Unifying aspect-based sentiment analysis BERT and multi-layered graph convolutional networks for comprehensive sentiment dissection.

Aspect-Based Sentiment Analysis (ABSA) represents a fine-grained approach to sentiment analysis, aiming to pinpoint and evaluate sentiments associated with specific aspects within a text. ABSA encompasses a set of sub-tasks that together facilitate a detailed understanding of the multifaceted sentiment expressions. These tasks include aspect and opinion terms extraction (ATE and OTE), classification of sentiment at the aspect level (ALSC), the coupling of aspect and opinion terms extraction (AOE and AOPE), and the challenging integration of these elements into sentiment triplets (ASTE). Our research introduces a comprehensive framework capable of addressing the entire gamut of ABSA sub-tasks. This framework leverages the contextual strengths of BERT for nuanced language comprehension and employs a biaffine attention mechanism for the precise delineation of word relationships. To address the relational complexity inherent in ABSA, we incorporate a Multi-Layered Enhanced Graph Convolutional Network (MLEGCN) that utilizes advanced linguistic features to refine the model's interpretive capabilities. We also introduce a systematic refinement approach within MLEGCN to enhance word-pair representations, which leverages the implicit outcomes of aspect and opinion extractions to ascertain the compatibility of word pairs. We conduct extensive experiments on benchmark datasets, where our model significantly outperforms existing approaches. Our contributions establish a new paradigm for sentiment analysis, offering a robust tool for the nuanced extraction of sentiment information across diverse text corpora. This work is anticipated to have significant implications for the advancement of sentiment analysis technology, providing deeper insights into consumer preferences and opinions for a wide range of applications.

Data analytics and modelling in context to determination of moisture susceptibility of reclaimed asphalt foamed bituminous mix.

The presence of water badly affects the moisture susceptibility of the reclaimed asphalt Foamed Bituminous Mix (FBM). The present study is mainly emphasized to assess the moisture susceptibility of reclaimed asphalt FBM, Where RAP is being incorporated as a replacement of fresh aggregates. Moisture susceptibility of the mix is evaluated in terms of tensile strength ratio (TSR) and resilient modulus ratio, subjected to different conditioning procedures namely AASHTO T283, modified IDOT, TG-2 guidelines, and MIST. Further data analytics and regression modeling are also carried out to determine the moisture susceptibility of the mix and to check the statistics among the variables. The findings show that the incorporation of RAP in the FBM improves moisture resistance. Further, FBM containing 100% RAP shows the least moisture susceptibility in terms of TSR and Mr ratio irrespective of any conditioning type. Moreover, MIST conditioning may be preferred to assess the moisture sensitivity as it simulates the field pore pressure effects. Further, mathematical analysis is carried out to predict the moisture susceptibility of mix. Adjusted R square coefficient indicates a better fit of the prediction model developed. Overall, the study may be helpful to highway professionals in analyzing the conditioning procedures and determining the moisture sensitivity of the reclaimed asphalt Foamed Bituminous Mix.

Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images.

Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagnosis of TB prevents further transmission and increases the survival rate of the affected person. One of the standard diagnosis methods is the sputum culture test. Diagnosing and rapid sputum test results usually take one to eight weeks in 24 h. Using posterior-anterior chest radiographs (CXR) facilitates a rapid and more cost-effective early diagnosis of tuberculosis. Due to intraclass variations and interclass similarities in the images, TB prognosis from CXR is difficult. We proposed an early TB diagnosis system (tbXpert) based on deep learning methods. Deep Fused Linear Triangulation (FLT) is considered for CXR images to reconcile intraclass variation and interclass similarities. To improve the robustness of the prognosis approach, deep information must be obtained from the minimal radiation and uneven quality CXR images. The advanced FLT method accurately visualizes the infected region in the CXR without segmentation. Deep fused images are trained by the Deep learning network (DLN) with residual connections. The largest standard database, comprised of 3500 TB CXR images and 3500 normal CXR images, is utilized for training and validating the recommended model. Specificity, sensitivity, Accuracy, and AUC are estimated to determine the performance of the proposed systems. The proposed system demonstrates a maximum testing accuracy of 99.2%, a sensitivity of 98.9%, a specificity of 99.6%, a precision of 99.6%, and an AUC of 99.4%, all of which are pretty high when compared to current state-of-the-art deep learning approaches for the prognosis of tuberculosis. To lessen the radiologist's time, effort, and reliance on the level of competence of the specialist, the suggested system named tbXpert can be deployed as a computer-aided diagnosis technique for tuberculosis.

Developing an SDN security model (EnsureS) based on lightweight service path validation with batch hashing and tag verification.

Software-defined networking (SDN) has significantly transformed the field of network management through the consolidation of control and provision of enhanced adaptability. However, this paradigm shift has concurrently presented novel security concerns. The preservation of service path integrity holds significant importance within SDN environments due to the potential for malevolent entities to exploit network flows, resulting in a range of security breaches. This research paper introduces a model called "EnsureS", which aims to enhance the security of SDN by proposing an efficient and secure service path validation approach. The proposed approach utilizes a Lightweight Service Path Validation using Batch Hashing and Tag Verification, focusing on improving service path validation's efficiency and security in SDN environments. The proposed EnsureS system utilizes two primary techniques in order to validate service pathways efficiently. Firstly, the method utilizes batch hashing in order to minimize computational overhead. The proposed EnsureS algorithm enhances performance by aggregating packets through batches rather than independently; the hashing process takes place on each one in the service pathway. Additionally, the implementation of tag verification enables network devices to efficiently verify the authenticity of packets by leveraging pre-established trust relationships. EnsureS provides a streamlined and effective approach for validating service paths in SDN environments by integrating these methodologies. In order to assess the efficacy of the Proposed EnsureS, a comprehensive series of investigations were conducted within a simulated SDN circumstance. The efficacy of Proposed EnsureS was then compared to that of established methods. The findings of our study indicate that the proposed EnsureS solution effectively minimizes computational overhead without compromising on the established security standards. The implementation successfully reduces the impact of different types of attacks, such as route alteration and packet spoofing, increasing SDN networks' general integrity.

Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease.

The patients' vocal Parkinson's disease (PD) changes could be identified early on, allowing for management before physically incapacitating symptoms appear. In this work, static as well as dynamic speech characteristics that are relevant to PD identification are examined. Speech changes or communication issues are among the challenges that Parkinson's individuals may encounter. As a result, avoiding the potential consequences of speech difficulties brought on by the condition depends on getting the appropriate diagnosis early. PD patients' speech signals change significantly from those of healthy individuals. This research presents a hybrid model utilizing improved speech signals with dynamic feature breakdown using CNN and LSTM. The proposed hybrid model employs a new, pre-trained CNN with LSTM to recognize PD in linguistic features utilizing Mel-spectrograms derived from normalized voice signal and dynamic mode decomposition. The proposed Hybrid model works in various phases, which include Noise removal, extraction of Mel-spectrograms, feature extraction using pre-trained CNN model ResNet-50, and the final stage is applied for classification. An experimental analysis was performed using the PC-GITA disease dataset. The proposed hybrid model is compared with traditional NN and well-known machine learning-based CART and SVM & XGBoost models. The accuracy level achieved in Neural Network, CART, SVM, and XGBoost models is 72.69%, 84.21%, 73.51%, and 90.81%. The results show that under these four machine approaches of tenfold cross-validation and dataset splitting without samples overlapping one individual, the proposed hybrid model achieves an accuracy of 93.51%, significantly outperforming traditional ML models utilizing static features in detecting Parkinson's disease.

Advancement in Biopolymer Assisted Cancer Theranostics.

Applications of nanotechnology have increased the importance of research and nanocarriers, which have revolutionized the method of drug delivery to treat several diseases, including cancer, in the past few years. Cancer, one of the world's fatal diseases, has drawn scientists' attention for its multidrug resistance to various chemotherapeutic drugs. To minimize the side effects of chemotherapeutic agents on healthy cells and to develop technological advancement in drug delivery systems, scientists have developed an alternative approach to delivering chemotherapeutic drugs at the targeted site by integrating it inside the nanocarriers like synthetic polymers, nanotubes, micelles, dendrimers, magnetic nanoparticles, quantum dots (QDs), lipid nanoparticles, nano-biopolymeric substances, etc., which has shown promising results in both preclinical and clinical trials of cancer management. Besides that, nanocarriers, especially biopolymeric nanoparticles, have received much attention from researchers due to their cost-effectiveness, biodegradability, treatment efficacy, and ability to target drug delivery by crossing the blood-brain barrier. This review emphasizes the fabrication processes, the therapeutic and theragnostic applications, and the importance of different biopolymeric nanocarriers in targeting cancer both in vitro and in vivo, which conclude with the challenges and opportunities of future exploration using biopolymeric nanocarriers in onco-therapy with improved availability and reduced toxicity.

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing.

Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.

Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization.

Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer's disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer's disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.

A novel artificial intelligence-based predictive analytics technique to detect skin cancer.

One of the leading causes of death among people around the world is skin cancer. It is critical to identify and classify skin cancer early to assist patients in taking the right course of action. Additionally, melanoma, one of the main skin cancer illnesses, is curable when detected and treated at an early stage. More than 75% of fatalities worldwide are related to skin cancer. A novel Artificial Golden Eagle-based Random Forest (AGEbRF) is created in this study to predict skin cancer cells at an early stage. Dermoscopic images are used in this instance as the dataset for the system's training. Additionally, the dermoscopic image information is processed using the established AGEbRF function to identify and segment the skin cancer-affected area. Additionally, this approach is simulated using a Python program, and the current research's parameters are assessed against those of earlier studies. The results demonstrate that, compared to other models, the new research model produces better accuracy for predicting skin cancer by segmentation.

Exploring Pharmacological Mechanisms of Essential Oils on the Central Nervous System.

Essential oils (EOs) have been traditionally used as ancient remedies to treat many health disorders due to their enormous biological activities. As mainstream allopathic medication currently used for CNS disorders is associated with adverse effects, the search to obtain safer alternatives as compared to the currently marketed therapies is of tremendous significance. Research conducted suggests that concurrent utilization of allopathic medicines and EOs is synergistically beneficial. Due to their inability to show untoward effects, various scientists have tried to elucidate the pharmacological mechanisms by which these oils exert beneficial effects on the CNS. In this regard, our review aims to improve the understanding of EOs' biological activity on the CNS and to highlight the significance of the utilization of EOs in neuronal disorders, thereby improving patient acceptability of EOs as therapeutic agents. Through data compilation from library searches and electronic databases such as PubMed, Google Scholar, etc., recent preclinical and clinical data, routes of administration, and the required or maximal dosage for the observation of beneficial effects are addressed. We have also highlighted the challenges that require attention for further improving patient compliance, research gaps, and the development of EO-based nanomedicine for targeted therapy and pharmacotherapy.

Phytochemical investigations and evaluation of antimutagenic activity of the alcoholic extract of Glycosmis pentaphylla and Tabernaemontana coronaria by Ames test.

Chemical investigation of root bark of Glycosmis pentaphylla and stem bark of Tabernaemontana coronaria led to the isolation of three carbazole alkaloids glycozoline, glycozolidine and methyl carbazole 3-carboxylate, two furoquinoline alkaloids skimmianine and dictamine, an acridone alkaloid arborinine, three monomeric indole alkaloids coronaridine, 10-methoxy coronaridine and tabernaemontanine, and two dimeric indole alkaloids voacamine and tabernaelegantine B. Their structures were established by detailed spectral analysis. Mutagenic and antimutagenic potential of methanol extract of both plant materials were evaluated by Ames test against known positive mutagens 2-aminofluorine, 4-nitro-O-phenylenediamine and sodium azide using Salmonella typhimurium TA 98 and TA 100 bacterial strains both in the presence and absence of S9. Both the extracts were non-mutagenic in nature. Both the extracts of G. pentaphylla and T. coronaria exhibited significant antimutagenic activity against NPD and sodium azide for S. typhimurium TA98 and TA100 strains. The results indicated that the extracts could counteract the mutagenicity induced by different genotoxic compounds.

A new bisbenzylisoquinoline alkaloid isolated from Thalictrum foliolosum, as a potent inhibitor of DNA topoisomerase IB of Leishmania donovani.

Chemical investigation of the stem of Thalictrum foliolosum resulted in the isolation of two new bisbenzylisoquinoline alkaloids (1 and 2) along with known protoberberine group of isoquinoline alkaloids thalifendine (3) and berberine (4). The structures of the new compounds were established by detailed 2D NMR spectral analysis with their configurations determined from their optical rotation values and confirmed using circular dichroism. Inhibitory activities of these four compounds against DNA topoisomerase IB of Leishmania donovani were evaluated. Compound 2 exhibited almost complete inhibition of the enzyme activity at 50 µM concentration and it was found to be effective in killing both wild type as well as SAG resistant promastigotes of the parasite.

Anthocephaline, a new indole alkaloid and cadambine, a potent inhibitor of DNA topoisomerase IB of Leishmania donovani (LdTOP1LS), isolated from Anthocephalus cadamba.

Chemical investigation of the stem bark of Anthocephalus cadamba has resulted in the isolation of anthocephaline (1), a new indole alkaloid, along with strictosamide (2), vincosamide (3) and cadambine (4). The structures of the isolated alkaloids (1-4) were established by detailed 2D NMR spectral analysis. Cadambine (4) exhibited potent DNA topoisomerase IB inhibitory activity.

A new ellagic acid glycoside and DNA topoisomerase IB inhibitory activity of saponins from Putranjiva roxburghii.

Chemical investigation of the stem bark and leaves of Putranjiva roxburghii has resulted in the isolation of a new ellagic acid glycoside (5) along with four saponins (1-4). The structures of the isolated compounds were established by detailed spectral analysis. Incidentally putranoside-A methyl ester (4) has been isolated for the first time from this species and the saponins (1-4) exhibited potent DNA topoisomerase IB inhibitory activity.

Withaferin A modulates the Spindle assembly checkpoint by degradation of Mad2-Cdc20 complex in colorectal cancer cell lines.

Withania somnifera L. Dunal (Ashwagandha) is used over centuries in the ayurvedic medicines in India. Withaferin A, a withanolide, is the major compound present in leaf extract of the plant which shows anticancer activity against leukemia, breast cancer and colorectal cancer. It arrests the ovarian cancer cells in the G2/M phase in dose dependent manner. In the current study we show the effect of Withaferin A on cell cycle regulation of colorectal cancer cell lines HCT116 and SW480 and its effect on cell fate. Treatment of these cells with this compound leads to apoptosis in a dose dependent manner. It causes the G2/M arrest in both the cell lines. We show that Withaferin A (WA) causes mitotic delay by blocking Spindle assembly checkpoint (SAC) function. Apoptosis induced by Withaferin A is associated with proteasomal degradation of Mad2 and Cdc20, an important constituent of the Spindle Checkpoint Complex. Further overexpression of Mad2 partially rescues the deleterious effect of WA by restoring proper anaphase initiation and keeping more number of cells viable. We hypothesize that Withaferin A kills cancer cells by delaying the mitotic exit followed by inducing chromosome instability.