The vehicular network plays a significant role in understanding the detailed study of vehicle communications. Multiple vehicles in the local communication range need to exchange the safety and infotainment information via common roadside infrastructure in Vehicular Ad hoc Networks (VANETs). The vehicle-to-Infrastructure (V2I) communication model helps to improve the efficiency of the intelligent transport system by providing safety warnings and reducing vehicle collisions. Machine learning is an artificial intelligence component that allows the machine to learn without being expressly trained to improve from experience automatically. Since VANET is imprecise and uncertain, Machine Learning (ML) and Software Agents (SAs) combining approaches resolve the issues of V2I communication challenges in VANETs. This paper proposes ML-based V2I Communication in VANETs using a software agent approach. The proposed agent-based model is made up of both static and mobile agents. The proposed model executes the decision tree algorithm to identify the event as non-critical or critical. The Q-Learning algorithm identifies the destination vehicle with improved bandwidth utilization, packet delivery ratio, end-to-end delay, V2I communication delay, and throughput and control overheads.

This paper presents a filter bank summation method to perform spectral splitting of input signal for binaural dichotic presentation along with dynamic range compression coupled with noise reduction algorithm based on wiener filter. This helps to compensate the effect of spectral masking, reduced dynamic range, and improves speech perception for moderate sensorineural hearing loss in the adverse listening conditions. We have considered cascaded structure of noise reduction technique; Filter Bank Summation (FBS) based amplitude compression and spectral splitting. Wiener filter produces the enhanced signal by removing unwanted noise. The signal is split into eighteen frequency bands, ranging from 0-5KHz, based on auditory critical bandwidths. To reduce the dynamic range, amplitude compression is carried out using constant compression factor in each of the bands. Subjective and objective assessment based on Mean Opinion Score (MOS) and Perceptual Evaluation of Speech Quality (PESQ) scores, respectively, are used to test the Perceived quality of speech for different Signalto-Noise Ratio (SNR) conditions. Vowel Consonant Vowel (VCV) syllable /aba/ and sentences were used as the test material. The results of the listening tests showed MOS scores for processed speech sentence "sky that morning was clear and bright blue"

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