NIIT (India)

The growing threat caused by DDoS attacks (distributed denial of service) poses a significant risk to the stability and availability of network infrastructure, especially softwaredefined. Traditional identification methods often have problems with the development of attack patterns and high network traffic volumes. In this paper developed an efficient DDoS recognition framework in which random forest (RF) and supported vector machine (SVM) evaluated in CICDDoS2019 records are trained and evaluated. Both the RF and SVM classifiers were evaluated in detail using key performance metrics such as detection accuracy, recall, F1 score, false negative rate, and runtime. Our experimental results show that the random forest model has a very high detection accuracy of 99.92% and the accuracy of the SVM model is 97.81%.

The Evolution of technology leads to an increase in vulnerabilities. Internet of Things (IoT) gadgets and new, future technologies like 5G, are expected to take over the globe and allow each user to have up to more than one device for monitoring their safety and well-being. IoT smart sensors are now essential. They are the ones gathering data from the actual world, but occasionally they are placed in difficult situations, and things may not go as planned. The sensors can malfunction, break, or be taken, or even intentionally tampered. These issues can cause sensors to provide anomalous or inaccurate data, which we refer to as outliers. In the given paper, we propose an anomaly detection and analysis framework for monitoring and identifying unauthorized access points in an IoT Sensor network. We also employ a combination of machine learning algorithms for further analysis of anomaly detection. The framework is applied to a dataset containing records of network activity, focusing on 'On-time' and 'CPU Power' metrics.

This paper serves as a medium to present a novel approach combining Circular Hough Transform in the pre-processing phase with YOLOv8n architecture for microplastic detection in glass containers. Our proposed system is lightweight in nature which processes a dataset of 781 images along with a csv file containing the information of the microplastic fragments present in the respective images, using CPU-based training. The methodology integrates traditional algorithm from the field of computer vision which is the Circular Hough Transform to isolate the necessary region thereby providing a more focused area to work on with a pre-trained neural network from the domain of deep learning to detect and classify microplastic within the samples. The empirical results of the model were as follows: achieved $71.55 \%$ precision, $30.44 \% \mathrm{mAP}$ @ 0.5, and $\mathbf{2 8 . 5 3 \%}$ recall, showcasing a conservative detection behavior suitable for quality control applications. While the performance of the model is not up to the level of GPU-based systems, yet our approach still offers accessibility and deployment efficiency with $\mathbf{8 4 . 7 m s}$ CPU inference time with $\mathbf{2 . 6 9 M}$ parameters.

SUMMARY RNAseq and DNAseq are fast, cost-effective and quantitative methods to dissect cancer cells. However, for each sample, they generate thousands of data points, and comparing patient samples multiplies the complexity. To handle these difficulties, we developed TumorMiner, a web-based tool for clinicians and basic researchers. Here we present our analyses and website for Small Cell Lung Cancer (SCLC). SCLC_TumorMiner includes 235 samples from untreated and relapse patients across the NCI, the University of Rochester, Tongji University and the University of Cologne (https://discover.nci.nih.gov/SclcTumorMinerCDB/). SCLC_TumorMiner allows the molecular classification of tumors based on the canonical NAPY classification and NMF, genomic network analyses exemplified by the Myc and Notch pathways, and the identification of risk-factors and predictive gene expression biomarkers for cell surface and intracellular targets such as DLL3, TROP2, SEZ6, CEA, TRPM5, SLFN11 and proapoptotic and multidrug resistance genes. The architecture of TumorMiner allows its extension to proteomic databases, and to other cancers and institutions worldwide to elucidate cancer pathways, achieve precision medicine and serve as a medical assistant software.

Salmonella biofilms are known to present a substantial public health concern owing to their remarkable ability to persist and exhibit resistance towards traditional antibiotics. The utilization of synthetic molecules emerges as a promising and innovative strategy in the battle against these biofilms. These chemical compounds have a broad range of functions, including adhesion inhibitors, dispersion agents, matrix-degrading enzymes, and quorum sensing disruptors. Each of these capabilities is carefully engineered to target different aspects of biofilm formation and architecture. Through the disruption of bacterial interactions within the biofilm and the destabilization of its intricate structure, synthetic molecules effectively heighten bacterial vulnerability to antibiotics and the host immune system. It is crucial to carry out further research to refine these synthetic compounds and enable their translation into practical applications intended to improve the prevention of infections and eventually promote better patient outcomes.

Fortified foods and beverages (FFBs) play a pivotal role in addressing nutritional deficiencies amid evolving dietary preferences in India. This research aims to explore Indian consumer perceptions of FFBs, emphasizing factors influencing adoption. To understand FFB acceptance in India’s culturally diverse and nutritionally varied context, the study integrated two influential consumer behavior models—the Health Belief Model and the Theory of Planned Behavior. Structural equation modeling was employed for data analysis, with online questionnaires collected from 421 FFB consumers. Factors like perceived susceptibility, perceived severity, perceived benefit, subjective norms, perceived behavioral control, and health consciousness were considered. All factors except perceived barrier significantly influenced behavioral intention to use FFBs. Moderating variables like age, gender, and income provided additional insights. The research outcomes offer valuable guidance to industry stakeholders, policymakers, and nutrition advocates, aiming to enhance nutritional standards and public health by encouraging FFB adoption in India.

In this paper, a DM DPDG TFET (Dielectrically modulated Drain pocket Dual gate Tunnel Field Effect Transistor) with an integrated nanocavity intended for biosensing applications is simulated and its performance assessed. The Silvaco Atlas TCAD used to do the simulations. The study compares several metrics for different biomolecules, including SARS COV-2 (Corona virus, K =2.5), Biotin (K =2.63), Protein (K =3.23), MCF-10A (Healthy Cancer cell, K =4.5), Carbohydrates (K =5) and MDA-MB-231 (Breast Cancer cell, K =22). These biomolecules are rendered immobile by a nanocavity is placed near the source end. When biomolecules are immobilized, the dielectric constant (K) of the nanocavities varies, which affects how the electrical properties of the proposed device is modulated. This modulation is tuned to identify the SARS COV-2, Breast cancer cell lines, and etc. To improve performance of the sensor device, the length of the oxide layer and thickness of the nanocavity adjusted in the process of optimization. The proposed Biosensor of its detection method is greatly influenced by the differences in the dielectric characteristics of different cell lines. The sensitivity of the biosensor is assessed in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>Ion, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>Vth, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>gm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>SS. The MDA-MB-231 (K =22) breast cancer cell line is the sample for which the biosensor shows highest sensitivity with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>V<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${_{\text {th}}} {=} 1.712$ </tex-math></inline-formula>V, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>I<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${_{\text {on}}} {=} 0.183$ </tex-math></inline-formula> mA/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>m, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>g<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${_{\text {m}}} {=} 0.581$ </tex-math></inline-formula> mA/V-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>m, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>SS =25.86 mV/decade. The effect of different cavity occupancy by immobilized cell lines is also investigated. Increase in cavity occupancy amplifies the variance in the performance characteristics of the biosensor. The threshold voltage(Vth) sensitivity of the proposed biosensor is compared to that of existing biosensors, it shows advantages in terms of cost-effectiveness and simplicity of manufacturing in addition to increased Ion/Ioff ratio, gm, sensitivity. As a result, the device has potential to use in the identification of SARS COV-2, cancerous cells, etc.