Autism Spectrum Disorder (ASD) is a disturbing neuro-developmental disorder affecting the behavioural ability and communication skills of an individual. Early detection of such prevalent abnormality through EEG pattern analysis with the help of computer-aided diagnostic tools; provide significant relief to the health care professionals for better management and treatment of such patients. This work has been proposed as an investigation of the sensory responsiveness of the children affected by ASD. This ASD is analyzed by extracting the non-linear features of the EEG signal. A total of 73 EEG signals are collected from different patients out of which 41 are affected with ASD and 32 with normal neural activity. These signals are then pre-processed and filtered using a low pass filter and then separated using Independent Component Analysis (ICA) into additive subcomponents. Significant features are extracted, which can be further investigated to identify event-related potential and Non-biological Artifacts. These features are then classified using Support Vector Machine (SVM) with an accuracy of 90.41%. As a result, appropriate sensory profiles can be obtained that may help proper diagnosis and treatment at an early stage.

The method of evaluating the physical traits of living creatures by generating essential proteins is known as gene expression. Translation and transcription are

Escherichia coli (E. coli) is a group of bacteria that cause infections in the gastrointestinal (GI) tract and urinary tract (UTIs). The rise in antimicrobial resistance (AMR) due to antibioticresistant genes (ARGs) linked to E. coli strains that cause UTIs poses a significant threat. Identifying ARGs and resistant strains is crucial for effective treatment. To identify ARGs and classify resistant strains in E. coli utilizing gene expression (GE) data with advanced computational techniques such as feature engineering and transfer learning (TL). In TL, knowledge acquired by the baseline model is transferred to the target domain (BI-LSTMGRU). ARGai 2.0 utilizes the Synthetic Minority Over-sampling Technique (SMOTE) for oversampling the GE dataset to evaluate the effectiveness of the proposed TL framework. Our proposed ARGai 2.0 model achieved a higher classification accuracy of 14% compared to 1D CNN and 11% compared to BI-LSTM-GRU individually. The analysis revealed that genes associated with the nitrate reductase operon (narU, narV, narW, narY, and narZ) exhibit high connectivity and interaction scores, indicating their central role in nitrate metabolism. This aligns with the high enrichment FDR of 3.07E-10 and fold enrichment of 229.28 for pathways related to nitrate reductase complex and nitrite transmembrane transporter activity. ARGai 2.0 successfully detected the significant genes responsible for antibiotic resistance and classified the resistant strains. The gene network analysis highlights the central role of nitrate metabolism genes, while peripheral genes like ansP and yncG are involved in more specialized functions.