Homophobia: An Autoethnographic Story

This article is an autoethnographic account of one person’s struggle with homophobia. It chronicles the experiences and internal battle of the author as she struggles to understand and be accepting of homosexuality. The author identifies and discusses messages received, in early childhood and adulthood, as it relates to homosexuality and gender. These messages encompass religious ideology, as well as family and community beliefs toward gay/lesbian individuals

Shirin Neshat’s Visual Narratives as Monumental Space of Truth and Memory: A Select Study

The Iranian visual artist, Shirin Neshat, through her visual arts of photography, video installations and movies seem to present the cultural transformation of her native land triggered by the Islamic revolution of 1979 which drastically changed the social, political and cultural realms of the land. The change became visually evident in the compulsory dress code for women who were forced to wear ‘chador’, a traditional Iranian loose black cloak like garment and veil in public space. The present paper is an attempt to study the three photographs included in the series, ‘The Women of Allah’ as revealing the monumental space within Iran that in turn exposes the ambiguous and ambivalent representations of the land and its people in the cultural context.  ‘Monumental space’ as envisaged by Henry Lefebvre in The Production of Space includes the notions of collective memory, power and permanence. This spatial concept when used to analyze the condition of post revolutionary Iran as revealed in these photographs in the series, may throw light on the selective representations and deliberate omissions leading to a tilted view of the past through history. It may help to analyze the alienation of the artist from her native land as enabling her to step back and understand the versions of truth represented by certain memories.   The portraits themselves may be revealed as the monumental space of untold saga of resistance and defiance against a set of narratives that project stereotypical images weaving their own versions of truth

Landslide hazard mapping of Wayanad District of Kerala, India, incorporating copula-based estimation of joint probability of rainfall

The development and integration of the spatial and temporal probabilities of landslides are required for complete landslide hazard mapping at any location. Under changing climate, the computation of the temporal probability of landslides with rainfall magnitude alone is inaccurate. This research proposes a framework based on copula functions to develop a landslide probability map using multi-site rainfall data by accounting for the rainfall variables of intensity and duration using a joint-probability approach. The proposed technique is used for Wayanad District, Kerala, India, considering extreme rainfall events in 2018. Firstly, the landslide susceptibility map of the district was developed using a robust random forest (RF) model. Based on regional geology, geomorphology, and climate, different regions of Wayanad have varying rainfall thresholds assessed according to the intensity and duration of the rainfall. Then, the temporal probability of landslides was developed, accounting for the intensity and duration of rainfall events using the joint-probability estimation using copula. Through the integration of the landslide spatial probability map with the temporal probability, landslide hazard maps (LHMs) for Wayanad were developed for time periods ranging from 1 to 50 years. The results of the study indicate the need for bi- or multi-variate landslide probability modeling in studies on regional landslide hazard assessments.</p

Pioneering Method to Analyse Depression in Human Being Using Audio-Video Parameters

In medical science there are different health disorders have high economical cost. There are many diseases present in medical science,mental disorder is the one of the most powerful disease which can have large impact on society. We are going to implement the innovative approach for sensing the human emotion by using automatic facial tracking in video, measuring facial activity, recognition of facial expression and by using the frequency of audio signal. The main goal is to develop and diagnostic mental disorder using affective sensing technique. The sound and visual communication complement each other, by considering this hypothesis for mental disorder analysis. Using multimodal approach for mental disorder diagnosis we are using combination of audio and video fusion method. A different audio and video features are generated separately. Frame level used to classify and combined video and audio features by using support vector machine (SVM).The innovative techniques show the proposed framework’s effectiveness in mental disorder analysis

Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review

The morbidity of scalp diseases is minuscule compared to other diseases, but the impact on the patient's life is enormous. It is common for people to experience scalp problems that include Dandruff, Psoriasis, Tinea-Capitis, Alopecia and Atopic-Dermatitis. In accordance with WHO research, approximately 70% of adults have problems with their scalp. It has been demonstrated in descriptive research that hair quality is impaired by impaired scalp, but these impacts are reversible with early diagnosis and treatment. Deep Learning advances have demonstrated the effectiveness of CNN paired with FCN in diagnosing scalp and skin disorders. In one proposed Deep-Learning-based scalp inspection and diagnosis system, an imaging microscope and a trained model are combined with an app that classifies scalp disorders accurately with an average precision of 97.41%- 99.09%. Another research dealt with classifying the Psoriasis using the CNN with an accuracy of 82.9%. As part of another study, an ML based algorithm was also employed. It accurately classified the healthy scalp and alopecia areata with 91.4% and 88.9% accuracy with SVM and KNN algorithms. Using deep learning models to diagnose scalp related diseases has improved due to advancements i computation capabilities and computer vision, but there remains a wide horizon for further improvements

Performance of antenna selection schemes for massive multiple-input multiple-output systems under Non-orthogonal multiple access cooperative communication

46-50Non-orthogonal multiple access (NOMA) has emerged as a promising technology for 5G systems. The most important characteristic of NOMA is that other users' messages is available to the users with better channel conditions. In this work, a modified antenna selection scheme called Double threshold generalized selection combining (DT-GSC) to save power in receivers used for massive multiple-input multiple-output (MIMO) applications are proposed. The diversity combiner selects the paths above two threshold values set at the combiner, the input and the combiner's output. These threshold values are selected based on the practical communication scenario. The average number of combined branches and estimated path are shown mathematically. The bit error performance of DT-GSC and maximal ratio combiner (MRC) are plotted. Through numerical examples it is evident that the new combining technique performs better compared to the existing ones. This combining technique is beneficial in the massive MIMO base station and user equipment with multiple antennas or cooperative communication set up with users employing the MRC scheme. Simulation results are presented to demonstrate the performance of the proposed technique

Feature Extraction Based on ORB- AKAZE for Echocardiogram View Classification

In computer vision, the extraction of robust features from images to construct models that automate image recognition and classification tasks is a prominent field of research. Handcrafted feature extraction and representation techniques become critical when dealing with limited hardware resource settings, low-quality images, and larger datasets. We propose two state-of-the-art handcrafted feature extraction techniques, Oriented FAST and Rotated BRIEF (ORB) and Accelerated KAZE (AKAZE), in combination with Bag of Visual Word (BOVW), to classify standard echocardiogram views using Machine learning (ML) algorithms. These novel approaches, ORB and AKAZE, which are rotation, scale, illumination, and noise invariant methods, outperform traditional methods. The despeckling algorithm Speckle Reduction Anisotropic Diffusion (SRAD), which is based on the Partial Differential Equation (PDE), was applied to echocardiogram images before feature extraction. Support Vector Machine (SVM), decision tree, and random forest algorithms correctly classified the feature vectors obtained from the ORB with accuracy rates of 96.5%, 76%, and 97.7%, respectively. Additionally, AKAZE\u27s SVM, decision tree, and random forest algorithms outperformed state-of-the-art techniques with accuracy rates of 97.7%, 90%, and 99%, respectively

The Effectiveness of a Suggested Program Based on Prior Knowledge to Develop Eighth Graders' English Reading Comprehension Skills

This study investigates the effectiveness of a suggested program based on prior knowledge to develop eighth graders' English reading comprehension skills. The researcher uses four tools, a checklist to determine the suitable reading comprehension skills for the eighth graders, a questionnaire to determine the degree of importance of the reading comprehension skills, an achievement test (Pre& post test) , the suggested program and teacher reflection. The suggested program consists of Teacher's Guide, Student's Book, teaching aids and evaluation tools. They include 12 lessons, activities and techniques to activate prior knowledge before reading comprehension lessons. The checklist and the questionnaire are applied before the pre-test to identify the most important skills which are used in the test. The researcher has benefited from the results of the questionnaire and the test when building the suggested program. The reading comprehension skills are classified under four levels: The literal level, the interpretive level, the critical level and the creative level. The researcher purposively chose Al Aishya Higher Basic School for Girls in Dair Al Balah for the experiment and randomly chose two classes from the eighth grade classes. The sample of the study was 80 female students, (40) students in each one. They equally divided into two groups, experimental and control. Both groups were pre-tested to assure that they both were equivalent. The results were statistically analyzed to be compared with the post-test results. The suggested program was taught to the experimental group while the control one to the traditional method, then, the post-test was applied on both groups. The results were statistically analyzed, using Statistical Package for Social Science (SPSS). The findings revealed that there were significant differences between the mean scores attained by the experimental group and those by the control group in favor of the experimental group. This was due to prior knowledge activation before reading a text. The experiment shows the importance of activating prior knowledge before reading a text. The researcher recommends in light of the above findings that the eighth graders' English Language teachers are urged to activate prior knowledge before taking reading a text in order to develop not only eighth graders' reading comprehension skills and increase their comprehension but also their general achievement in English language

Innovative Approach to Detect Mental Disorder Using Multimodal Technique

The human can display their emotions through facial expressions. To achieve more effective human- computer interaction, the emotion recognize from human face could prove to be an invaluable tool. In this work the automatic facial recognition system is described with the help of video. The main aim is to focus on detecting the human face from the video and classify the emotions on the basis of facial features .There have been extensive studies of human facial expressions. These facial expressions are representing happiness, sadness, anger, fear, surprise and disgust. It including preliterate ones, and found much commonality in the expression and recognition of emotions on the face. Emotion detection from speech has many important applications. In human-computer based systems, emotion recognition systems provide users with improved services as per their emotions criteria. It is quite limited on body of work on detecting emotion in speech. The developers are still debating what features effect the emotion identification in speech. There is no particularity for the best algorithm for classifying emotion, and which emotions to class together