Alzheimer’s disease is a multifactorial and progressive neurodegenerative disorder that affects an individual’s memory and cognitive skills. It is a major cause of death around the globe and according to 2015 Alzheimer’s association report, the death percentage has increased to 71% since year 2000. The clinical symptoms of the disease appear at a stage when the loss has become irreversible. Modern brain imaging techniques have enabled us to non-invasively visualize the internal structures of the brain. Scientists believe that structural and functional changes due to Alzheimer’s disease begin in the brain more than 20 years before any clinical symptoms are observed. Early detection of the disease is crucial for the patient, care givers and relatives to cope with the situation. It will also help medical practitioners to discover new drugs. For this reason there is an imperative need of image based automated techniques to assist medical professionals in correct diagnosis of Alzheimer’s disease using brain images. In recent years, there is an intensive research focused on the identification of Alzheimer’s disease from brain images using machine learning methods. Structural brain images like MRI have been extensively used in this regard. In our research work, we have proposed an automated image processing based approach for the early identification of Alzheimer’s disease from MRI scans of the brain. The dataset selected consists of 236 age and gender matched individuals and the features selected are volume of GM, WM and CSF, and size of hippocampus. In addition to image features, genetic aspects of Alzheimer’s disease are also considered in classification task. Well known APOE risk gene data and 14 SNP data associated with Alzheimer’s disease are incorporated in the feature set. Seven different classification models from different algorithmic paradigms are used for identification of patients and controls. For evaluation of our scheme, we have used cross validation and 66% vi ii split test strategy. Classification results are obtained using image features, genetic features and combination of the both. It is observed that image features produced best classification of cases and controls. On the other hand, genetic data can be very useful in predicting the risk of disease well before any changes to brain are observed. The proposed approach is novel because it has been able to achieve higher accuracy/specificity/sensitivity values even using smaller feature set which is not the case of existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. Best results (100% accuracy, 100% specificity, 100% sensitivity) are achieved using volume of GM and size of left hippocampus with J48 classifier. Similarly APOE risk gene predicted the disease with 75% accuracy for all classifiers whereas SNP data achieved 86% accuracy with Naïve Bayes and SVM. The proposed approach will play a vital role in the domain of Computer Aided Diagnostics and Preventive Studies.
مولانا سید منت اﷲ رحمانی مرحوم دارالمصنفین میں یہ خبر نہایت غم و ندوہ کے ساتھ سنی گئی کہ امارت شرعیہ بہار و اڑیسہ کے امیر، مسلم پرسنل لا بورڈ کے جنرل سکریٹری، مسلم مجلس مشاورت کے بانی ممبر، دارالعلوم دیوبند و ندوہ کی مجلس انتظامیہ کے رکن اور خانقاہ رحمانی کے سجادہ نشین مولانا سید منت اﷲ رحمانی کا انتقال ۳ رمضان المبارک ۱۹؍ مارچ کی شب میں ہوگیا، اناﷲ وانا الیہ راجعون۔ ان کا مرثیہ صرف ایک عالم کا نہیں بلکہ ایک عالم کا ماتم ہے، ہندوستانی مسلمانوں کے لیے ان جیسی ستودہ و صفات ہستیاں اس دور قحط الرجال میں نعمت سے کم نہیں اور اس نعمت کے چھن جانے سے حرمان و نقصان کی کیفیت اور شدید ہوجاتی ہے۔ انھوں نے ایسے ماحول میں آنکھیں کھولیں جو علم و معرفت اور شریعت و طریقت کی دولت سے مالا مال تھا ان کے والد ماجد مولانا سید محمد علی مونگیریؒ، شاہ فضل رحمن گنج مراد آبادیؒ سے تعلق، رد عیسائیت، تحریک ندوۃ العلماء اور ردقادیانیت میں اپنے کارناموں کے سبب نمونہ سلف اور طبقہ علماء و مشائخ میں ممتاز حیثیت رکھتے تھے، ان کی اقامت کانپور میں تھی لیکن ہدایت و ارشاد کے لیے وہ مونگیر اور اس کے اطراف میں برابر تشریف لے جایا کرتے تھے، جب وہاں قادیانیت کا فتنہ زیادہ سنگین ہوا تو اس کا مکمل قلع قمع کرنے کے لیے ۱۳۲۰ھ میں انھوں نے مستقل طور پر مونگیر میں اقامت اختیار کی، مولانا منت اﷲ رحمانی ۱۳۳۲ھ میں پیدا ہوئے، اپنے بھائیوں میں وہ سب سے چھوٹے تھے، مولانا مونگیریؒ کے انتقال کے وقت ان کی عمر تقریباً دس برس تھی، ان سے بیعت تو حاصل ہوئی لیکن استفادہ کا زیادہ موقع نہ ملا، انھوں نے بعد میں دیوبند اور ندوہ میں بھی تعلیم حاصل کی، ندوہ میں وہ...
Tujuan dari penelitian ini untuk mengimplementasikan implementasi model waterfall dalam perancangan sistem surat perintah perjalanan dinas berbasis website dengan metode SDLC. Perancangan sistem Surat Perintah Perjalanan Dinas pada Badan Pusat Statistik Kabupaten Pesisir Selatan bertujuan untuk mengkomputerisasikan pembuatan dan pengolahan data Surat Perintah Perjalanan Dinas secara efisien dan efektif. Pengelolaan SPPD ini di rancang menggunakan teknologi informasi berbasis website. Metode perancangan yang akan digunakan yaitu metode SDLC dengan model waterfall yang prosesnya secara sistematis atau berurutan. Sistem mempermudah efektivitas kinerja proses pembuatan SPPD di Badan Pusat Statistik Kabupaten Pesisir Selatan, serta penyimpanan data yang telah di buat tersimpan dengan baik, keamanan data lebih terjamin dan bisa di akses kapan pun dan di mana pun, serta menyelesaikan permasalahan yang ada pada Badan Pusat Statistik Kab. Pesisir Selatan dalam melakukan penginputan data dan pembuatan laporan yang akurat dan tepat waktu
Environments for algorithms can be categorized as static or dynamic. A static environment remains stationary throughout the execution of the algorithm, while in a dynamic environment the environment changes during the execution of the algorithm. The algorithms for planning in static and dynamic environments can be divided into offline and online algorithms. This research implements an online algorithm for an unknown environment and combined exploration and planning in a hybrid architecture. A simulated system of agents based on swarm intelligence is presented for route optimization and exploration. Two versions of the system are implemented and compared for performance- i.e., a simulated ant agent system and a simulated niche based particle swarm optimization. A simulated ant agent system is presented to address the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using a modified ant colony optimization algorithm for dealing with online route planning. The SAAS generates and optimizes routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints, and its efficiency has been tested in a mine field simulation with different environment configurations. It is capable of tracking a stationary as well as a non-stationary goal and performs equally well as compared to moving target search algorithm. Route planning for dynamic environment is further extended by using another optimization technique for generation of multiple routes. Simulated niche based particle swarm has been used for dynamic online route planning, optimization of the routes, and it has proved to be an effective technique. It efficiently deals with route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated niche based particle swarm optimization (SN-PSO) is proposed using a modified particle swarm optimization algorithm for dealing with online route planning. The SN-PSO generates and optimizes multiple routes in complex and large environments with constraints. The SN-PSO is shown to be an efficient technique for providing safe, short,and feasible routes under dynamic constraints. The efficiency of the SN-PSO is tested in a mine field simulation with different environment configuration, and it successfully generates multiple feasible routes. Finally, the swarm based techniques are further compared with an evolutionary algorithm (genetic algorithm) for performance and scalability. Statistical results showed that evolutionary techniques perform well in less cluttered environments and their performance degrades with the increase in environment complexity. For small size maps, the evolutionary technique performs well but its efficiency decreases with an increase in map size.