Hima Deepthi Vankayalapati

Speech emotion recognition is one of the most emerging areas for emotion detection that may fall within the scope of affective computing. In this particular case, emotional speech files of spoken words delivered during verbal communication are of interest. The emotions of speech are investigated through sound and emotion in speech and are modeled through machine learning. Through machine learning, we performed a series of experiments on datasets like RAVDESS, TESS, SAVEE, and EMO-DB, which lean toward the objective that a Recurrent Neural Network (RNN) and (CLAF-SER): The Cross-Lingual Attention-Based Adversarial Framework for SER would be able to detect and classify such emotions as sadness, anger, happiness, neutrality, and fear. Features such as MFCC, LPCC, pitch, energy, and chroma were extracted before implementing the RNN. Through this model, TESS achieved the highest accuracy among the other datasets. However, CLAF-SER gives the best performance when all datasets are combined.

Experiencing the ongoing difficulties in the "security" field and upgrading the current standards by planning a cutting-edge innovation. Understood assurance of your cash in the bank being protected has consistently been the reality of concern. At the point when people were on the planet, they need cash for different things. As a long time, elapsed and with enormous advancement, individuals began bringing in cash, property, gems, and a lot of more valuable things. With enormous improvement, individuals wanted to get their income. It is not sufficient to have these embellishments, however, security of this is vital, for this reason, we keep them in bank storage. In any case, we regularly hear or read in a paper that some phony individual approaches the storage of someone else and has taken the cash. To conquer this sort of cheats, validation of the individual who needs to utilize the storage is vital. A novel security framework has been proposed which utilizing voice distinguishing proof, face location to improve the real time efficiency. We can contrast the enlisted pictures on the off chance that it is matched with the registered picture the storage will open any other way storage won’t open.

A real-time head pose and gaze estimation (HPGE) algorithm has excellent potential for technological advancements either in human–machine or human–robot interactions. For example, in high-accuracy advent applications such as Driver’s Assistance System (DAS), HPGE plays a crucial role in omitting accidents and road hazards. In this paper, the authors propose a new hybrid framework for improved estimation by combining both the appearance and geometric-based conventional methods to extract local and global features. Therefore, the Zernike moments algorithm has been prominent in extracting rotation, scale, and illumination invariant features. Later, conventional discriminant algorithms were used to classify the head poses and gaze direction. Furthermore, the experiments were performed on standard datasets and real-time images to analyze the accuracy of the proposed algorithm. As a result, the proposed framework has immediately estimated the range of direction changes under different illumination conditions. We obtained an accuracy of ~85%; the average response time was 21.52 and 7.483 ms for estimating head poses and gaze, respectively, independent of illumination, background, and occlusion. The proposed method is promising for future developments of a robust system that is invariant even to blurring conditions and thus reaching much more significant performance enhancement.

To develop a 3D printed material system for extracting the electrical conductivity of the piezoelectric material, to fabricate the integrated piezoelectric prototype and it’s always have a good availability with the more number of variety of materials for making with special characteristics. This paper introduces with direct ink writing method for printing the piezoelectric material by using multi materials such as terphinal, ethyl cellulose, methanol and graphene. The materials and processing steps for the manufacturing the 3d printed piezoelectric component, the individual material characteristics and the equivalent circuit is provided. Electrical Conductivity analysis on the 3D printed piezoelectric done with the above mentioned material and the result has been discussed by using the four point probe method.

Facial expressions are the facial changes in light of a man's interior enthusiastic moods, aims, or social interchanges which are investigated by computer frameworks that endeavor to consequently examine and perceive facial movements and facial component changes from visual data. Now and again the facial expression recognition has been mistaken for feeling examination in the computer vision space prompts uncouth backings of acknowledgment process such as face detection, feature recognition and expression recognition in that way bringing about the issues of identifying impediments, enlightenments, posture varieties, acknowledgment, decrease in dimensionality, and so forth. Notwithstanding that, an appropriate computation and forecast of exact outcomes additionally enhances the execution of the facial Expression recognition. Henceforth, a detailed study was required about the strategies and systems utilized for unraveling the issues of facial expressions during the time of face detection, feature recognition and expression recognition. So thepaper displayed different current strategies and afterward basically considered the effort by the different researchers in the area of Facial Expression Recognition.

With the ever-increasing need for concealing messages within cover media like image, video, and audio, numerous attempts have been developed for steganography. Most of the steganographic techniques perform their embedding operation on the cover image without selecting a better location. The right selection of location for embedding the information can lead to high imperceptibility and robustness. Accordingly, in this paper, we develop a new cost function for estimating the cost of every pixel to identify the good location to embed the message data. The proposed cost estimation procedure utilizes multiple parameters like wavelet coefficient, edge transformation, and pixel intensity. The proposed cost matrix is then utilized to embed the message data into the cover media using an embedding integer. The proposed steganographic technique is experimented with two magnetic resonance brain images, and the results are analyzed with the peak-to-peak signal-to-noise ratio (PSNR) and mean square error. The robustness analysis ensured that the proposed steganographic technique outperforms the existing methods by reaching the maximum PSNR of 72.74 dB.