Social Media and understudies execution. For data collection, survey method was used with the help of structured questionnaire. The sample size was 63. Students from different year were used as respondents. SPSS-22 was used for analyzing the data. The study concluded with the study of positive and negative influence of Social Media. The positive effect includes easy passage of Information, completion of homework & assignments and finally it encourage the academic exercise and facilitate with others. The negative effect includes that the students get dependent on it, their performance has decreased with the more use of Social Media and finally it is a distracting element.

Сучасне покоління – це покоління соціальних мереж. Цікаво спостерігати, як Інтернет змінює життя людей. Соціальні мережі – це міжнародний тренд, який поширюється по всьому світу та досяг того рівня, коли ми можемо стверджувати, що немає такої людини у будь-якій точці світу, яка б не отримувала інформацію з Інтернету. Соціальні мережі, перш за все, були створені з метою підтримки зв’язку між сім’ями та друзями, проте пізніше вони почали використовуватися і в бізнес технологіях і сьогодні представляють собою новий популярний метод охвата клієнтів. Аналогічно студенти використовують соціальні мережі для того, щоб підтримувати зв’язок з колегами та викладачами, оскільки завдяки ним прискорюється потік інформації та знань. Інтенсивність використання соціальних мереж пов’язана з їхньою можливістю швидкого обміну інформацією та одночасним зв’язком із будь-ким у будь-якій точці світу. Це дослідження направлено на вивчення впливу соціальних мереж на академічну успішність студентів. В рамках дослідження було використано метод збору інформації за допомогою структурованого опитувальника. Вибірка складалася з 63 студентів різних років навчання. Програма SPSS-22 була використана для аналізу даних. В рамках дослідження було визначено як позитивний, так і негативний вплив соціальних мереж. Позитивний ефект визначається швидким доступом до інформації та виконанням домашніх завдань, а також залученням до академічного заняття та сприяє співробітництву один з одним. Негативний ефект визначається тим, що у студентів виникає залежність від соціальних мереж: їхня успішність знижується, якщо підвищується ступінь використання соціальних мереж, і, в кінці-кінців, вони можуть бути деструктивними., Современное поколение – это поколение социальных сетей. Удивительно наблюдать, как Интернет меняет жизнь людей. Социальные сети – это международный тренд, который распространяется по всему миру и достиг того уровня, когда мы можем утверждать, что нет такого человека в любой точке мира, который бы не черпал информацию из Интернета. Социальные сети, прежде всего, зародились с целью поддержания связи между семьей и друзьями, однако позднее они начал применяться и в бизнес технологиях и сегодня представляют новый популярный метод охвата клиентов. Аналогично студенты применяют социальные сети для того, чтобы поддерживать связь с коллегами и преподавателями, поскольку благодаря ним ускоряется поток информации и знаний. Интенсивность использования социальных сетей связана с их возможностью быстрого обмена информацией и одновременной связи с кем-либо в любой точке Земли. Данное исследование направлено на изучение влияния социальных сетей на академическую успеваемость студентов. В рамках исследования использовался метод сбора информации с помощью структурированного опросника. Выборка состояла из 63 студентов различных годов обучения. Программа SPSS-22 была использована для анализа данных. В рамках исследования было определено как положительное, так и отрицательное влияние социальных сетей. Положительный эффект определяется быстрым доступом к информации и выполнением домашних заданий, а также вовлечением в академическое занятие и способствует сотрудничеству друг с другом. Отрицательный эффект заключается в том, что у студентов возникает зависимость от социальных сетей: их успеваемость снижается, если увеличивается степень использования социальных сетей, и, в конечном счете, они могут быть деструктивны., Today’s generation is of Networking. It is amazing to look how Internet is changing the lives of people. Social Networking is an international trend, and has spread its wings and has reached to that limit where we can find anyone from any part of the world and gain ample knowledge relating to anything. Social Media primarily originated to be connected with family and friends, but later it was adopted by business as well, as it wanted to take advantage of new and popular method of reaching to customer. On the same lines, students adopted Social Media to be in touch with colleagues and teachers as there is faster flowing of information and knowledge. The intensity of Social Media lies in its capacity to share information and connect with everyone simultaneously with everyone on the earth. The present investigation attempts to go around the impact of Social Media and understudies execution. For data collection, survey method was used with the help of structured questionnaire. The sample size was 63. Students from different year were used as respondents. SPSS-22 was used for analyzing the data. The study concluded with the study of positive and negative influence of Social Media. The positive effect includes easy passage of Information, completion of homework & assignments and finally it encourage the academic exercise and facilitate with others. The negative effect includes that the students get dependent on it, their performance has decreased with the more use of Social Media and finally it is a distracting element.

The researchers carry this project to know the negative effect of Social Media on the students’ academic performance. Survey method was used to collect the primary data. Sample size was 197 respondents. The respondents are students of Erbil City (Iraq). Data was analyzed using Correlation of Variables, ANOVA and Regression with the help of SPSS software. The study revealed that Time Waste, Cause of Distraction and Reduced Command over Language as the most responsible factors for Social Media to affects the students’ academic performance. However, the researcher found no significance in the variable of Reduction in Real Human Contact.

Technology has made the life faster and easier. With the growth of technology, every sector has upgraded them to be competitive in the market. With the introduction of latest technology, customer satisfaction comes into picture. Banks are also using technology for satisfying the customers. The bank offers various services to their customer to satisfy them. Automated Teller Machine (ATM) services if one of them. Customer satisfaction is the prime objective of Banks. This research work is aspiring to examine the ramification of Automated Teller Machine (ATM) service qualities on overall customer satisfaction. Survey method was used to collect data. Information from 211 ATM users was collected from different places of Kurdistan region using a structured questionnaire. A pilot survey was conducted to ensure all dimensions meets to its objectives of research. SPSS-22 was used for analyzing the data. To recognize the service quality components of ATM and their association with overall customer satisfaction descriptive analysis, correlation and coefficient and regression test and ANOVA test were used. Results indicates that majority of dimensions are significantly correlated with overall customer satisfaction. At the end, few recommendations were made for improvement of ATM service quality.

This study focuses on the sequence stratigraphy and the dominated seismic facies in the Central Taranaki basin. Four regional seismic sequences namely SEQ4 to SEQ1 from bottom to top and four boundaries representing unconformities namely H4 to H1 from bottom to top have been traced based on the reflection terminations. This was validated using well logs information. An onlapping feature on the seismic section indicates a new perspective surface separated between the upper and lower Giant formation, which indicates a period of seawater encroachment. This study focused extensively on deposition units from SEQ4 to SEQ1. The seismic facies, isochron map, and depositional environment were determined, and the system tract was established. This study was also able to propose a new perspective sequence stratigraphy framework of the basin and probable hydrocarbon accumulations and from the general geological aspect, SA-Middle Giant Formation (SEQ3) could act as potential traps.

Petroleum systems in offshore are often very complex and subtle because of variety of depositional environments. Delineating a reservoir based on conventional seismic and well-log stratigraphic analysis in such complex settings often leads to uncertainties. A reliable model of reservoir can forecast the production and performance of a reservoir which can reduce the drilling risks and associated uncertainties significantly. This work is aimed to develop a new concept in reservoir modeling by integrating seismic inversion and rock physics tools. First, in order to define litho facies, rock physics modeling was carried out through well log analysis separately for each facie. Next, the available subsurface information is incorporated in a Bayesian engine which outputs the several realizations of elastic reservoir properties and their respective probabilities, which then are used for post-inversion analysis. Seismic inversion fully exploited the vast areal coverage of the seismic data and integrated it with the well logs resulted in high-resolution acoustic impedance realizations. 3D impedance models coupled with the petrophysical analysis were then used to delineate the reservoir bodies.

Blockchain is an emerging field of study in a number of applications and domains. Especially when combine with Internet of Things (IoT) this become truly transformative, opening up new plans of action, improving engagement and revolutionizing many sectors including agriculture. IoT devices are intelligent and have high critical capabilities but low-powered and have less storage, and face many challenges when used in isolation. Maintaining the network and consuming IoT energy by means of redundant or fabricated data transfer lead to consumption of high energy and reduce the life of IoT network. Therefore, an appropriate routing scheme should be in place to ensure consistency and energy efficiency in an IoT network. This research proposes an efficient routing scheme by integrating IoT with Blockchain for distributed nodes which work in a distributed manner to use the communicating links efficiently. The proposed protocol uses smart contracts within heterogeneous IoT networks to find a route to Base Station (BS). Each node can ensure route from an IoT node to sink then base station and permits IoT devices to collaborate during transmission. The proposed routing protocol removes redundant data and blocks IoT architecture attacks and leads to lower consumption of energy and improve the life of network. The performance of this scheme is compared with our existing scheme IoT-based Agriculture and LEACH in Agriculture. Simulation results show that integrating IoT with Blockchain scheme is more efficient, uses low energy, improves throughput and enhances network lifetime.