Natural Language Processing Algorithms Research Articles

Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.

Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. Our objective was to compare the performance of unsupervised representations of sequences of disease codes generated by bag-of-words versus sequence-based NLP algorithms at predicting clinically relevant outcomes. This cohort study used primary care EHRs from 6286233 people with Multiple Long-Term Conditions in England. For each patient, an unsupervised vector representation of their time-ordered sequences of diseases was generated using 2 input strategies (212 disease categories versus 9462 diagnostic codes) and different NLP algorithms (Latent Dirichlet Allocation, doc2vec, and 2 transformer models designed for EHRs). We also developed a transformer architecture, named EHR-BERT, incorporating sociodemographic information. We compared the performance of each of these representations (without fine-tuning) as inputs into a logistic classifier to predict 1-year mortality, healthcare use, and new disease diagnosis. Patient representations generated by sequence-based algorithms performed consistently better than bag-of-words methods in predicting clinical endpoints, with the highest performance for EHR-BERT across all tasks, although the absolute improvement was small. Representations generated using disease categories perform similarly to those using diagnostic codes as inputs, suggesting models can equally manage smaller or larger vocabularies for prediction of these outcomes. Patient representations produced by sequence-based NLP algorithms from sequences of disease codes demonstrate improved predictive content for patient outcomes compared with representations generated by co-occurrence-based algorithms. This suggests transformer models may be useful for generating multi-purpose representations, even without fine-tuning.

Braille-Vox Pro Empowering Inclusive Communication

Braille-Vox Pro is an innovative project with the objective of creating a voice-to-Braille conversion system for individuals with vision impairments. The project intends to overcome the reading and writing difficulties encountered by visually impaired individuals, which can have a substantial impact on their ability to access information and communicate effectively. By turning spoken words into Braille text in real-time, the technology will give a practical answer to these issues, allowing visually impaired people to be more autonomous and productive. The research employs advanced natural language processing algorithms capable of effectively recognizing and interpreting spoken words and translating them to Braille text. Also, the system will include a Braille display device that will allow visually impaired individuals to read the transformed text. The development of this technology will give visually impaired individuals with a new degree of accessibility and inclusion, providing them more effective communication and information access. The Braille-Vox Pro project is a huge advancement in assistive technology, providing visually impaired folks with a much-needed answer to their difficulties. By allowing individuals to read and write Braille with their voice, the technology will enhance their capacity to communicate, gain access to information, and participate more fully in society. This project has the potential to aid millions of visually impaired individuals worldwide, which is a tremendous impact. The Braille-Vox Pro project is an innovative and vital Endeavor that will have a significant influence on the lives of individuals with vision impairments. [1] Keywords— Sub-images, Fuzzy logic, Image enhancement, Histogram, HSV, Image processing.

Management, Utilization, and Outcomes of Preterm Labor in an Integrated Health Care System.

Fetal fibronectin (fFN) testing and transvaginal ultrasound (TVUS) are diagnostic tools used to predict impending spontaneous preterm birth (sPTB) among women presenting with preterm labor (PTL). We evaluated the association between fFN testing or TVUS cervical length (CL) measurement in predicting sPTB, respiratory distress syndrome (RDS), neonatal intensive care unit (NICU) admission, and sPTB-related costs. We conducted a retrospective cohort study using data from the Kaiser Permanente Southern California electronic health system (January 1, 2009-December 31, 2020) using diagnostic and procedure codes, along with a natural language processing algorithm to identify pregnancies with PTL evaluations. PTL evaluation was defined as having fFN and/or TVUS assessment. Outcomes were ascertained using diagnostic, procedural, and diagnosis-related group codes. Multivariable logistic regression assessed the association between fFN and/or TVUS results and perinatal outcomes. Compared with those without PTL evaluations, those with positive fFN tests had higher adjusted odds ratio (adj.OR) for sPTB (2.95, 95% confidence interval [CI]: 2.64, 3.29), RDS (2.34, 95% CI: 2.03, 2.69), and NICU admission (2.24, 95% CI: 2.01, 2.50). In contrast, those who tested negative had lower odds for sPTB (adj.OR: 0.75, 95% CI: 0.70, 0.79), RDS (adj.OR: 0.67, 95% CI: 0.61, 0.73), and NICU admission (adj.OR: 0.74, 95% CI: 0.70, 0.79). Among those with positive fFN results, the odds of sPTB was inversely associated with CL. Health care costs for mothers and neonates were lowest for those with fFN testing only. This study demonstrates that positive fFN results were associated with an increased odds of sPTB, RDS, and NICU admission and the association with sPTB was inversely proportional to CL. Additionally, negative fFN results were associated with decreased odds of sPTB, RDS, and NICU admissions. fFN testing may predict these and other sPTB-related adverse outcomes hence its utility should be explored further. Moreover, fFN testing has some cost savings over TVUS. · Patients with positive fFN tests had higher odds of sPTB, RDS, and NICU admission.. · Inverse relationship between sPTB and CL among those with positive fFN tests was observed.. · Health care costs for mothers and neonates were lowest for those with fFN testing only..

A transdiagnostic prodrome for severe mental disorders: an electronic health record study.

Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.

HEALPAL: YOUR PERSONAL HEALTH COMAPANION

In an era where rapid technological advancements intersect with healthcare needs, the development of an intelligent healthcare chatbot has emerged as a promising solution. This study presents a comprehensive exploration into the conceptualization, design, and implementation of an AI-powered chatbot tailored to provide accurate and personalized healthcare information. Integrating Natural Language Processing (NLP) algorithms and Artificial Intelligence Markup Language (AIML), the chatbot aims to bridge the gap between individuals and reliable medical knowledge. Keywords—Chatbot, NLP, Markup Language

Identification of sleep phenotypes in COPD using machine learning-based cluster analysis

BackgroundDisturbed sleep in patients with COPD impact quality of life and predict adverse outcomes. Research questionTo identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification. Study design and methodsA longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates. ResultsAmong 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality. InterpretationWe identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population.

Injury severity prediction and exploration of behavior-cause relationships in automotive crashes using natural language processing and extreme gradient boosting

Addressing the global challenge of traffic crashes necessitates transcending traditional statistical models, which often fail to fully capture the interactions between factors causing crashes. This oversight restricts the predictive accuracy and adaptability of current methodologies. Additionally, there is a notable gap in research that examines the links between behavior-cause relationships and crash injury severity. Our study deploys Natural Language Processing (NLP) and Frequent Pattern (FP) growth algorithm to mine crash narratives for behavior-cause connections, combines with the predictive strength of eXtreme Gradient Boosting (XGBoost) and the interpretative clarity offered by SHapley Additive exPlanations (SHAP), our approach not only predicts crash injury severity with satisfactory precision but also explains the influence of specific behavior-cause and environment conditions on crash outcomes. The integration of NLP and XGBoost, complemented by SHAP insights, has shown promising results with an accuracy of 0.79, outperforming traditional discrete choice models and competes closely with other machine learning approaches, including Support Vector Machines, Random Forest, Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). Through detailed textual analysis and the establishment of a behavior-cause matrix, identifying five broad crash causes linked to 141 specific crash cause with behaviors, we uncover critical patterns such as the prominence of distracted driving in severe crashes. This comprehensive approach not only fills a critical research gap by linking behavior-cause relationships with injury severity but also sets the stage for developing targeted interventions to enhance road safety.

Video Summarization for Marathi Language

The Video Summarization Platform using Python Flask is a comprehensive tool designed to summarize Marathi and English videos while providing summaries in Hindi, Marathi, and English languages. Leveraging machine learning and natural language processing (NLP) techniques, this platform offers a sophisticated solution for efficiently extracting key information from videos. The platform begins by transcribing the audio content of the video into text using automatic speech recognition (ASR) technology. This transcription process ensures that the platform can accurately analyze and summarize the video's content. Next, the text is translated into the target languages, namely Hindi, Marathi, and English, enabling users from diverse linguistic backgrounds to access the summarized content. To generate concise and informative summaries, advanced NLP algorithm is applied. This algorithm analyze the transcribed text to identify the most significant phrases, sentences, and concepts. By considering factors such as keyword frequency, semantic relevance, and context, the platform effectively distils the video's content into digestible summaries. Additionally, machine learning models are employed to classify the type of video content. These models are trained on diverse datasets encompassing various video genres and topics. By recognizing patterns and features within the video content, the platform can accurately categorize videos into distinct types, such as news, interviews, tutorials, or entertainment. The platform's user interface, powered by Python Flask, offers a seamless experience for users to upload videos, select their preferred language for summarization, and receive concise summaries in their chosen languages. The intuitive design ensures accessibility and ease of use, catering to both novice and advanced users. Overall, the Video Summarization Platform serves as a valuable resource for individuals seeking efficient ways to consume multimedia content. Whether for educational, informational, or entertainment purposes, this platform empowers users to access summarized video content in multiple languages, facilitated by cutting-edge machine learning and NLP technologies. Key Words: Transcription, Marathi-speaking users, Marathi YouTube videos, video content, transcription, summary, translation, Natural Language Toolkit (NLTK), content comprehension, user interaction data, past summaries, recommendation

Abstract PS07-02: Benign breast disease and microcalcifications in percutaneous biopsies and breast cancer risk

Abstract Objectives: Mammographic microcalcifications (calcs) are a sentinel marker of in-situ and invasive breast cancer (BC) but most calcs reflect benign breast disease (BBD). BBD comprises a diverse set of lesions, variably associated with increased BC risk. While calc frequency data for the modern era are limited, historically, calcs were reported in 11% of BBD biopsies. Some studies suggest that calcs in BBD impact BC risks; however, the frequency and BC risks associated with calcs during the percutaneous biopsy era have not been clearly established. Accordingly, we assessed the association of calcs with specific BBD lesions and BC risk in a large contemporaneous BBD cohort including detailed pathology review. Methods: A cohort of 4,819 patients with percutaneously diagnosed BBD from 2002-2013 at Mayo Clinic, were identified. The cohort was followed from 6 months after biopsy until censoring, BC diagnosis, or December 2021. Mean follow-up was 11.3 (SD=5.1) years for BBD patients who remained cancer-free in follow-up compared with 7.4 (SD=4.3) years for cases. Histology of all biopsies were reviewed microscopically to record pathologic findings. A natural language processing algorithm (NLPA) was developed and utilized to identify the presence of calcs based on pathology reports. NLPA results were compared to 300 matched biopsy reports, taken as ground truth. We performed age-adjusted Cox proportional hazards regression to assess hazard ratios (HRs) with 95% confidence intervals (CIs) for specific BBD lesions, stratified by the presence (calc+) or absence of calcs (calcs-), with estimation of an interaction term to test for differences in risks by calc strata. Standardized incidence ratios (SIRs) estimated risks for calc+ and calc- BBD lesions versus matched strata for Iowa SEER population-based BC incidence rates. Results: The NLPA achieved sensitivity=0.993 and specificity=0.986. Overall, 42.8% of BBD biopsies were calc+, including 49.1% of biopsies preceding BC and 42.4% of biopsies not preceding BC (Chi-square p=0.02). Overall, risks of BC among women with BBD calc+ biopsies did not differ significantly from those of women with calc- BBD: HR=1.13 (95% CI: 0.90,1.41), p=0.290. SIRs were also similar for the two groups; women with BBD calc+ biopsies had an SIR =2.00 (95% CI: 1.72, 2.33) and BBD calc- biopsies yielded an SIR= 1.91 (95% CI: 1.64,2.22) (Table 1). HRs were similar for specific calc+ and calc- BBD lesions, with progressively higher risks for non-proliferative disease (NP), proliferative disease without atypia (PDWA) and atypical hyperplasia (AH) (pinteraction=0.55). Interaction terms were not significant for any specific lesion, apart from a marginally significant result for dilated ducts, wherein risks were marginally higher for calc- lesions (pInteraction =0.048), perhaps representing a chance finding. SIRs for calc+ versus calc- BBD lesions were also similar. Overall, 77.3% of AH was calc+; for AH calc+, SIR=3.76 (95%CI: 2.74-5.17) and for AH calc-, SIR=4.41 (95%CI: 2.56-7.59). Conclusion: In this recent, larger percutaneous BBD cohort, calcs were present in 42.8% of all specimens, representing a marked increase compared with historical BBD data, particularly those dominated by surgical biopsy for diagnosis and preceding implementation of full-field digital mammography. In this study, calcs in percutaneously diagnosed BBD were not associated with increased BC risk. Table 1: Comparison of standard incidence ratio of subsequent breast cancer with and without microcalcifications in benign breast disease by histologic impression (NP = non-proliferative disease, PDWA = proliferative disease without atypia, and AH = atypical hyperplasia). (1) compared to Iowa SEER population Citation Format: Sarah Schrup, Robert Vierkant, Stacey Winham, Celine Vachon, Derek Radisky, Jodi Carter, Laura Pacheco-Spann, Matthew Jensen, Amy Degnim, Mark Sherman. Benign breast disease and microcalcifications in percutaneous biopsies and breast cancer risk [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS07-02.

USE OF NATURAL LANGUAGE PROCESSING TECHNIQUES TO PREDICT PATIENT SELECTION FOR TOTAL HIP ARTHROPLASTY: RESULTS FROM THE AI TO REVOLUTIONISE THE PATIENT CARE PATHWAY IN HIP AND KNEE ARTHROPLASTY (ARCHERY) PROJECT

To examine whether Natural Language Processing (NLP) using a state-of-the-art clinically based Large Language Model (LLM) could predict patient selection for Total Hip Arthroplasty (THA), across a range of routinely available clinical text sources.Data pre-processing and analyses were conducted according to the Ai to Revolutionise the patient Care pathway in Hip and Knee arthroplasty (ARCHERY) project protocol (https://www.researchprotocols.org/2022/5/e37092/). Three types of deidentified Scottish regional clinical free text data were assessed: Referral letters, radiology reports and clinic letters. NLP algorithms were based on the GatorTron model, a Bidirectional Encoder Representations from Transformers (BERT) based LLM trained on 82 billion words of de-identified clinical text. Three specific inference tasks were performed: assessment of the base GatorTron model, assessment after model-fine tuning, and external validation.There were 3911, 1621 and 1503 patient text documents included from the sources of referral letters, radiology reports and clinic letters respectively. All letter sources displayed significant class imbalance, with only 15.8%, 24.9%, and 5.9% of patients linked to the respective text source documentation having undergone surgery. Untrained model performance was poor, with F1 scores (harmonic mean of precision and recall) of 0.02, 0.38 and 0.09 respectively. This did however improve with model training, with mean scores (range) of 0.39 (0.31–0.47), 0.57 (0.48–0.63) and 0.32 (0.28–0.39) across the 5 folds of cross-validation. Performance deteriorated on external validation across all three groups but remained highest for the radiology report cohort.Even with further training on a large cohort of routinely collected free-text data a clinical LLM fails to adequately perform clinical inference in NLP tasks regarding identification of those selected to undergo THA. This likely relates to the complexity and heterogeneity of free-text information and the way that patients are determined to be surgical candidates.

Natural Language Processing to Identify Home Health Care Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates

ObjectivesHome health care patients who are at risk for becoming Incapacitated with No Evident Advance Directives or Surrogates (INEADS) may benefit from timely intervention to assist them with advance care planning. This study aimed to develop natural language processing algorithms for identifying home care patients who do not have advance directives, family members, or close social contacts who can serve as surrogate decision-makers in the event that they lose decisional capacity. DesignCross-sectional study of electronic health records. Setting and ParticipantsPatients receiving post-acute care discharge services from a large home health agency in New York City in 2019 (n = 45,390 enrollment episodes). MethodsWe developed a natural language processing algorithm for identifying information documented in free-text clinical notes (n = 1,429,030 notes) related to 4 categories: evidence of close relationships, evidence of advance directives, evidence suggesting lack of close relationships, and evidence suggesting lack of advance directives. We validated the algorithm against Gold Standard clinician review for 50 patients (n = 314 notes) to calculate precision, recall, and F-score. ResultsAlgorithm performance for identifying text related to the 4 categories was excellent (average F-score = 0.91), with the best results for “evidence of close relationships” (F-score = 0.99) and the worst results for “evidence of advance directives” (F-score = 0.86). The algorithm identified 22% of all clinical notes (313,290 of 1,429,030) as having text related to 1 or more categories. More than 98% of enrollment episodes (48,164 of 49,141) included at least 1 clinical note containing text related to 1 or more categories. Conclusions and ImplicationsThis study establishes the feasibility of creating an automated screening algorithm to aid home health care agencies with identifying patients at risk of becoming INEADS. This screening algorithm can be applied as part of a multipronged approach to facilitate clinician support for advance care planning with patients at risk of becoming INEADS.

Validation of a Natural Language Processing Algorithm using National Reporting Data to Improve Identification of Anesthesia-related ADVerse evENTs: The “ADVENTURE” Study

BackgroundReporting and analysis of adverse events (AE) is associated with improved healthcare learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives. MethodsWe used machine learning to analyze 9,559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020, for a total of 135,000 unique de-identified AE reports. We validated the labeling and determined the associations between different root causes and patient consequences. The model was validated by independent expert anesthesiologists. ResultsThe machine learning and Artificial Intelligence (AI) model trained on 9,559 AE datasets accurately categorized 8800 (88%) of reported AE. The three most frequent AE types were “difficult orotracheal intubation” (16.9% of AE reports), “medication error” (10.5%), and “post-induction hypotension” (6.9%). The accuracy of the AI model reached 70.9% sensitivity, 96.6% specificity for “difficult intubation”, 43.2% sensitivity, and 98.9% specificity for “medication error.” ConclusionsThis unsupervised method provides an accurate, automated, AI-supported search algorithm that ranks and helps to understand complex patterns of risky patient situations and has greater speed, precision, and clarity when compared to manual human data extraction. Machine learning (ML) and natural language processing models can effectively be used to process natural language AE reports and augment expert clinician input. This model can support clinical applications and methodological standards of implementations and be used to better inform and enhance decision-making for improved risk management and patient safety. Trial RegistrationThe study was approved by the ethics committee of the French Society of Anesthesiology (IRB 00010254-2020-20) and the CNIL (CNIL: 118 58 95) and the study was registered with ClinicalTrials.gov (NCT: NCT05185479).

A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness

ObjectivesWe evaluated methods for preparing electronic health record data to reduce bias before applying artificial intelligence (AI). MethodsWe created methods for transforming raw data into a data framework for applying machine learning and natural language processing techniques for predicting falls and fractures. Strategies such as inclusion and reporting for multiple races, mixed data sources such as outpatient, inpatient, structured codes, and unstructured notes, and addressing missingness were applied to raw data to promote a reduction in bias. The raw data was carefully curated using validated definitions to create data variables such as age, race, gender, and healthcare utilization. For the formation of these variables, clinical, statistical, and data expertise were used. The research team included a variety of experts with diverse professional and demographic backgrounds to include diverse perspectives. ResultsFor the prediction of falls, information extracted from radiology reports was converted to a matrix for applying machine learning. The processing of the data resulted in an input of 5,377,673 reports to the machine learning algorithm, out of which 45,304 were flagged as positive and 5,332,369 as negative for falls. Processed data resulted in lower missingness and a better representation of race and diagnosis codes. For fractures, specialized algorithms extracted snippets of text around keywork “femoral” from dual x-ray absorptiometry (DXA) scans to identify femoral neck T-scores that are important for predicting fracture risk. The natural language processing algorithms yielded 98% accuracy and 2% error rate The methods to prepare data for input to artificial intelligence processes are reproducible and can be applied to other studies. ConclusionThe life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. When applying artificial intelligence methods, input data must be prepared optimally to reduce algorithmic bias, as biased output is harmful. Building AI-ready data frameworks that improve efficiency can contribute to transparency and reproducibility. The roadmap for the application of AI involves applying specialized techniques to input data, some of which are suggested here. This study highlights data curation aspects to be considered when preparing data for the application of artificial intelligence to reduce bias.

Fine-grained food image classification and recipe extraction using a customized deep neural network and NLP

Global eating habits cause health issues leading people to mindful eating. This has directed attention to applying deep learning to food-related data. The proposed work develops a new framework integrating neural network and natural language processing for classification of food images and automated recipe extraction. It address the challenges of intra-class variability and inter-class similarity in food images that have received shallow attention in the literature. Firstly, a customized lightweight deep convolution neural network model, MResNet-50 for classifying food images is proposed. Secondly, automated ingredient processing and recipe extraction is done using natural language processing algorithms: Word2Vec and Transformers in conjunction. Thirdly, a representational semi-structured domain ontology is built to store the relationship between cuisine, food item, and ingredients. The accuracy of the proposed framework on the Food-101 and UECFOOD256 datasets is increased by 2.4% and 7.5%, respectively, outperforming existing models in literature such as DeepFood, CNN-Food, Wiser, and other pre-trained neural networks.

An Interview System Using AI Technology

The rapid advancement of artificial intelligence (AI) has led to innovative applications across various industries, and one such application is the development of an AI-based interview system. This system aims to enhance and streamline the traditional interview process by leveraging AI technologies to assess and interact with candidates. The AI- based interview system incorporates natural language processing (NLP), machine learning (ML), and sentiment analysis algorithms to analyze both verbal and non-verbal cues during interviews. By employing AI, the system can assess a candidate's communication skills, problem-solving abilities, and cultural fit within the organization. The system's machine learning algorithms continuously learn and improve over time, enhancing their ability to accurately evaluate candidates and predict their potential success within the company. The benefits of an AI-based interview system include increased efficiency in the hiring process, reduction in human bias, and the ability to handle a large number of interviews simultaneously. Furthermore, the system can provide data-driven insights to recruiters, aiding in evidence-based decision-making. While the development and implementation of an AI-based interview system pose ethical considerations and challenges related to privacy and bias, ongoing research and ethical guidelines are essential to address these concerns and ensure fair and transparent use of the technology. As technology continues to evolve, further research and collaboration between AI developers and human resource professionals are crucial to harness the full potential of this innovative tool in the hiring process.

AI Enabled Chatbot

Abstract: As technology continues its rapid evolution, the integration of Artificial Intelligence (AI) into chatbot systems emerges as a pivotal frontier in reshaping human-computer interaction. This research paper embarks on a comprehensive exploration of the intricate synergy between AI technologies and chatbot development, with a primary focus on elevating user interactions to unprecedented levels of sophistication and intuitiveness. By delving into a nuanced understanding of methodologies, technologies, and challenges, this study aims to provide a holistic perspective on the present state and future potential of AIenabled chatbots. The journey begins with a retrospective analysis of the evolution of chatbots, tracing their trajectory from rulebased systems to the forefront of AI-driven conversational agents. The advent of AI, particularly Natural Language Processing (NLP) and machine learning algorithms, has ushered in a new era, enabling chatbots to not only comprehend but also respond intelligently, mirroring the complexities of human language.

Advanced Social Media Toxic Comments Detection System Using AI

Abstract: In the contemporary digital landscape, social media platforms have evolved into essential components of our daily lives, fostering connections, idea sharing, and meaningful conversations. Nevertheless, the escalating volume of online interactions has ushered in a concerning rise in toxic comments and cyberbullying, casting a shadow over the potential for a healthy onlineenvironment. Toxic comments, spanning hate speech, harassment, and offensive content, not only inflict harm on individuals but also detrimentally affect the overall user experience. This project seeks to address this pressing issue through the development of a cutting-edge Toxic Comment Detection system, leveraging the power of Natural Language Processing (NLP) and Machine Learning (ML) techniques. The primary objective of this endeavor is to create an automated system capable of identifying and flagging toxic comments in realtime across various social media platforms. By employing advanced NLP algorithms and ML models, the system aims to analyze textual content swiftly and accurately, pinpointing instances of toxicity. Once identified, the system will promptly notify moderators, enabling swift intervention and potential removal of the harmful content. By implementing this technological solution, the project aspires to contribute significantly to fostering a safer and more inclusive online environment where users can engage without fear of encountering toxic behavior. Through the fusion of NLP and ML, this endeavor aims to exemplify the transformative potential of technology in mitigating the challenges posed by toxic comments in the digital age.

Android Legal Assistance Application: Bridging the Gap in Legal Access

The Android Legal Assistance Application represents a significant stride towards democratizing access to legal services through technological innovation. In this paper, we provide a thorough analysis of the application’s function- alities, algorithms, and impact. By leveraging the ubiquity of smartphones and advanced algorithms, our application aims to bridge the gap between individuals and legal resources, empowering users to navigate complex legal land- scapes effectively. Through a comprehensive examination, we elucidate how the application addresses the persistent challenge of unequal access to legal resources, particularly for marginalized communities. By providing tailored legal assistance, recommendations, and access to a vast repository of legal documents, our application strives to promote justice and equality. We delve into the core features of the application, including its legal assistance feature, lawyer recommendation system, and extensive legal document library. Moreover, we discuss the system architecture, tools, and technologies employed in the development process. Through advanced algorithms in natural language processing (NLP) and document analysis, our application delivers accurate and contextually appropriate legal guidance to users. We also present empirical results indicating improved accessibility, enhanced user experience, and positive user feed- back. Finally, we outline future prospects for the application, emphasizing the integration of additional legal services, implementation of robust security measures, and refinement of algorithms. Overall, the Android Legal Assistance Application signifies a paradigm shift in legal access, paving the way for a more inclusive and equitable society.

Veritas AI: The ChatGPT Polygraph

Aims: The objective of Veritas AI is to revolutionize the domain of lie detection through the deployment of a cutting-edge algorithm within the realms of computational linguistics and artificial intelligence. Study Design: Veritas AI is conceptualized as a groundbreaking framework that integrates advanced syntactic and semantic analysis, leveraging generative pre-trained transformers to identify linguistic cues indicative of deception. Place and Duration of Study: The research underpinning Veritas AI’s algorithm was meticulously executed at the Abacus CSE Lab over a period from December 2022 to March 2024, ensuring a robust empirical foundation for the system’s validation and optimization. Methodology: Employing a deep learning neural network at its core, Veritas AI is trained on a diverse dataset comprising both truthful and deceptive dialogues. This training is complemented by multimodal biometric interrogation techniques and sophisticated natural language processing algorithms. Results: The empirical results underscore Veritas AI’s unparalleled accuracy in discerning truth, marked by its ability to provide real-time adaptive feedback and maintain robust performance across various communication scenarios. Conclusion: In conclusion, Veritas AI stands as a testament to the symbiotic potential of human ingenuity and machine learning. Its precision-engineered algorithm, underpinned by empirical validation, heralds a transformative leap in the field of automated veracity assessment, setting a new benchmark for truth analysis in the digital age.

Active Chat Monitoring and Suspicious Chat Detection Over Internet

In today's digital landscape, the proliferation of online communication platforms has brought about unprecedented opportunities for interaction and collaboration. However, this vast virtual space also presents challenges in ensuring safety and security, particularly concerning the monitoring of chat activities and the detection of suspicious behaviour. This research paper investigates the methods and technologies employed proactive chat monitoring and random chat across the web. The paper begins by examining the importance of monitoring chat platforms to prevent various forms of malicious activities, including cyberbullying, fraud, terrorism, and other illicit behaviours. It then delves into the techniques utilized for real-time monitoring of chat conversations, such as keyword filtering, sentiment analysis, and natural language processing algorithms. Furthermore, the paper explores the principles behind suspicious chat detection, highlighting the indicators of potentially harmful or illegal activities within chat communications. This includes analysing patterns of conversation, identifying anomalous behaviour, and integrating machine learning models to improve detection accuracy. Moreover, the research discusses the ethical considerations and privacy concerns associated with active chat monitoring, emphasizing the need for transparent policies and safeguards to protect user privacy while ensuring effective security measures. Keywords: Active chat monitoring, Suspicious chat detection, Online communication platforms, Cybersecurity, Malicious activities, Keyword filtering, Sentiment analysis, Natural language processing algorithms.