Cognizant (India)

Companies can't function without efficient human resource management, especially when it comes to matching open positions with qualified applicants. Nevertheless, in the modern HRM environment, recruiters have obstacles like managing a high number of resumes, catering to candidates' varied interests, and satisfying employers' growing need for specialised skills. Intelligent CV analysis and job matching solutions are necessary since these time-sensitive judgements frequently degrade selection quality. The research methodology used in this study is a hybrid approach that combines machine learning, fuzzy logic, and NLP. Tokenisation, phrase splitting, named entity recognition, and part-of-speech tagging are preprocessing stages that are carried out before BERT is used for training, engineering, and feature extraction. Combining ANNs with fuzzy regression models improves predicted accuracy by taking advantage of both the adaptability of ANNs and the partial data handling capabilities of fuzzy algorithms. The results demonstrate that the suggested Fuzzy ANN model outperforms the competition, reaching a 98.38% analytical accuracy rate. Based on these results, it's clear that the model can make HRM more efficient, less taxing on recruiters, and better at connecting candidates with jobs.

Watermarking is crucial for protecting medical images, guaranteeing data integrity and authenticity in telemedicine, especially for rural healthcare facilities with limited computational power. This paper introduces a lightweight watermarking scheme that performs a comparative study of Lifting Wavelet Transform (LWT) versus Discrete Wavelet Transform (DWT), both combined with Discrete Cosine Transform (DCT), to achieve optimal imperceptibility, robustness, and computational efficiency. The comparison of LWT and DWT is essential because LWT has lower computational complexity and memory usage, hence more appropriate for resource-limited environments than DWT, and without the use of Singular Value Decomposition (SVD) to further reduce processing requirements and improve compatibility with low-power devices. The suggested approach adopts adaptive embedding against texture complexity, secure synchronization patterns with m-sequences and Barker codes, and geometric correction utilizing ORB for guaranteed watermark extraction under attacks from JPEG compression, Gaussian noise attack, rotation attack, median filter attack, and Gaussian blur. A new error correction code interleaved with majority voting is employed to increase the watermark’s strength. Tested at various embedding strengths, the scheme attains Peak Signal to Noise Ratio (PSNR) values over 40 dB and Structural Similarity Index (SSIM) values above 0.95, signifying extremely little visual degradation. Low Bit Error Rates (BER) and high Normalized Correlation (NC) in various attack scenarios validate its success. This computational-friendly solution is designed to arm remote healthcare organizations with limited computer ability to reliably authenticate and attest to medical images, meeting severe telemedicine demands.

Polymers are very important in the process of making pharmaceutical goods. When it comes to chemicals, polymers are known for being big and heavy. Polymers are made up of many smaller parts called monomers. These monomers are linked together by covalent bonds or other chemical reactions. When several monomer units are joined together to make a long chain polymer, this is called polymerization. Polymer nanoparticles (PNPs) are made using physical methods or direct nanosynthesis. They are then polymerized in micro- or nanoemulsions with nanoreactor sections. Polymers from both natural and man-made sources are used a lot in the medicinal and biomedical fields, and their use is growing quickly. Polymers are used a lot in the pharmacy business today to control how drugs are released. We will talk about this subject in more detail in the parts that follow. Other applications of polymers are packaging materials, medical equipment, and packaging aids for pharmaceuticals, such as coating agents, suspending agents, emulsifying agents, adjuvants, adhesives, etc. This chapter's goal is to offer a comprehensive overview of the classification of polymers, characterization, and many applications for pharmaceutical polymers in solid oral dosage forms. The several kinds of polymeric excipients are discussed, and their unique functions in oral medication administration are highlighted. This chapter may help scientists rationally use polymeric excipients, taking full advantage of their many properties and potential effects on drug delivery.

The automotive industry stands at the crossroads of a profound transformation—one driven by the convergence of sustainability goals, digital innovation, and evolving consumer expectations. Integrated Innovations in Automotive Manufacturing, R&D, Marketing, Financial Services, and Connected Mobility explores how Artificial Intelligence (AI), Machine Learning (ML), and Cloud Technologies are reshaping every facet of the automotive value chain to deliver smarter, greener, and more customer-centric solutions. This book offers a holistic perspective on how integrated technologies are no longer optional enhancements but essential components for competitiveness in the modern automotive landscape. In manufacturing, AI-powered robotics, predictive maintenance, and digital twins are streamlining operations and minimizing waste. In R&D, advanced simulation and generative design are accelerating innovation cycles. Marketing and customer engagement have become increasingly data-driven, delivering personalized experiences powered by real-time insights. Financial services are also evolving—leveraging cloud-based platforms and AI to offer flexible financing models, dynamic insurance products, and risk analysis tailored to consumer behavior. Meanwhile, connected mobility, encompassing smart vehicles, shared transportation, and autonomous systems, is redefining how people interact with and move through the world. By presenting real-world case studies, emerging trends, and actionable strategies, this book serves as a roadmap for automakers, technology leaders, policymakers, and innovators seeking to drive sustainable growth through digital integration. It also addresses critical challenges such as data privacy, cybersecurity, ethical AI, and the need for cross-sector collaboration. At its core, this book is about transformation—not just of vehicles or systems, but of the broader purpose of the automotive industry. The road ahead is not only electric and autonomous but also intelligent and interconnected. Integrated Innovations invites readers to explore how the synergy of AI, ML, and cloud computing is accelerating progress toward a more sustainable and resilient mobility future.

Data rejection and filtration are required in this step to remove outliers and noise, to get a realistic picture of normal behaviour. The output of a health monitoring system is usually a numerical quantity or an indicator that quantifies the condition of the monitored system's component or subsystem. Different conditions can be represented by different values of such indicators. These features capture higher-level information in the sensor data. The parameters acting as condition indicators for faults are identified and monitored to detect, identify, and characterise faults by studying anomalies and trends. Diagnostic processes allow the rapid determination of specific components that need to be replaced during maintenance. Prognostic processes enable the prediction of the residual life of components by analysing trends in historical observations. A scheme capable of performing fault detection and identification has to be developed first. In case faults are identified, isolation schemes should indicate the degraded subsystem or part of the system which is affected by the fault. Finally, a set of different nudges has to be identified and assessed, regarding the more or less strong deviation from expected performance that is introduced by the fault and its progression. This assessment has to be performed either by making direct use of a generic degradation model or by employing machine learning techniques.

Existing data governance frameworks are facing issues due to the fast development of generative artificial intelligence (AI), which has garnered international attention. AI regulation is made more challenging by the growing technological complexity and size of data utilization, which also poses problems for the existing legal framework. The literature on ethical and legal concerns related to artificial intelligence and data privacy is reviewed in this article. It concluded that the rapid advancement of AI, particularly generative models, presents significant ethical and legal challenges related to data privacy, security, and personal rights. As AI increasingly relies on vast amounts of personal data, risks such as data leakage, misuse, and cyberattacks escalate. Existing privacy regulations struggle to keep pace with evolving technologies, highlighting the urgent need for robust frameworks, interdisciplinary collaboration, and ethical foresight. Addressing these concerns requires a balanced approach that safeguards individual privacy while fostering responsible innovation, ensuring that AI development aligns with societal values and legal protections in the digital age.

Medical nutrition therapy is important for diabetes management. This randomized controlled trial investigated the effects of a diabetes-specific formula (DSF) on glycemic control and cardiometabolic risk factors in adults with type 2 diabetes (T2D). Participants (n = 235) were randomized to either DSF with standard of care (SOC) (DSF group; n = 117) or SOC only (control group; n = 118). The DSF group consumed one or two DSF servings daily as meal replacement or partial meal replacement. The assessments were done at baseline, on day 45, and on day 90. There were significant reductions in glycated hemoglobin (-0.44% vs. -0.26%, p = 0.015, at day 45; -0.50% vs. -0.21%, p = 0.002, at day 90) and fasting blood glucose (-0.14 mmol/L vs. +0.32 mmol/L, p = 0.036, at day 90), as well as twofold greater weight loss (-1.30 kg vs. -0.61 kg, p < 0.001, at day 45; -1.74 kg vs. -0.76 kg, p < 0.001, at day 90) in the DSF group compared with the control group. The decrease in percent body fat and increase in percent fat-free mass at day 90 in the DSF group were almost twice that of the control group (1.44% vs. 0.79%, p = 0.047). In addition, the percent change in visceral adipose tissue at day 90 in the DSF group was several-fold lower than in the control group (-6.52% vs. -0.95%, p < 0.001). The DSF group also showed smaller waist and hip circumferences, and lower diastolic blood pressure than the control group (all overall p ≤ 0.045). DSF with SOC yielded significantly greater improvements than only SOC in glycemic control, body composition, and cardiometabolic risk factors in adults with T2D.

Childhood undernutrition is associated with increased morbidity, mortality and a high socio-economic burden. Supporting Pediatric GRowth and Health OUTcomes (SPROUT) is a randomized, controlled trial evaluating the effects of an oral nutritional supplement (ONS) with dietary counseling (DC; n = 164) compared to a DC-only group who continued consuming their habitual milk (n = 166; NCT05239208). Children aged 24-60 months who were at risk or with undernutrition, as defined by weight-for-age [WAZ] < -1 and height-for-age [HAZ] < -1 according to the WHO Growth Standards, and who also met the criterion of weight-for-height [WHZ] < 0, were enrolled in Vietnam. ONS + DC had a larger WAZ increase at day 120 (primary endpoint) vs. DC (least squares mean, LSM (SE): 0.30 (0.02) vs. 0.13 (0.02); p < 0.001), and larger improvements in all weight, BMI and weight-for-height indices at day 30 and 120 (all p < 0.01). Height gain was larger in ONS + DC in all indices, including height-for-age difference [HAD; cm: 0.56 (0.07) vs. 0.10 (0.07); p < 0.001], at day 120. ONS + DC had larger arm muscle but not arm fat indices, higher parent-rated appetite, physical activity and energy levels, longer night sleep, fewer and shorter awakenings, and better sleep quality than DC. Adding ONS to DC, compared to DC-alone, improves growth in weight and height, linear catch-up growth, and health outcomes in children with or at risk of undernutrition.

Differential equations are an essential part of mathematical modeling for various engineering and science problems. In this regard, many systems are data driven and hence the modeled differential equation too. In this context, the Artificial Neural Network (ANN) is a well-known model inspired by the Natural Neural Network (NNN) to simulate, analyze, and process the information. It possesses three layers, viz., input, hidden, and output, by which one can process the information at hand. As such, this chapter includes the idea of ANN with the steepest descent search optimization technique to solve ordinary differential equations and a system identification structural problem. The backpropagation technique has also been adopted to solve the aforementioned problems and to compare the acquired results. Both methods are found to be effective for solving ODEs by constructing the appropriate form of trial solutions. The obtained results clearly manifest that this neural network architecture with optimization technique is simple, efficient, and appropriate to implement for dynamic physical problems.