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Skin cancer is deemed to be the most dangerous type of cancer. It occurs due to the damage caused to the DNA and if left untreated can lead to death. Various methods have been devised over the last few years for skin cancer detection. However, their performance was affected by various challenges in image analysis, like color illumination variations, differences in shape, size, etc. Therefore, to tackle these issues the novel framework, deep learning (DL) technique for accurate skin cancer detection and lesion segmentation is developed. Primarily, the skin image is pre-processed by employing a weighted median filtering to eradicate the noises contained. Then, the segmentation of skin lesions is carried out by employing the efficient neural network (ENet). After that, augmentation is accomplished to avoid overfitting, and later, feature extraction is carried out. At last, skin cancer detection is effectuated with the help of hybrid GoogleNet-LeNet (HGLeNet), which is obtained by merging GoogleNet and LeNet, here layers are modified using the regression concept. Furthermore, the introduced framework is examined for its effectiveness through accuracy, sensitivity and specificity. Moreover, HGLeNet attained the highest accuracy of 0.922, sensitivity of 0.928, as well as specificity of 0.924. [ABSTRACT FROM AUTHOR]

Interviews 50 primary school head teachers, 304 primary teachers, and 223 secondary department heads in England and Wales to evaluate the National Curriculum's impact. Reveals primary personnel's concern for increased assessment and recordkeeping and secondary heads' concern over language and science instruction. Discovers anxiety over curricular demands for increased science and technology. (CH)

A survey was made of all institutions offering inservice diploma courses for specialist teachers of children with special education needs in order to determine areas of special need covered by the courses, qualifications of the teacher educators, and characteristics of the teachers taking the courses. Results are discussed. (Author/MT)

Data derived from observation of 32 classrooms (eight- and nine-year-olds) are analyzed to consider the relationship between teaching methods and the amount of class time pupils spend on curriculum tasks. Results indicate that classrooms with higher levels of whole-class interaction have higher levels of pupil time on task, even during periods of individual activity. (Author/CH)

Current attitudes of junior class teachers in Great Britain concerning parents of children with special educational needs were evaluated. Results indicated that most teachers desired occasional but not frequent contact with parents. (DB)

Biometric identification is a mandatory tool to secure digital information for various industrial, government, commercial, and security applications. Face recognition is a distinct problem and lacks a unique solution applicable to all situations. Face recognition is not effective in identifying individuals in conditions, when a person is using glasses, hats or has a beard etc. Alternative technologies like Iris and retinal scan biometric techniques need sophisticated equipment, which is not financially viable for all applications. Voice recognition methods have low accuracy and are affected by situations where a change in a person's voice due to illness like cold render absolute identification inaccurate. This paper proposes a biometric method implementing multiple techniques i.e., both face and voice recognition technique as an effective identification tool. Identification process using combined biometric methods makes a foolproof security system, thereby leaving no scope for error. A comprehensive assessment of the performance accuracy of several algorithms like Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLD), Dynamic Time Warping (DTW), Support Virtual Machines (SVM), Neural Networks (NN) implemented in face and voice recognition were studied and then corresponding performance accuracy was reviewed. A new technique has been proposed using these performance accuracy results which would help provide the best hybrid method. The proposed hybrid biometric identification method is a viable solution for industries or areas with need for high security of their data systems. [ABSTRACT FROM AUTHOR]

Remote cardiovascular disease (CVD) diagnosis from ECG plays an important role in health care domain. Data mining, the major step in the process of the extraction of knowledge using descriptive and predictive algorithms that aid in making proactive decisions, has also been used for CVD diagnosis. Recently, diverse techniques have been developed for analyzing the ECG signals. However, due to the diversity of techniques used, terminologies, performance measures used in different techniques makes analysis and comparing of results thwarting. The aim of this work is to essentially explore and present the analysis of different data mining algorithms proposed earlier in literature for CVD diagnosis, their advantages and limitations. This paper presents various techniques for CVD diagnosis using data mining from an ECG signal under four major phases - ECG Acquisition, ECG Compression, ECG Feature Extraction and ECG diagnosis. The primary aim of this paper is to categorize the various researches done in this regard to provide a glossary for interested researchers and to aid in identifying their potential research direction. [ABSTRACT FROM AUTHOR]

This project was concerned with ways of supporting the ordinary school in the task of meeting special educational needs. It examined three broad strategies: support services provided from outside the school; link arrangements with special schools; and in‐service training. The present paper presents selected findings concerned with each. There has been moderate growth in local authority support services, and developments regarding the new Special Needs Advisory and Support Services are discussed. The nature and extent of links between ordinary schools and special schools are described. Finally, there is a brief report on the early development of the one‐term courses on Special Educational Needs in the Ordinary School which were running in 1984/ 85. [ABSTRACT FROM PUBLISHER]